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WN1TaGjVC9U | A generative nonparametric Bayesian model for whole genomes | https://openreview.net/forum?id=WN1TaGjVC9U | [
"Alan Nawzad Amin",
"Eli N Weinstein",
"Debora Susan Marks"
] | Poster | null | Generative probabilistic modeling of biological sequences has widespread existing and potential use across biology and biomedicine, particularly given advances in high-throughput sequencing, synthesis and editing. However, we still lack methods with nucleotide resolution that are tractable at the scale of whole genomes... | [
"Bayesian nonparametrics",
"genomics",
"hypothesis testing"
] | We develop a scalable nonparametric Bayesian model of genomic sequences with theoretical guarantees. | 7,445 | null | null |
Tzkev89HeLZ | On Empirical Risk Minimization with Dependent and Heavy-Tailed Data | https://openreview.net/forum?id=Tzkev89HeLZ | [
"Abhishek Roy",
"Krishna Balasubramanian",
"Murat A Erdogdu"
] | Poster | null | In this work, we establish risk bounds for Empirical Risk Minimization (ERM) with both dependent and heavy-tailed data-generating processes. We do so by extending the seminal works~\cite{pmlr-v35-mendelson14, mendelson2018learning} on the analysis of ERM with heavy-tailed but independent and identically distributed obs... | [
"non-iid learning",
"risk bounds",
"empirical risk minimization",
"concentration inequalities",
"small-ball method"
] | This paper develops risk bounds for empirical risk minimization with (polynomially) heavy-tailed and strictly stationary exponentially $\beta$-mixing data generating model. | 7,443 | 2109.02224 | title_snapshot |
tqQ-8MuSqm | Scaling Up Exact Neural Network Compression by ReLU Stability | https://openreview.net/forum?id=tqQ-8MuSqm | [
"Thiago Serra",
"Xin Yu",
"Abhinav Kumar",
"Srikumar Ramalingam"
] | Poster | null | We can compress a rectifier network while exactly preserving its underlying functionality with respect to a given input domain if some of its neurons are stable. However, current approaches to determine the stability of neurons with Rectified Linear Unit (ReLU) activations require solving or finding a good approximatio... | [
"Scaling",
"Neural Networks",
"Lossless Compression",
"ReLU"
] | We scale up the lossless compression in ReLU neural network based on solving a single optimization problem to identify all stable neurons. | 7,441 | 2102.07804 | title_snapshot |
q4Dln9kWFA0 | Heterogeneous Multi-player Multi-armed Bandits: Closing the Gap and Generalization | https://openreview.net/forum?id=q4Dln9kWFA0 | [
"Chengshuai Shi",
"Wei Xiong",
"Cong Shen",
"Jing Yang"
] | Poster | null | Despite the significant interests and many progresses in decentralized multi-player multi-armed bandits (MP-MAB) problems in recent years, the regret gap to the natural centralized lower bound in the heterogeneous MP-MAB setting remains open. In this paper, we propose BEACON -- Batched Exploration with Adaptive COmmuni... | [
"Multi-agent System",
"Multi-armed Bandits",
"Decentralized Learning"
] | This work closed the regret gap from centralized performance in decentralized heterogeneous multi-player multi-armed bandits, and extended the study from the linear reward function to general reward functions. | 7,433 | 2110.14622 | title_snapshot |
b83ibRX55T | Towards Gradient-based Bilevel Optimization with Non-convex Followers and Beyond | https://openreview.net/forum?id=b83ibRX55T | [
"Risheng Liu",
"Yaohua Liu",
"Shangzhi Zeng",
"Jin Zhang"
] | Spotlight | null | In recent years, Bi-Level Optimization (BLO) techniques have received extensive attentions from both learning and vision communities. A variety of BLO models in complex and practical tasks are of non-convex follower structure in nature (a.k.a., without Lower-Level Convexity, LLC for short). However, this challenging cl... | [
"Bi-level programming",
"gradient-based method",
"asymptotic convergence",
"few-shot classification",
"data hyper-cleaning"
] | null | 7,432 | 2110.00455 | title_snapshot |
f9mSLa07Ncc | Learning latent causal graphs via mixture oracles | https://openreview.net/forum?id=f9mSLa07Ncc | [
"Bohdan Kivva",
"Goutham Rajendran",
"Pradeep Kumar Ravikumar",
"Bryon Aragam"
] | Poster | null | We study the problem of reconstructing a causal graphical model from data in the presence of latent variables. The main problem of interest is recovering the causal structure over the latent variables while allowing for general, potentially nonlinear dependencies. In many practical problems, the dependence between raw ... | [
"causal graphical models",
"latent variables",
"mixture model",
"algorithms"
] | Theoretical guarantees and efficient algorithms for learning causal graphical models with latent variables. | 7,425 | 2106.15563 | title_snapshot |
5af9FHClUZu | Fast Projection onto the Capped Simplex with Applications to Sparse Regression in Bioinformatics | https://openreview.net/forum?id=5af9FHClUZu | [
"Andersen Ang",
"Jianzhu Ma",
"Nianjun Liu",
"Kun Huang",
"Yijie Wang"
] | Poster | null | We consider the problem of projecting a vector onto the so-called k-capped simplex, which is a hyper-cube cut by a hyperplane.
For an n-dimensional input vector with bounded elements, we found that a simple algorithm based on Newton's method is able to solve the projection problem to high precision with a complexity ro... | [
"Capped Simplex",
"Projection",
"Newton's Method",
"Sparse Regression via Boolean Relaxation",
"Bioinformatics",
"GWAS"
] | We find that solving the projection onto the capped simplex by Newton's method is FAST. | 7,422 | 2110.08471 | title_snapshot |
YV3uoawS5KK | Averaging on the Bures-Wasserstein manifold: dimension-free convergence of gradient descent | https://openreview.net/forum?id=YV3uoawS5KK | [
"Jason Altschuler",
"Sinho Chewi",
"Patrik Robert Gerber",
"Austin J Stromme"
] | Spotlight | null | We study first-order optimization algorithms for computing the barycenter of Gaussian distributions with respect to the optimal transport metric. Although the objective is geodesically non-convex, Riemannian gradient descent empirically converges rapidly, in fact faster than off-the-shelf methods such as Euclidean grad... | [
"Bures-Wasserstein barycenter",
"dimension-free convergence",
"entropic regularization",
"first-order optimization",
"geometric median",
"non-convex optimization",
"Riemannian optimization"
] | We improve state-of-the-art convergence guarantees for Riemannian gradient descent for computing geometric averages of Gaussians. | 7,421 | 2106.08502 | title_snapshot |
Ke9lCi1vGF | Escaping Saddle Points with Compressed SGD | https://openreview.net/forum?id=Ke9lCi1vGF | [
"Dmitrii Avdiukhin",
"Grigory Yaroslavtsev"
] | Poster | null | Stochastic gradient descent (SGD) is a prevalent optimization technique for large-scale distributed machine learning. While SGD computation can be efficiently divided between multiple machines, communication typically becomes a bottleneck in the distributed setting. Gradient compression methods can be used to alleviate... | [
"optimization",
"distributed optimization",
"nonconvex optimization",
"machine learning",
"gradient descent",
"saddle points"
] | SGD with compressor converges to a second-order stationary point with improved total communication | 7,419 | 2105.10090 | title_snapshot |
YadmOcMC9aa | Reinforcement Learning with Latent Flow | https://openreview.net/forum?id=YadmOcMC9aa | [
"Wenling Shang",
"Xiaofei Wang",
"Aravind Srinivas",
"Aravind Rajeswaran",
"Yang Gao",
"Pieter Abbeel",
"Michael Laskin"
] | Poster | null | Temporal information is essential to learning effective policies with Reinforcement Learning (RL). However, current state-of-the-art RL algorithms either assume that such information is given as part of the state space or, when learning from pixels, use the simple heuristic of frame-stacking to implicitly capture temp... | [
"Machine Learning",
"Reinforcement Learning",
"Latent Flow"
] | We introduce FLARE, a pixel-based RL algorithm which utilizes late fusion with latent flow to improve performance on the DeepMind control and Atari pixel-based RL benchmarks. | 7,413 | 2101.01857 | title_snapshot |
bm1Mrc3WHSe | Rank Overspecified Robust Matrix Recovery: Subgradient Method and Exact Recovery | https://openreview.net/forum?id=bm1Mrc3WHSe | [
"Lijun Ding",
"Liwei Jiang",
"Yudong Chen",
"Qing Qu",
"Zhihui Zhu"
] | Poster | null | We study the robust recovery of a low-rank matrix from sparsely and grossly corrupted Gaussian measurements, with no prior knowledge on the intrinsic rank. We consider the robust matrix factorization approach. We employ a robust $\ell_1$ loss function and deal with the challenge of the unknown rank by using an overspec... | [
"Low rank",
"Robust recovery",
"Subgradient",
"Rank overspecification"
] | null | 7,412 | 2109.11154 | title_snapshot |
XnIYa2OG2sr | An Exact Characterization of the Generalization Error for the Gibbs Algorithm | https://openreview.net/forum?id=XnIYa2OG2sr | [
"Gholamali Aminian",
"Yuheng Bu",
"Laura Toni",
"Miguel R. D. Rodrigues",
"Gregory Wornell"
] | Poster | null | Various approaches have been developed to upper bound the generalization error of a supervised learning algorithm. However, existing bounds are often loose and lack of guarantees. As a result, they may fail to characterize the exact generalization ability of a learning algorithm.
Our main contribution is an exact chara... | [
"Gibbs algorithm",
"generalization error",
"information-theoretic bounds",
"PAC-Bayesian bounds"
] | Our main contribution is an exact characterization of the expected generalization error of the Gibbs algorithm using symmetrized KL information between the input training samples and the output hypothesis. | 7,405 | null | null |
mekyxmlLJNd | Effective Meta-Regularization by Kernelized Proximal Regularization | https://openreview.net/forum?id=mekyxmlLJNd | [
"Weisen Jiang",
"James Kwok",
"Yu Zhang"
] | Poster | null | We study the problem of meta-learning, which has proved to be advantageous to accelerate learning new tasks with a few samples. The recent approaches based on deep kernels achieve the state-of-the-art performance. However, the regularizers in their base learners are not learnable. In this paper, we propose an algorithm... | [
"meta-learning"
] | null | 7,404 | null | null |
48uzkHOKMfz | Accelerating Robotic Reinforcement Learning via Parameterized Action Primitives | https://openreview.net/forum?id=48uzkHOKMfz | [
"Murtaza Dalal",
"Deepak Pathak",
"Ruslan Salakhutdinov"
] | Poster | null | Despite the potential of reinforcement learning (RL) for building general-purpose robotic systems, training RL agents to solve robotics tasks still remains challenging due to the difficulty of exploration in purely continuous action spaces. Addressing this problem is an active area of research with the majority of foc... | [
"reinforcement learning",
"robotic manipulation",
"motion primitives",
"hierarchical RL"
] | We show that a simple redefinition of an RL agent's underlying action space can substantially improve performance on complex robotic control tasks. | 7,402 | 2110.15360 | title_snapshot |
WigDnV-_Gq | BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation | https://openreview.net/forum?id=WigDnV-_Gq | [
"Mingguo He",
"Zhewei Wei",
"Zengfeng Huang",
"Hongteng Xu"
] | Poster | null | Many representative graph neural networks, $e.g.$, GPR-GNN and ChebNet, approximate graph convolutions with graph spectral filters. However, existing work either applies predefined filter weights or learns them without necessary constraints, which may lead to oversimplified or ill-posed filters. To overcome these issue... | [
"Graph Neural Networks",
"Bernstein Polynomial Approximation",
"Arbitrary Spectral Filters"
] | We propose BernNet, a graph neural network that designs and learns an arbitrary spectral filter via Bernstein polynomial approximation. | 7,400 | 2106.10994 | title_snapshot |
jFMzBeLyTc0 | Class-Disentanglement and Applications in Adversarial Detection and Defense | https://openreview.net/forum?id=jFMzBeLyTc0 | [
"Kaiwen Yang",
"Tianyi Zhou",
"Yonggang Zhang",
"Xinmei Tian",
"Dacheng Tao"
] | Poster | null | What is the minimum necessary information required by a neural net $D(\cdot)$ from an image $x$ to accurately predict its class? Extracting such information in the input space from $x$ can allocate the areas $D(\cdot)$ mainly attending to and shed novel insights to the detection and defense of adversarial attacks. In t... | [
"class disentanglement",
"interpretable machine learning",
"adversarial detection"
] | We propose a simple VAE+classifier structure to separate the class information from an image by decomposing it into two part. | 7,396 | null | null |
kQDPhAZHYi | S$^3$: Sign-Sparse-Shift Reparametrization for Effective Training of Low-bit Shift Networks | https://openreview.net/forum?id=kQDPhAZHYi | [
"Xinlin Li",
"Bang Liu",
"Yaoliang Yu",
"Wulong Liu",
"Chunjing Xu",
"Vahid Partovi Nia"
] | Poster | null | Shift neural networks reduce computation complexity by removing expensive multiplication operations and quantizing continuous weights into low-bit discrete values, which are fast and energy-efficient compared to conventional neural networks. However, existing shift networks are sensitive to the weight initialization an... | [
"Convolution Neural Network",
"CNN",
"Edge Computing",
"Edge",
"Quantization",
"Low-bit",
"Multiplication-free",
"Bit-shift"
] | S$^3$ re-parameterization for efficient training of low-bit shift networks combating vanishing gradient problem and weight sign freezing problem, 3-bit multiplication-free network compete full-precision model on ImageNet. | 7,392 | 2107.03453 | title_snapshot |
aoXERVeC7cC | Selective Sampling for Online Best-arm Identification | https://openreview.net/forum?id=aoXERVeC7cC | [
"Romain Camilleri",
"Zhihan Xiong",
"Maryam Fazel",
"Lalit K Jain",
"Kevin Jamieson"
] | Poster | null | This work considers the problem of selective-sampling for best-arm identification. Given a set of potential options $\mathcal{Z}\subset\mathbb{R}^d$, a learner aims to compute with probability greater than $1-\delta$, $\arg\max_{z\in \mathcal{Z}} z^{\top}\theta_{\ast}$ where $\theta_{\ast}$ is unknown. At each time ste... | [
"active learning",
"bandits"
] | null | 7,388 | 2110.14864 | title_snapshot |
sRojdWhXJx | Revitalizing CNN Attention via Transformers in Self-Supervised Visual Representation Learning | https://openreview.net/forum?id=sRojdWhXJx | [
"Chongjian GE",
"Youwei Liang",
"Yibing Song",
"Jianbo Jiao",
"Jue Wang",
"Ping Luo"
] | Poster | null | Studies on self-supervised visual representation learning (SSL) improve encoder backbones to discriminate training samples without labels. While CNN encoders via SSL achieve comparable recognition performance to those via supervised learning, their network attention is under-explored for further improvement. Motivated ... | [
"Self-Supervised Visual Representation Learning",
"Vision Transformers"
] | We revitalize CNN encoder attentions via transformer in self-supervised visual representation learning | 7,387 | 2110.05340 | title_judge |
oAjn5-AgSd | Local Signal Adaptivity: Provable Feature Learning in Neural Networks Beyond Kernels | https://openreview.net/forum?id=oAjn5-AgSd | [
"Stefani Karp",
"Ezra Winston",
"Yuanzhi Li",
"Aarti Singh"
] | Poster | null | Neural networks have been shown to outperform kernel methods in practice (including neural tangent kernels). Most theoretical explanations of this performance gap focus on learning a complex hypothesis class; in some cases, it is unclear whether this hypothesis class captures realistic data. In this work, we propose a ... | [
"deep learning theory",
"neural networks",
"kernels"
] | We propose an explanation for the gap between neural networks and their corresponding neural tangent kernels based on the ability of neural networks to find a sparse, localized signal in the presence of noise. | 7,384 | null | null |
Z2ZWIvNeVUl | On the Stochastic Stability of Deep Markov Models | https://openreview.net/forum?id=Z2ZWIvNeVUl | [
"Jan Drgona",
"Sayak Mukherjee",
"Jiaxin Zhang",
"Frank Y Liu",
"Mahantesh Halappanavar"
] | Poster | null | Deep Markov models (DMM) are generative models which are scalable and expressive generalization of Markov models for representation, learning, and inference problems. However, the fundamental stochastic stability guarantees of such models have not been thoroughly investigated. In this paper, we present a novel stabilit... | [
"markov models",
"deep neural networks",
"stochastic stability",
"dynamical systems"
] | The paper presents a novel stability analysis method for deep Markov models and provide sufficient conditions of DMM's stochastic stability. | 7,382 | 2111.04601 | title_snapshot |
wRFj6EKvpl | How Data Augmentation affects Optimization for Linear Regression | https://openreview.net/forum?id=wRFj6EKvpl | [
"Boris Hanin",
"Yi Sun"
] | Poster | null | Though data augmentation has rapidly emerged as a key tool for optimization in modern machine learning, a clear picture of how augmentation schedules affect optimization and interact with optimization hyperparameters such as learning rate is nascent. In the spirit of classical convex optimization and recent work on imp... | [
"data augmentation",
"stochastic optimization",
"convex optimization"
] | We fully characterize the impact of data augmentation on optimization in the case of linear regression with MSE loss. | 7,379 | 2010.11171 | title_snapshot |
rA9HFxFT7th | Sageflow: Robust Federated Learning against Both Stragglers and Adversaries | https://openreview.net/forum?id=rA9HFxFT7th | [
"Jungwuk Park",
"Dong-Jun Han",
"Minseok Choi",
"Jaekyun Moon"
] | Poster | null | While federated learning (FL) allows efficient model training with local data at edge devices, among major issues still to be resolved are: slow devices known as stragglers and malicious attacks launched by adversaries. While the presence of both of these issues raises serious concerns in practical FL systems, no kno... | [
"Federated Learning",
"Stragglers",
"Adversaries"
] | We propose a new federated learning algorithm that handles both stragglers and adversaries simultaneously, via staleness-aware grouping with entropy-based filtering and loss-weighted averaging. | 7,375 | null | null |
wJXWzCsGlZw | Practical, Provably-Correct Interactive Learning in the Realizable Setting: The Power of True Believers | https://openreview.net/forum?id=wJXWzCsGlZw | [
"JULIAN KATZ-SAMUELS",
"Blake Mason",
"Kevin Jamieson",
"Rob Nowak"
] | Poster | null | We consider interactive learning in the realizable setting and develop a general framework to handle problems ranging from best arm identification to active classification. We begin our investigation with the observation that agnostic algorithms \emph{cannot} be minimax-optimal in the realizable setting. Hence, we desi... | [
"Active Learning",
"Active Classification",
"Multi-Armed Bandits"
] | We consider interactive learning in the realizable setting and design novel computationally efficient algorithms for general function classes that match the minimax lower bound up to logarithmic factors. | 7,368 | 2111.04915 | title_snapshot |
DvxH_RCnSj3 | Implicit Task-Driven Probability Discrepancy Measure for Unsupervised Domain Adaptation | https://openreview.net/forum?id=DvxH_RCnSj3 | [
"Mao Li",
"Kaiqi Jiang",
"Xinhua Zhang"
] | Poster | null | Probability discrepancy measure is a fundamental construct for numerous machine learning models such as weakly supervised learning and generative modeling. However, most measures overlook the fact that the distributions are not the end-product of learning, but are the basis of downstream predictor. Therefore it is im... | [
"probability discrepancy measure",
"unsupervised domain adaptation"
] | Warping the probability discrepancy measure towards the end tasks can significantly improve unsupervised domain adaptation. | 7,364 | null | null |
Xs-vglI4EBi | On the Convergence Theory of Debiased Model-Agnostic Meta-Reinforcement Learning | https://openreview.net/forum?id=Xs-vglI4EBi | [
"Alireza Fallah",
"Kristian Georgiev",
"Aryan Mokhtari",
"Asuman E. Ozdaglar"
] | Poster | null | We consider Model-Agnostic Meta-Learning (MAML) methods for Reinforcement Learning (RL) problems, where the goal is to find a policy using data from several tasks represented by Markov Decision Processes (MDPs) that can be updated by one step of \textit{stochastic} policy gradient for the realized MDP. In particular, u... | [
"meta-learning theory",
"reinforcement learning theory",
"optimization"
] | We resolve the bias issue in the update of original Model-Agnostic Meta-Learning (MAML) method for the reinforcement learning problem and provide convergence guarantees for our method. | 7,363 | 2002.05135 | title_snapshot |
xRLT28nnlFV | On Robust Optimal Transport: Computational Complexity and Barycenter Computation | https://openreview.net/forum?id=xRLT28nnlFV | [
"Khang Le",
"Huy Nguyen",
"Quang Minh Nguyen",
"Tung Pham",
"Hung Bui",
"Nhat Ho"
] | Poster | null | We consider robust variants of the standard optimal transport, named robust optimal transport, where marginal constraints are relaxed via Kullback-Leibler divergence. We show that Sinkhorn-based algorithms can approximate the optimal cost of robust optimal transport in $\widetilde{\mathcal{O}}(\frac{n^2}{\varepsilon})$... | [
"optimal transport",
"optimization",
"complexity",
"robustness"
] | We provide computational complexity for a robust variant of optimal transport and the corresponding barycenter problem. | 7,359 | 2102.06857 | title_snapshot |
ZgUZmeV1Mtu | Few-Round Learning for Federated Learning | https://openreview.net/forum?id=ZgUZmeV1Mtu | [
"Younghyun Park",
"Dong-Jun Han",
"Do-Yeon Kim",
"Jun Seo",
"Jaekyun Moon"
] | Poster | null | In federated learning (FL), a number of distributed clients targeting the same task collaborate to train a single global model without sharing their data. The learning process typically starts from a randomly initialized or some pretrained model. In this paper, we aim at designing an initial model based on which an arb... | [
"Federated Learning"
] | We propose a meta-training algorithm to design an initial model based on which an arbitrary group of clients can obtain a global model for its own purpose, within only a few rounds of FL | 7,357 | null | null |
NE0YlkgRo9x | A single gradient step finds adversarial examples on random two-layers neural networks | https://openreview.net/forum?id=NE0YlkgRo9x | [
"Sebastien Bubeck",
"Yeshwanth Cherapanamjeri",
"Gauthier Gidel",
"Remi Tachet des Combes"
] | Spotlight | null | Daniely and Schacham recently showed that gradient descent finds adversarial examples on random undercomplete two-layers ReLU neural networks. The term “undercomplete” refers to the fact that their proof only holds when the number of neurons is a vanishing fraction of the ambient dimension. We extend their result to th... | [
"adversarial examples",
"random neural networks",
"deep-learning theory"
] | We prove that a single gradient step finds adversarial examples on random two-layers neural networks. | 7,355 | 2104.03863 | title_snapshot |
qb0qTdxPWzY | List-Decodable Mean Estimation in Nearly-PCA Time | https://openreview.net/forum?id=qb0qTdxPWzY | [
"Ilias Diakonikolas",
"Daniel Kane",
"Daniel Kongsgaard",
"Jerry Li",
"Kevin Tian"
] | Spotlight | null | Robust statistics has traditionally focused on designing estimators tolerant to a minority of contaminated data. {\em List-decodable learning}~\cite{CharikarSV17} studies the more challenging regime where only a minority $\tfrac 1 k$ fraction of the dataset, $k \geq 2$, is drawn from the distribution of interest, and n... | [
"robust statistics",
"learning theory",
"mixture models",
"semidefinite programming",
"list-decodable learning"
] | We give a state-of-the-art algorithm for list-decodable mean estimation, the robust generalization of learning mixture models, attaining optimal error in polylogarithmic calls to approximate PCA. | 7,354 | 2011.09973 | title_snapshot |
YxxzNLfXBz | Conservative Data Sharing for Multi-Task Offline Reinforcement Learning | https://openreview.net/forum?id=YxxzNLfXBz | [
"Tianhe Yu",
"Aviral Kumar",
"Yevgen Chebotar",
"Karol Hausman",
"Sergey Levine",
"Chelsea Finn"
] | Poster | null | Offline reinforcement learning (RL) algorithms have shown promising results in domains where abundant pre-collected data is available. However, prior methods focus on solving individual problems from scratch with an offline dataset without considering how an offline RL agent can acquire multiple skills. We argue that a... | [
"offline reinforcement learning",
"multi-task reinforcement learning",
"deep reinforcement learning"
] | null | 7,353 | 2109.08128 | title_snapshot |
Tv0O_cAdKtW | On sensitivity of meta-learning to support data | https://openreview.net/forum?id=Tv0O_cAdKtW | [
"Mayank Agarwal",
"Mikhail Yurochkin",
"Yuekai Sun"
] | Poster | null | Meta-learning algorithms are widely used for few-shot learning. For example, image recognition systems that readily adapt to unseen classes after seeing only a few labeled examples. Despite their success, we show that modern meta-learning algorithms are extremely sensitive to the data used for adaptation, i.e. support ... | [
"meta-learning",
"sensitivity",
"robustness"
] | We demonstrate that meta-learning algorithms applied to few-shot learning are extremely sensitive to the support data. | 7,352 | 2110.13953 | title_snapshot |
09-zkOYoVof | Generalization of Model-Agnostic Meta-Learning Algorithms: Recurring and Unseen Tasks | https://openreview.net/forum?id=09-zkOYoVof | [
"Alireza Fallah",
"Aryan Mokhtari",
"Asuman E. Ozdaglar"
] | Poster | null | In this paper, we study the generalization properties of Model-Agnostic Meta-Learning (MAML) algorithms for supervised learning problems. We focus on the setting in which we train the MAML model over $m$ tasks, each with $n$ data points, and characterize its generalization error from two points of view: First, we assum... | [
"meta-learning theory",
"generalization bounds",
"algorithmic stability",
"convex optimization"
] | null | 7,347 | 2102.03832 | title_snapshot |
YlM3tey8Z5I | Self-Interpretable Model with Transformation Equivariant Interpretation | https://openreview.net/forum?id=YlM3tey8Z5I | [
"Yipei Wang",
"Xiaoqian Wang"
] | Poster | null | With the proliferation of machine learning applications in the real world, the demand for explaining machine learning predictions continues to grow especially in high-stakes fields. Recent studies have found that interpretation methods can be sensitive and unreliable, where the interpretations can be disturbed by pertu... | [
"interpretable machine learning",
"transformation equivariance",
"computer vision"
] | We propose a self-interpretable model that has transformation-equivariant interpretations, and comparable expressive power as benchmark black-box model. | 7,346 | null | null |
ChWy1anEuow | Risk Bounds for Over-parameterized Maximum Margin Classification on Sub-Gaussian Mixtures | https://openreview.net/forum?id=ChWy1anEuow | [
"Yuan Cao",
"Quanquan Gu",
"Misha Belkin"
] | Poster | null | Modern machine learning systems such as deep neural networks are often highly over-parameterized so that they can fit the noisy training data exactly, yet they can still achieve small test errors in practice. In this paper, we study this "benign overfitting" phenomenon of the maximum margin classifier for linear classi... | [
"maximum margin classification",
"over-parameterization",
"benign overfitting"
] | null | 7,340 | 2104.13628 | title_snapshot |
KCd-3Pz8VjM | Automatic Unsupervised Outlier Model Selection | https://openreview.net/forum?id=KCd-3Pz8VjM | [
"Yue Zhao",
"Ryan Rossi",
"Leman Akoglu"
] | Poster | null | Given an unsupervised outlier detection task on a new dataset, how can we automatically select a good outlier detection algorithm and its hyperparameter(s) (collectively called a model)? In this work, we tackle the unsupervised outlier model selection (UOMS) problem, and propose MetaOD, a principled, data-driven approa... | [
"outlier detection",
"anomaly detection",
"unsupervised learning",
"model selection",
"automated machine learning"
] | Automatic Unsupervised Outlier Model Selection | 7,338 | null | null |
pu6loAVvBZb | Robust Regression Revisited: Acceleration and Improved Estimation Rates | https://openreview.net/forum?id=pu6loAVvBZb | [
"Arun Jambulapati",
"Jerry Li",
"Tselil Schramm",
"Kevin Tian"
] | Poster | null | We study fast algorithms for statistical regression problems under the strong contamination model, where the goal is to approximately optimize a generalized linear model (GLM) given adversarially corrupted samples. Prior works in this line of research were based on the \emph{robust gradient descent} framework of \cite{... | [
"robust statistics",
"stochastic optimization",
"linear regression",
"acceleration"
] | We present nearly-linear time algorithms for statistical regression problems with improved runtime or estimation guarantees over the prior state-of-the-art. | 7,337 | 2106.11938 | title_snapshot |
2ybxtABV2Og | BNS: Building Network Structures Dynamically for Continual Learning | https://openreview.net/forum?id=2ybxtABV2Og | [
"Qi Qin",
"Wenpeng Hu",
"Han Peng",
"Dongyan Zhao",
"Bing Liu"
] | Poster | null | Continual learning (CL) of a sequence of tasks is often accompanied with the catastrophic forgetting(CF) problem. Existing research has achieved remarkable results in overcoming CF, especially for task continual learning. However, limited work has been done to achieve another important goal of CL,knowledge transfer.In ... | [
"continual learning"
] | A reinforcement learning based continual learning method is proposed. | 7,331 | null | null |
ZDMqRGSksHs | Adaptive Sampling for Minimax Fair Classification | https://openreview.net/forum?id=ZDMqRGSksHs | [
"Shubhanshu Shekhar",
"Greg Fields",
"Mohammad Ghavamzadeh",
"Tara Javidi"
] | Poster | null | Machine learning models trained on uncurated datasets can often end up adversely affecting inputs belonging to underrepresented groups. To address this issue, we consider the problem of adaptively constructing training sets which allow us to learn classifiers that are fair in a {\em minimax} sense. We first propose an ... | [
"Fairness",
"deep learning",
"active sampling"
] | We propose and analyze an algorithm to adaptively build datasets to ensure minimax fairness among specified subpopulations. | 7,327 | 2103.00755 | title_snapshot |
MlFcgL2AP4d | Near-Optimal Offline Reinforcement Learning via Double Variance Reduction | https://openreview.net/forum?id=MlFcgL2AP4d | [
"Ming Yin",
"Yu Bai",
"Yu-Xiang Wang"
] | Poster | null | We consider the problem of offline reinforcement learning (RL) --- a well-motivated setting of RL that aims at policy optimization using only historical data. Despite its wide applicability, theoretical understandings of offline RL, such as its optimal sample complexity, remain largely open even in basic settings such... | [
"Theory",
"Reinforcement Learning Theory",
"Markov Decision Process Theory"
] | null | 7,323 | 2102.01748 | title_snapshot |
6dUJPrPPUau | Few-Shot Data-Driven Algorithms for Low Rank Approximation | https://openreview.net/forum?id=6dUJPrPPUau | [
"Piotr Indyk",
"Tal Wagner",
"David Woodruff"
] | Poster | null | Recently, data-driven and learning-based algorithms for low rank matrix approximation were shown to outperform classical data-oblivious algorithms by wide margins in terms of accuracy. Those algorithms are based on the optimization of sparse sketching matrices, which lead to large savings in time and memory during tes... | [
"low rank approximation",
"numerical linear algebra",
"learning-based algorithms",
"svd",
"matrix sketching"
] | We provide new, time and sample efficient, and interpretable algorithms for data-driven low rank matrix approximation. We provide both theoretical results and empirical evaluation for our algorithms. | 7,305 | null | null |
mjyMGFL8N2 | Provable Guarantees for Self-Supervised Deep Learning with Spectral Contrastive Loss | https://openreview.net/forum?id=mjyMGFL8N2 | [
"Jeff Z. HaoChen",
"Colin Wei",
"Adrien Gaidon",
"Tengyu Ma"
] | Oral | null | Recent works in self-supervised learning have advanced the state-of-the-art by relying on the contrastive learning paradigm, which learns representations by pushing positive pairs, or similar examples from the same class, closer together while keeping negative pairs far apart. Despite the empirical successes, theoretic... | [
"theory",
"deep learning theory",
"unsupervised learning theory",
"representation learning theory"
] | We propose a novel theoretical framework for studying self-supervised learning algorithms. | 7,303 | 2106.04156 | title_snapshot |
x8k1nAoGu1U | Fast Doubly-Adaptive MCMC to Estimate the Gibbs Partition Function with Weak Mixing Time Bounds | https://openreview.net/forum?id=x8k1nAoGu1U | [
"Shahrzad Haddadan",
"Yue Zhuang",
"Cyrus Cousins",
"Eli Upfal"
] | Poster | null | We present a novel method for reducing the computational complexity of rigorously estimating the partition functions of Gibbs (or Boltzmann) distributions, which arise ubiquitously in probabilistic graphical models. A major obstacle to applying the Gibbs distribution in practice is the need to estimate their partition ... | [
"MCMC",
"Gibbs distribution",
"partition functions"
] | we propose a doubly adaptive algorithms for estimating partition function of Gibbs directions and show theoretically and through experiments that it beats the state of the art | 7,296 | 2111.07372 | title_snapshot |
FMPuzXV1fR | Breaking the centralized barrier for cross-device federated learning | https://openreview.net/forum?id=FMPuzXV1fR | [
"Sai Praneeth Karimireddy",
"Martin Jaggi",
"Satyen Kale",
"Mehryar Mohri",
"Sashank J. Reddi",
"Sebastian U Stich",
"Ananda Theertha Suresh"
] | Poster | null | Federated learning (FL) is a challenging setting for optimization due to the heterogeneity of the data across different clients which gives rise to the client drift phenomenon. In fact, obtaining an algorithm for FL which is uniformly better than simple centralized training has been a major open problem thus far. In th... | [
"Federated Learning",
"Non-convex Optimization",
"Distributed Optimization",
"Communication Complexity"
] | New framework which i) adapts arbitary centralized algorithms to the federated setting, and ii) obtains the first rates which are uniformly better than centralized training. | 7,292 | null | null |
_pmQOVi3gHx | Linear and Kernel Classification in the Streaming Model: Improved Bounds for Heavy Hitters | https://openreview.net/forum?id=_pmQOVi3gHx | [
"Arvind V. Mahankali",
"David Woodruff"
] | Poster | null | We study linear and kernel classification in the streaming model. For linear classification, we improve upon the algorithm of (Tai, et al. 2018), which solves the $\ell_1$ point query problem on the optimal weight vector $w_* \in \mathbb{R}^d$ in sublinear space. We first give an algorithm solving the more difficult $\... | [
"Linear classification",
"heavy hitters",
"kernel classification",
"streaming algorithms",
"sketching",
"tensors"
] | null | 7,285 | null | null |
kcI3T5qe1jr | Controllable and Compositional Generation with Latent-Space Energy-Based Models | https://openreview.net/forum?id=kcI3T5qe1jr | [
"Weili Nie",
"Arash Vahdat",
"Anima Anandkumar"
] | Poster | null | Controllable generation is one of the key requirements for successful adoption of deep generative models in real-world applications, but it still remains as a great challenge. In particular, the compositional ability to generate novel concept combinations is out of reach for most current models. In this work, we use en... | [
"Controllable generation",
"compositional generation",
"image synthesis",
"energy-based models",
"deep generative models",
"neural ODEs"
] | We introduce an EBM in the latent space of existing generative models and a new ODE sampling method for controllable and compositional generation. | 7,281 | 2110.10873 | title_snapshot |
817F5yuNAf1 | Automatic and Harmless Regularization with Constrained and Lexicographic Optimization: A Dynamic Barrier Approach | https://openreview.net/forum?id=817F5yuNAf1 | [
"Chengyue Gong",
"Xingchao Liu",
"qiang liu"
] | Poster | null | Many machine learning tasks have to make a trade-off between two loss functions, typically the main data-fitness loss and an auxiliary loss.
The most widely used approach is to optimize the linear combination of the objectives, which, however, requires manual tuning of the combination coefficient and is theoretically ... | [
"constrained optimization",
"lexicographic optimization",
"multi-objective optimization",
"pareto set",
"multi-task learning"
] | null | 7,276 | null | null |
8bbevt2MKPX | Continuous-time edge modelling using non-parametric point processes | https://openreview.net/forum?id=8bbevt2MKPX | [
"Xuhui Fan",
"Bin Li",
"Feng Zhou",
"Scott A Sisson"
] | Poster | null | The mutually-exciting Hawkes process (ME-HP) is a natural choice to model reciprocity, which is an important attribute of continuous-time edge (dyadic) data. However, existing ways of implementing the ME-HP for such data are either inflexible, as the exogenous (background) rate functions are typically constant and the ... | [
"Continuous-time Edges",
"Sigmoidal Gaussian Process",
"Variational Inference"
] | We use Sigmoidal Gaussian Process modulated point processes to model continuous-time edges. | 7,274 | null | null |
a7APmM4B9d | Decision Transformer: Reinforcement Learning via Sequence Modeling | https://openreview.net/forum?id=a7APmM4B9d | [
"Lili Chen",
"Kevin Lu",
"Aravind Rajeswaran",
"Kimin Lee",
"Aditya Grover",
"Michael Laskin",
"Pieter Abbeel",
"Aravind Srinivas",
"Igor Mordatch"
] | Poster | null | We introduce a framework that abstracts Reinforcement Learning (RL) as a sequence modeling problem. This allows us to draw upon the simplicity and scalability of the Transformer architecture, and associated advances in language modeling such as GPT-x and BERT. In particular, we present Decision Transformer, an architec... | [
"transformers",
"reinforcement learning",
"deep learning",
"generative modeling"
] | Transformers can do offline RL successfully. | 7,270 | 2106.01345 | title_snapshot |
7PkfLkyLMRM | Mitigating Covariate Shift in Imitation Learning via Offline Data With Partial Coverage | https://openreview.net/forum?id=7PkfLkyLMRM | [
"Jonathan Daniel Chang",
"Masatoshi Uehara",
"Dhruv Sreenivas",
"Rahul Kidambi",
"Wen Sun"
] | Poster | null | This paper studies offline Imitation Learning (IL) where an agent learns to imitate an expert demonstrator without additional online environment interactions. Instead, the learner is presented with a static offline dataset of state-action-next state triples from a potentially less proficient behavior policy. We introdu... | [
"Offline Imitation Learning",
"Imitation Learning"
] | We present an algorithmic framework to mitigate the covariate shift issue in imitation learning using offline data with partial coverage and the principle of pessimism in the face of uncertainty. | 7,265 | 2106.03207 | title_judge |
-zgb2v8vV_w | Adaptive Risk Minimization: Learning to Adapt to Domain Shift | https://openreview.net/forum?id=-zgb2v8vV_w | [
"Marvin Mengxin Zhang",
"Henrik Marklund",
"Nikita Dhawan",
"Abhishek Gupta",
"Sergey Levine",
"Chelsea Finn"
] | Poster | null | A fundamental assumption of most machine learning algorithms is that the training and test data are drawn from the same underlying distribution. However, this assumption is violated in almost all practical applications: machine learning systems are regularly tested under distribution shift, due to changing temporal cor... | [
"distribution shift",
"domain generalization",
"test time adaptation"
] | null | 7,260 | 2007.02931 | title_snapshot |
ebIORrYImx | Practical Near Neighbor Search via Group Testing | https://openreview.net/forum?id=ebIORrYImx | [
"Joshua Engels",
"Benjamin Coleman",
"Anshumali Shrivastava"
] | Spotlight | null | We present a new algorithm for the approximate near neighbor problem that combines classical ideas from group testing with locality-sensitive hashing (LSH). We reduce the near neighbor search problem to a group testing problem by designating neighbors as "positives," non-neighbors as "negatives," and approximate member... | [
"group testing",
"locality sensitive hashing",
"near neighbor search",
"index"
] | We combine group testing with locality sensitive hashing to develop a near neighbor search algorithm with 10x faster query time on high-dimensional datasets. | 7,254 | 2106.11565 | title_snapshot |
twz1QqzU0Hp | A No-go Theorem for Robust Acceleration in the Hyperbolic Plane | https://openreview.net/forum?id=twz1QqzU0Hp | [
"Linus Hamilton",
"Ankur Moitra"
] | Poster | null | In recent years there has been significant effort to adapt the key tools and ideas in convex optimization to the Riemannian setting. One key challenge has remained: Is there a Nesterov-like accelerated gradient method for geodesically convex functions on a Riemannian manifold? Recent work has given partial answers and ... | [
"geodesic convexity",
"acceleration",
"lower bounds"
] | We prove that in a noisy setting, there is no analogue of accelerated gradient descent for geodesically convex functions on the hyperbolic plane. | 7,252 | null | null |
UYI6Sk_3Nox | Low-dimensional Structure in the Space of Language Representations is Reflected in Brain Responses | https://openreview.net/forum?id=UYI6Sk_3Nox | [
"Richard Antonello",
"Javier S. Turek",
"Vy A. Vo",
"Alexander Huth"
] | Poster | null | How related are the representations learned by neural language models, translation models, and language tagging tasks?
We answer this question by adapting an encoder-decoder transfer learning method from computer vision to investigate the structure among 100 different feature spaces extracted from hidden representatio... | [
"fMRI Encoding Models",
"Language Representations",
"Natural Language Processing"
] | We show that language representations from NLP models have low-dimensional structure and that this structure is reflected in brain responses to those representations. | 7,244 | 2106.05426 | title_snapshot |
ii5mGEbRo93 | Logarithmic Regret in Feature-based Dynamic Pricing | https://openreview.net/forum?id=ii5mGEbRo93 | [
"Jianyu Xu",
"Yu-Xiang Wang"
] | Spotlight | null | Feature-based dynamic pricing is an increasingly popular model of setting prices for highly differentiated products with applications in digital marketing, online sales, real estate and so on. The problem was formally studied as an online learning problem [Javanmard & Nazerzadeh, 2019] where a seller needs to propose p... | [
"dynamic pricing",
"online learning",
"adversarial features",
"optimal regret",
"affine invariant",
"distribution-free."
] | We present algorithms that guarantees logarithmic (minimax) regrets in both stochastic and adversarial feature-based dynamic pricing problems with market noises. | 7,237 | 2102.10221 | title_snapshot |
XGSQfOVxVp4 | On the Variance of the Fisher Information for Deep Learning | https://openreview.net/forum?id=XGSQfOVxVp4 | [
"Alexander Soen",
"Ke Sun"
] | Poster | null | In the realm of deep learning, the Fisher information matrix (FIM) gives novel insights and useful tools to characterize the loss landscape, perform second-order optimization, and build geometric learning theories. The exact FIM is either unavailable in closed form or too expensive to compute. In practice, it is almost... | [
"Fisher information",
"natural gradient",
"Cramer-Rao Lower Bound",
"deep learning"
] | We explore the variance of the Fisher information matrix in the context of deep learning. | 7,229 | 2107.04205 | title_snapshot |
NCDMYD2y5kK | Deep Extrapolation for Attribute-Enhanced Generation | https://openreview.net/forum?id=NCDMYD2y5kK | [
"Alvin Chan",
"Ali Madani",
"Ben Krause",
"Nikhil Naik"
] | Poster | null | Attribute extrapolation in sample generation is challenging for deep neural networks operating beyond the training distribution. We formulate a new task for extrapolation in sequence generation, focusing on natural language and proteins, and propose GENhance, a generative framework that enhances attributes through a le... | [
"extrapolation",
"generative modeling",
"controllable generation",
"protein design"
] | How do we generate sequences that extrapolate beyond the training distribution? | 7,205 | 2107.02968 | title_snapshot |
fqfHJqNy_uY | Rates of Estimation of Optimal Transport Maps using Plug-in Estimators via Barycentric Projections | https://openreview.net/forum?id=fqfHJqNy_uY | [
"NABARUN DEB",
"Promit Ghosal",
"Bodhisattva Sen"
] | Poster | null | Optimal transport maps between two probability distributions $\mu$ and $\nu$ on $\R^d$ have found extensive applications in both machine learning and statistics. In practice, these maps need to be estimated from data sampled according to $\mu$ and $\nu$. Plug-in estimators are perhaps most popular in estimating transpo... | [
"Discrete-discrete optimal transport",
"Kantorovich relaxation",
"Legendre-Fenchel dual",
"Semi-discrete Optimal Transport",
"Wasserstein barycenter",
"Wasserstein Distance."
] | We provide a new stability estimate for barycentric projections under minimal smoothness assumptions which we use to prove rates of convergence for general plug-in estimators of optimal transport maps. | 7,202 | 2107.01718 | title_snapshot |
LJjC6DmSkgT | Continual Learning via Local Module Composition | https://openreview.net/forum?id=LJjC6DmSkgT | [
"Oleksiy Ostapenko",
"Pau Rodriguez",
"Massimo Caccia",
"Laurent Charlin"
] | Poster | null | Modularity is a compelling solution to continual learning (CL), the problem of modeling sequences of related tasks. Learning and then composing modules to solve different tasks provides an abstraction to address the principal challenges of CL including catastrophic forgetting, backward and forward transfer across tasks... | [
"continual learning",
"modularity",
"compositionality",
"neverending learning",
"lifelong learning",
"plasticity",
"plasticity-stability dilemma",
"task incremental learning",
"multitask learning",
"routing",
"dynamic architectures",
"model growing",
"out-of-distribution generalization",
"... | We introduce an approach to modular Continual Learning where each module can “decide” about its relevancy given an input. | 7,196 | 2111.07736 | title_snapshot |
JpDlWGTBHB | Probabilistic Attention for Interactive Segmentation | https://openreview.net/forum?id=JpDlWGTBHB | [
"Prasad Gabbur",
"Manjot Bilkhu",
"Javier Movellan"
] | Spotlight | null | We provide a probabilistic interpretation of attention and show that the standard dot-product attention in transformers is a special case of Maximum A Posteriori (MAP) inference. The proposed approach suggests the use of Expectation Maximization algorithms for on-line adaptation of key and value model parameters. This ... | [
"Attention",
"Transformers",
"Probabilistic model",
"Gaussian mixture model",
"Interactive segmentation",
"Semantic segmentation"
] | A new perspective of attention as a probabilistic generative model with applications to interactive image segmentation. | 7,195 | 2106.15338 | title_snapshot |
70kOIgjKhbA | When does Contrastive Learning Preserve Adversarial Robustness from Pretraining to Finetuning? | https://openreview.net/forum?id=70kOIgjKhbA | [
"Lijie Fan",
"Sijia Liu",
"Pin-Yu Chen",
"Gaoyuan Zhang",
"Chuang Gan"
] | Poster | null | Contrastive learning (CL) can learn generalizable feature representations and achieve state-of-the-art performance of downstream tasks by finetuning a linear classifier on top of it. However, as adversarial robustness becomes vital in image classification, it remains unclear whether or not CL is able to preserve robu... | [
"Adversarial robustness",
"self-supervised learning",
"pretraining and finetuning"
] | This work revisits and advances contrastive learning principles through the lens of adversarial robustness, aiming to improve robustness transferability in the self-supervised pretraining + supervised finetuning paradigm | 7,194 | 2111.01124 | title_snapshot |
_Rtm4rYnIIL | MobILE: Model-Based Imitation Learning From Observation Alone | https://openreview.net/forum?id=_Rtm4rYnIIL | [
"Rahul Kidambi",
"Jonathan Daniel Chang",
"Wen Sun"
] | Poster | null | This paper studies Imitation Learning from Observations alone (ILFO) where the learner is presented with expert demonstrations that consist only of states visited by an expert (without access to actions taken by the expert). We present a provably efficient model-based framework MobILE to solve the ILFO problem. MobILE ... | [
"Imitation Learning from Observation Alone",
"Imitation Learning",
"Imitation Learning Theory"
] | We present an efficient model-based algorithm with strategic exploration for solving the imitation learning from observations alone problem. | 7,191 | 2102.10769 | title_snapshot |
tjwQaOI9tdy | Symbolic Regression via Deep Reinforcement Learning Enhanced Genetic Programming Seeding | https://openreview.net/forum?id=tjwQaOI9tdy | [
"Terrell N. Mundhenk",
"Mikel Landajuela",
"Ruben Glatt",
"Claudio P. Santiago",
"Daniel faissol",
"Brenden K. Petersen"
] | Poster | null | Symbolic regression is the process of identifying mathematical expressions that fit observed output from a black-box process. It is a discrete optimization problem generally believed to be NP-hard. Prior approaches to solving the problem include neural-guided search (e.g. using reinforcement learning) and genetic progr... | [
"symbolic regression",
"genetic programming",
"neural-guided search",
"reinforcement learning"
] | We use a hybrid genetic programming and neural-guided search approach to solve symbolic regression. | 7,188 | 2111.00053 | title_judge |
GEm4o9A6Jfb | PLUR: A Unifying, Graph-Based View of Program Learning, Understanding, and Repair | https://openreview.net/forum?id=GEm4o9A6Jfb | [
"Zimin Chen",
"Vincent Josua Hellendoorn",
"Pascal Lamblin",
"Petros Maniatis",
"Pierre-Antoine Manzagol",
"Daniel Tarlow",
"Subhodeep Moitra"
] | Spotlight | null | Machine learning for understanding and editing source code has recently attracted significant interest, with many developments in new models, new code representations, and new tasks.
This proliferation can appear disparate and disconnected, making each approach seemingly unique and incompatible, thus obscuring the core... | [
"learning for code",
"program understanding",
"program repair",
"relation-aware transformers",
"graph-based deep learning"
] | A single graph-based architecture can be applied to 16 seemingly different ML4Code tasks and achieves great results. | 7,185 | null | null |
gbcsmD3Iznu | End-to-End Weak Supervision | https://openreview.net/forum?id=gbcsmD3Iznu | [
"Salva Rühling Cachay",
"Benedikt Boecking",
"Artur Dubrawski"
] | Poster | null | Aggregating multiple sources of weak supervision (WS) can ease the data-labeling bottleneck prevalent in many machine learning applications, by replacing the tedious manual collection of ground truth labels.
Current state of the art approaches that do not use any labeled training data, however, require two separate mo... | [
"weak supervision",
"deep learning",
"data programming"
] | A neural end-to-end system that learns exclusively from multiple sources of weak supervision | 7,184 | 2107.02233 | title_snapshot |
ELU8Bu1Z9w1 | Autonomous Reinforcement Learning via Subgoal Curricula | https://openreview.net/forum?id=ELU8Bu1Z9w1 | [
"Archit Sharma",
"Abhishek Gupta",
"Sergey Levine",
"Karol Hausman",
"Chelsea Finn"
] | Poster | null | Reinforcement learning (RL) promises to enable autonomous acquisition of complex behaviors for diverse agents. However, the success of current reinforcement learning algorithms is predicated on an often under-emphasised requirement -- each trial needs to start from a fixed initial state distribution. Unfortunately, res... | [
"reinforcement learning",
"curriculum",
"autonomous learning",
"reset-free reinforcement learning"
] | null | 7,181 | 2107.12931 | title_snapshot |
vRwnHlAgK5x | Coupled Segmentation and Edge Learning via Dynamic Graph Propagation | https://openreview.net/forum?id=vRwnHlAgK5x | [
"Zhiding Yu",
"Rui Huang",
"Wonmin Byeon",
"Sifei Liu",
"Guilin Liu",
"Thomas Breuel",
"Anima Anandkumar",
"Jan Kautz"
] | Poster | null | Image segmentation and edge detection are both central problems in perceptual grouping. It is therefore interesting to study how these two tasks can be coupled to benefit each other. Indeed, segmentation can be easily transformed into contour edges to guide edge learning. However, the converse is nontrivial since gener... | [
"Semantic Segmentation",
"Semantic Edge Detection",
"Structured Prediction",
"Propagation Network"
] | A coupled learning framework for joint semantic segmentation and semantic edge detection | 7,178 | null | null |
zO6Q8q2AmbV | On Component Interactions in Two-Stage Recommender Systems | https://openreview.net/forum?id=zO6Q8q2AmbV | [
"Jiri Hron",
"Karl Krauth",
"Michael Jordan",
"Niki Kilbertus"
] | Poster | null | Thanks to their scalability, two-stage recommenders are used by many of today's largest online platforms, including YouTube, LinkedIn, and Pinterest. These systems produce recommendations in two steps: (i) multiple nominators—tuned for low prediction latency—preselect a small subset of candidates from the whole item po... | [
"recommender systems",
"mixture of experts",
"bandits",
"scalability"
] | Candidate generators in two-stage systems can be viewed as experts for their item subset, and thus trained jointly using Mixture-of-Experts algorithms. | 7,173 | 2106.14979 | title_snapshot |
W9oywyjO8VN | Tractable Regularization of Probabilistic Circuits | https://openreview.net/forum?id=W9oywyjO8VN | [
"Anji Liu",
"Guy Van den Broeck"
] | Spotlight | null | Probabilistic Circuits (PCs) are a promising avenue for probabilistic modeling. They combine advantages of probabilistic graphical models (PGMs) with those of neural networks (NNs). Crucially, however, they are tractable probabilistic models, supporting efficient and exact computation of many probabilistic inference qu... | [
"Probabilistic Circuits",
"Tractable Probabilistic Models",
"Parameter Regularization",
"Overfitting"
] | We proposed tractable regularization techniques for Probabilistic Circuits. | 7,172 | 2106.02264 | title_snapshot |
BbikqBWZTGB | NeRV: Neural Representations for Videos | https://openreview.net/forum?id=BbikqBWZTGB | [
"Hao Chen",
"Bo He",
"Hanyu Wang",
"Yixuan Ren",
"Ser-Nam Lim",
"Abhinav Shrivastava"
] | Poster | null | We propose a novel neural representation for videos (NeRV) which encodes videos in neural networks. Unlike conventional representations that treat videos as frame sequences, we represent videos as neural networks taking frame index as input. Given a frame index, NeRV outputs the corresponding RGB image. Video encoding... | [
"neural representation",
"implicit representation",
"video compression",
"video denoising"
] | null | 7,169 | 2110.13903 | title_snapshot |
apK65PUH0l9 | Detecting Errors and Estimating Accuracy on Unlabeled Data with Self-training Ensembles | https://openreview.net/forum?id=apK65PUH0l9 | [
"Jiefeng Chen",
"Frederick Liu",
"Besim Avci",
"Xi Wu",
"Yingyu Liang",
"Somesh Jha"
] | Poster | null | When a deep learning model is deployed in the wild, it can encounter test data drawn from distributions different from the training data distribution and suffer drop in performance. For safe deployment, it is essential to estimate the accuracy of the pre-trained model on the test data. However, the labels for the test ... | [
"unsupervised accuracy estimation",
"error detection",
"self-training ensembles"
] | Propose a principled and practically effective framework for unsupervised accuracy estimation and error detection tasks with theoretical analysis and state-of-the-art performance | 7,162 | 2106.15728 | title_snapshot |
TFEFvU0ZV6Q | Baby Intuitions Benchmark (BIB): Discerning the goals, preferences, and actions of others | https://openreview.net/forum?id=TFEFvU0ZV6Q | [
"Kanishk Gandhi",
"Gala Stojnic",
"Brenden M. Lake",
"Moira Rose Dillon"
] | Poster | null | To achieve human-like common sense about everyday life, machine learning systems must understand and reason about the goals, preferences, and actions of other agents in the environment. By the end of their first year of life, human infants intuitively achieve such common sense, and these cognitive achievements lay the ... | [
"Cognitive Psychology",
"Common Sense",
"Reasoning",
"Cognitive Development",
"Intuitive Psychology"
] | We present the Baby Intuitions Benchmark (BIB) that challenges machines to understand and reason about the goals, preferences, and actions of other agents. | 7,160 | 2102.11938 | title_snapshot |
9SD2Rb3NiWu | A Compositional Atlas of Tractable Circuit Operations for Probabilistic Inference | https://openreview.net/forum?id=9SD2Rb3NiWu | [
"antonio vergari",
"YooJung Choi",
"Anji Liu",
"Stefano Teso",
"Guy Van den Broeck"
] | Oral | null | Circuit representations are becoming the lingua franca to express and reason about tractable generative and discriminative models. In this paper, we show how complex inference scenarios for these models that commonly arise in machine learning---from computing the expectations of decision tree ensembles to information-... | [
"probabilistic reasoning",
"tractable inference",
"probabilistic circuits",
"sum-product networks"
] | We systematically characterize a tractable model class for an inference scenario by building a modular pipeline of atomic operations and thus distilling an efficient algorithm for it | 7,157 | 2102.06137 | title_judge |
vAMh-dcNMcR | Consistent Non-Parametric Methods for Maximizing Robustness | https://openreview.net/forum?id=vAMh-dcNMcR | [
"Robi Bhattacharjee",
"Kamalika Chaudhuri"
] | Poster | null | Learning classifiers that are robust to adversarial examples has received a great deal of recent attention. A major drawback of the standard robust learning framework is the imposition of an artificial robustness radius $r$ that applies to all inputs, and ignores the fact that data may be highly heterogeneous. In parti... | [
"non-parametric classifiers",
"adversarial examples",
"robustness",
"large sample limit"
] | We propose a new notion of adaptive robustness and examine conditions for non-parametric methods to converge in this setting. | 7,156 | 2102.09086 | title_snapshot |
NEQYGJr1qL3 | Scalable Neural Data Server: A Data Recommender for Transfer Learning | https://openreview.net/forum?id=NEQYGJr1qL3 | [
"Tianshi Cao",
"Sasha Doubov",
"David Acuna",
"Sanja Fidler"
] | Poster | null | Absence of large-scale labeled data in the practitioner's target domain can be a bottleneck to applying machine learning algorithms in practice. Transfer learning is a popular strategy for leveraging additional data to improve the downstream performance, but finding the most relevant data to transfer from can be challe... | [
"Transfer Learning",
"Computer Vision",
"Data Recommendation"
] | We present a data recommendation system for transfer learning that scales to arbitrary number of data sources. | 7,153 | 2206.09386 | title_snapshot |
k505ekjMzww | Residual Pathway Priors for Soft Equivariance Constraints | https://openreview.net/forum?id=k505ekjMzww | [
"Marc Anton Finzi",
"Gregory Benton",
"Andrew Gordon Wilson"
] | Poster | null | Models such as convolutional neural networks restrict the hypothesis space to a set of functions satisfying equivariance constraints, and improve generalization in problems by capturing relevant symmetries. However, symmetries are often only partially respected, preventing models with restriction biases from fitting th... | [
"symmetry",
"equivariance",
"group equivariance",
"priors"
] | We introduce Residual Pathway Priors for converting hard architectural constraints to soft priors, enabling exploitation of approximate symmetries. | 7,151 | 2112.01388 | title_snapshot |
Jhp38rtUTV | UCB-based Algorithms for Multinomial Logistic Regression Bandits | https://openreview.net/forum?id=Jhp38rtUTV | [
"Sanae Amani",
"Christos Thrampoulidis"
] | Poster | null | Out of the rich family of generalized linear bandits, perhaps the most well studied ones are logistic bandits that are used in problems with binary rewards: for instance, when the learner aims to maximize the profit over a user that can select one of two possible outcomes (e.g., `click' vs `no-click'). Despite remarkab... | [
"Generalized Linear Bandits",
"Logistic Bandits",
"Multinomial Logit (MNL)",
"Upper Confidence Bound"
] | We study problems with more than possible outcomes selected by the user and use multinomial logit to model the probability of each possible outcome. We propose an algorithm with sublinear regret with small dependency on problem-dependent constants. | 7,149 | 2103.11489 | title_snapshot |
LAwuz_L9U9j | Representation Learning for Event-based Visuomotor Policies | https://openreview.net/forum?id=LAwuz_L9U9j | [
"Sai Vemprala",
"Sami Mian",
"Ashish Kapoor"
] | Spotlight | null | Event-based cameras are dynamic vision sensors that provide asynchronous measurements of changes in per-pixel brightness at a microsecond level. This makes them significantly faster than conventional frame-based cameras, and an appealing choice for high-speed robot navigation. While an interesting sensor modality, this... | [
"Event cameras",
"Representation Learning",
"Reinforcement Learning",
"Variational Autoencoder"
] | We present methods for representation learning and reinforcement learning directly from asynchronous event camera, and demonstrate advantages over frame-based techniques using an obstacle avoidance task. | 7,148 | 2103.00806 | title_snapshot |
4S4nbt-rD6 | Bridging the Gap Between Practice and PAC-Bayes Theory in Few-Shot Meta-Learning | https://openreview.net/forum?id=4S4nbt-rD6 | [
"Nan Ding",
"Xi Chen",
"Tomer Levinboim",
"Sebastian Goodman",
"Radu Soricut"
] | Poster | null | Despite recent advances in its theoretical understanding, there still remains a significant gap in the ability of existing PAC-Bayesian theories on meta-learning to explain performance improvements in the few-shot learning setting, where the number of training examples in the target tasks is severely limited. This gap ... | [
"meta-learning",
"few-shot learning",
"PAC-Bayesian theory"
] | Improve PAC-Bayesian bounds on few-shot meta-learning. | 7,145 | 2105.14099 | title_snapshot |
hJOLFJIJ_zy | Dense Keypoints via Multiview Supervision | https://openreview.net/forum?id=hJOLFJIJ_zy | [
"Zhixuan Yu",
"Haozheng Yu",
"Long Sha",
"Sujoy Ganguly",
"Hyun Soo Park"
] | Spotlight | null | This paper presents a new end-to-end semi-supervised framework to learn a dense keypoint detector using unlabeled multiview images. A key challenge lies in finding the exact correspondences between the dense keypoints in multiple views since the inverse of the keypoint mapping can be neither analytically derived nor dif... | [
"Dense keypoint estimation",
"Multiview supervision",
"Dense epipolar geometry",
"Semi-supervised learning"
] | null | 7,142 | null | null |
lzZX7E713nJ | Equivariant Manifold Flows | https://openreview.net/forum?id=lzZX7E713nJ | [
"Isay Katsman",
"Aaron Lou",
"Derek Lim",
"Qingxuan Jiang",
"Ser-Nam Lim",
"Christopher De Sa"
] | Poster | null | Tractably modelling distributions over manifolds has long been an important goal in the natural sciences. Recent work has focused on developing general machine learning models to learn such distributions. However, for many applications these distributions must respect manifold symmetries—a trait which most previous mod... | [
"manifold",
"normalizing flow",
"equivariant",
"invariant"
] | We construct manifold normalizing flows which are equivariant to isometric actions. | 7,139 | 2107.08596 | title_snapshot |
t-7Jx48oaG | Analyzing the Generalization Capability of SGLD Using Properties of Gaussian Channels | https://openreview.net/forum?id=t-7Jx48oaG | [
"Hao Wang",
"Yizhe Huang",
"Rui Gao",
"Flavio Calmon"
] | Poster | null | Optimization is a key component for training machine learning models and has a strong impact on their generalization. In this paper, we consider a particular optimization method---the stochastic gradient Langevin dynamics (SGLD) algorithm---and investigate the generalization of models trained by SGLD. We derive a new g... | [
"Information theory",
"statistical learning theory"
] | null | 7,137 | null | null |
DWvcqoRAQP8 | Safe Policy Optimization with Local Generalized Linear Function Approximations | https://openreview.net/forum?id=DWvcqoRAQP8 | [
"Akifumi Wachi",
"Yunyue Wei",
"Yanan Sui"
] | Poster | null | Safe exploration is a key to applying reinforcement learning (RL) in safety-critical systems. Existing safe exploration methods guaranteed safety under the assumption of regularity, and it has been difficult to apply them to large-scale real problems. We propose a novel algorithm, SPO-LF, that optimizes an agent's poli... | [
"Safe Reinforcement Learning",
"Constrained Markov Decision Process"
] | Formulate a safe reinforcement learning problem where features are locally available upon observation, and propose an algorithm with theoretical guarantee on optimality and safety, which can be applied to large-scale problems. | 7,133 | 2111.04894 | title_snapshot |
VD3TMzyxKK | Probabilistic Forecasting: A Level-Set Approach | https://openreview.net/forum?id=VD3TMzyxKK | [
"Hilaf Hasson",
"Bernie Wang",
"Tim Januschowski",
"Jan Gasthaus"
] | Poster | null | Large-scale time series panels have become ubiquitous over the last years in areas such as retail, operational metrics, IoT, and medical domain (to name only a few). This has resulted in a need for forecasting techniques that effectively leverage all available data by learning across all time series in each panel. Amon... | [
"probabilistic forecasting",
"quantile regression trees",
"consistency"
] | null | 7,132 | null | null |
SBNs7EULzqq | Non-Asymptotic Analysis for Two Time-scale TDC with General Smooth Function Approximation | https://openreview.net/forum?id=SBNs7EULzqq | [
"Yue Wang",
"Shaofeng Zou",
"Yi Zhou"
] | Poster | null | Temporal-difference learning with gradient correction (TDC) is a two time-scale algorithm for policy evaluation in reinforcement learning. This algorithm was initially proposed with linear function approximation, and was later extended to the one with general smooth function approximation. The asymptotic convergence fo... | [
"TDC",
"finite-sample analysis",
"tracking error bound",
"Markovian noise",
"non-convex"
] | This paper provides a non-asymptotic analysis for the TDC algorithm with general smooth function approximation. | 7,131 | 2104.02836 | title_snapshot |
luWTh5Q63e | Predicting Event Memorability from Contextual Visual Semantics | https://openreview.net/forum?id=luWTh5Q63e | [
"Qianli Xu",
"Fen Fang",
"Ana Garcia del Molino",
"Vigneshwaran Subbaraju",
"Joo Hwee Lim"
] | Poster | null | Episodic event memory is a key component of human cognition. Predicting event memorability,i.e., to what extent an event is recalled, is a tough challenge in memory research and has profound implications for artificial intelligence. In this study, we investigate factors that affect event memorability according to a cue... | [
"Event memory",
"image memorability",
"visual semantics",
"episodic memory",
"lifelog"
] | A new dataset and baseline model for predicting event memorability from visual information and its context | 7,129 | null | null |
MtvKv_BDVV | ATISS: Autoregressive Transformers for Indoor Scene Synthesis | https://openreview.net/forum?id=MtvKv_BDVV | [
"Despoina Paschalidou",
"Amlan Kar",
"Maria Shugrina",
"Karsten Kreis",
"Andreas Geiger",
"Sanja Fidler"
] | Poster | null | The ability to synthesize realistic and diverse indoor furniture layouts automatically or based on partial input, unlocks many applications, from better interactive 3D tools to data synthesis for training and simulation. In this paper, we present ATISS, a novel autoregressive transformer architecture for creating diver... | [
"Indoor Scene Synthesis",
"Layout Generation",
"Autoregressive Set Generation",
"Generative Models"
] | We propose an autoregressive transformer architecture for indoor scene synthesis that generates room layouts as unordered sets of objects and allows for a variety of interactive applications with versatile user input. | 7,124 | 2110.03675 | title_snapshot |
RYcgfqmAOHh | Deep Learning with Label Differential Privacy | https://openreview.net/forum?id=RYcgfqmAOHh | [
"Badih Ghazi",
"Noah Golowich",
"Ravi Kumar",
"Pasin Manurangsi",
"Chiyuan Zhang"
] | Poster | null | The Randomized Response (RR) algorithm is a classical technique to improve robustness in survey aggregation, and has been widely adopted in applications with differential privacy guarantees. We propose a novel algorithm, Randomized Response with Prior (RRWithPrior), which can provide more accurate results while maintai... | [
"differential privacy",
"label differential privacy",
"randomized response",
"deep learning",
"self-supervised learning"
] | null | 7,123 | 2102.06062 | title_snapshot |
Tsp2PL7-GQ | Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networks | https://openreview.net/forum?id=Tsp2PL7-GQ | [
"Avi Schwarzschild",
"Eitan Borgnia",
"Arjun Gupta",
"Furong Huang",
"Uzi Vishkin",
"Micah Goldblum",
"Tom Goldstein"
] | Poster | null | Deep neural networks are powerful machines for visual pattern recognition, but reasoning tasks that are easy for humans may still be difficult for neural models. Humans possess the ability to extrapolate reasoning strategies learned on simple problems to solve harder examples, often by thinking for longer. For example,... | [
"Deep learning",
"algorithms",
"generalization",
"recurrent networks",
"prefix sums",
"mazes",
"chess"
] | Recurrent netowrks can learn processes that can generalize from easy training data to harder examples at test time by iterating more times. | 7,113 | 2106.04537 | title_snapshot |
bDHBNVtB9XA | Learning Optimal Predictive Checklists | https://openreview.net/forum?id=bDHBNVtB9XA | [
"Haoran Zhang",
"Quaid Morris",
"Berk Ustun",
"Marzyeh Ghassemi"
] | Poster | null | Checklists are simple decision aids that are often used to promote safety and reliability in clinical applications. In this paper, we present a method to learn checklists for clinical decision support. We represent predictive checklists as discrete linear classifiers with binary features and unit weights. We then learn... | [
"healthcare",
"interpretability",
"fairness",
"integer programming",
"discrete optimization",
"classification"
] | We present an integer programming method to learn optimal checklists for classification tasks. | 7,107 | 2112.01020 | title_snapshot |
KBnXrODoBW | Pay Attention to MLPs | https://openreview.net/forum?id=KBnXrODoBW | [
"Hanxiao Liu",
"Zihang Dai",
"David So",
"Quoc V Le"
] | Poster | null | Transformers have become one of the most important architectural innovations in deep learning and have enabled many breakthroughs over the past few years. Here we propose a simple network architecture, gMLP, based solely on MLPs with gating, and show that it can perform as well as Transformers in key language and visio... | [
"Transformer",
"Attention",
"MLP"
] | A simple variant of MLP that works well for key applications that Transformers are good at: BERT for NLP and ViT for vision | 7,105 | 2105.08050 | title_snapshot |
sxjpM-kvVv_ | Center Smoothing: Certified Robustness for Networks with Structured Outputs | https://openreview.net/forum?id=sxjpM-kvVv_ | [
"Aounon Kumar",
"Tom Goldstein"
] | Poster | null | The study of provable adversarial robustness has mostly been limited to classification tasks and models with one-dimensional real-valued outputs. We extend the scope of certifiable robustness to problems with more general and structured outputs like sets, images, language, etc. We model the output space as a metric spa... | [
"Adversarial Robustness",
"Certified Robustness",
"Randomized Smoothing",
"Structured Outputs"
] | null | 7,104 | 2102.09701 | title_snapshot |
tJ_CO8orSI | Adjusting for Autocorrelated Errors in Neural Networks for Time Series | https://openreview.net/forum?id=tJ_CO8orSI | [
"Fan-Keng Sun",
"Chris Lang",
"Duane S Boning"
] | Poster | null | An increasing body of research focuses on using neural networks to model time series. A common assumption in training neural networks via maximum likelihood estimation on time series is that the errors across time steps are uncorrelated. However, errors are actually autocorrelated in many cases due to the temporality o... | [
"time series forecasting",
"autocorrelated errors"
] | Learning autocorrelation coefficient jointly with model parameters to adjust the autocorrelated errors in neural networks for time series. | 7,086 | 2101.12578 | title_snapshot |
a2Gr9gNFD-J | Characterizing possible failure modes in physics-informed neural networks | https://openreview.net/forum?id=a2Gr9gNFD-J | [
"Aditi Krishnapriyan",
"Amir Gholami",
"Shandian Zhe",
"Robert Kirby",
"Michael W. Mahoney"
] | Poster | null | Recent work in scientific machine learning has developed so-called physics-informed neural network (PINN) models. The typical approach is to incorporate physical domain knowledge as soft constraints on an empirical loss function and use existing machine learning methodologies to train the model. We demonstrate that, wh... | [
"scientific machine learning",
"physics-informed neural networks",
"regularization",
"constrained optimization",
"unconstrained optimization"
] | We characterize the challenges associated with incorporating fundamental physical laws into the machine learning process ("physics-informed neural networks"), and devise strategies to overcome their failure modes by changing the learning paradigm. | 7,081 | 2109.01050 | title_snapshot |
w5fW0TNWPyc | Machine Learning for Variance Reduction in Online Experiments | https://openreview.net/forum?id=w5fW0TNWPyc | [
"Yongyi Guo",
"Dominic Coey",
"Mikael Konutgan",
"Wenting Li",
"Chris Schoener",
"Matt Goldman"
] | Poster | null | We consider the problem of variance reduction in randomized controlled trials, through the use of covariates correlated with the outcome but independent of the treatment. We propose a machine learning regression-adjusted treatment effect estimator, which we call MLRATE. MLRATE uses machine learning predictors of the ou... | [
"experimentation",
"variance reduction",
"agnostic statistics",
"debiased machine learning",
"semiparametrics",
"experiment splitting"
] | We show how to use supervised ML methods to substantially increase precision in experimental causal inference. | 7,079 | 2106.07263 | title_snapshot |
DZKsFQyDB9 | PatchGame: Learning to Signal Mid-level Patches in Referential Games | https://openreview.net/forum?id=DZKsFQyDB9 | [
"Kamal Gupta",
"Gowthami Somepalli",
"Anubhav Gupta",
"Vinoj Jayasundara",
"Matthias Zwicker",
"Abhinav Shrivastava"
] | Poster | null | We study a referential game (a type of signaling game) where two agents communicate with each other via a discrete bottleneck to achieve a common goal. In our referential game, the goal of the speaker is to compose a message or a symbolic representation of "important" image patches, while the task for the listener is t... | [
"emergent language",
"referential games",
"self-supervised learning",
"mid-level patches"
] | Emergent communication via mid-level patches in a referential game played on a large-scale image dataset | 7,078 | 2111.01785 | title_snapshot |
x2rdRAx3QF | Self-Consistent Models and Values | https://openreview.net/forum?id=x2rdRAx3QF | [
"Gregory Farquhar",
"Kate Baumli",
"Zita Marinho",
"Angelos Filos",
"Matteo Hessel",
"Hado van Hasselt",
"David Silver"
] | Poster | null | Learned models of the environment provide reinforcement learning (RL) agents with flexible ways of making predictions about the environment.
Models enable planning, i.e. using more computation to improve value functions or policies, without requiring additional environment interactions.
In this work, we investigate a w... | [
"reinforcement learning",
"model-based reinforcement learning",
"planning",
"value equivalence"
] | Maybe we should train models and value functions to be jointly self-consistent. | 7,070 | 2110.12840 | title_snapshot |
503UwCYEe5 | Understanding How Encoder-Decoder Architectures Attend | https://openreview.net/forum?id=503UwCYEe5 | [
"Kyle Aitken",
"Vinay Venkatesh Ramasesh",
"Yuan Cao",
"Niru Maheswaranathan"
] | Poster | null | Encoder-decoder networks with attention have proven to be a powerful way to solve many sequence-to-sequence tasks. In these networks, attention aligns encoder and decoder states and is often used for visualizing network behavior. However, the mechanisms used by networks to generate appropriate attention matrices are st... | [
"Attention",
"NLP"
] | We investigate the dynamics behind networks trained on sequence to sequence tasks with and without attention. | 7,051 | 2110.15253 | title_snapshot |
gRlsFQMo_ze | Reverse engineering learned optimizers reveals known and novel mechanisms | https://openreview.net/forum?id=gRlsFQMo_ze | [
"Niru Maheswaranathan",
"David Sussillo",
"Luke Metz",
"Ruoxi Sun",
"Jascha Sohl-Dickstein"
] | Poster | null | Learned optimizers are parametric algorithms that can themselves be trained to solve optimization problems. In contrast to baseline optimizers (such as momentum or Adam) that use simple update rules derived from theoretical principles, learned optimizers use flexible, high-dimensional, nonlinear parameterizations. Alth... | [
"optimization",
"learned optimizers",
"reverse engineering",
"RNNs"
] | We reverse engineer learned optimizers trained on a simple tasks and show that they learn interpretable and intuitive mechanisms. | 7,045 | 2011.02159 | title_snapshot |
SPrVNsXnGd | Renyi Differential Privacy of The Subsampled Shuffle Model In Distributed Learning | https://openreview.net/forum?id=SPrVNsXnGd | [
"Antonious M. Girgis",
"Deepesh Data",
"Suhas Diggavi"
] | Poster | null | We study privacy in a distributed learning framework, where clients collaboratively build a learning model iteratively through
interactions with a server from whom we need privacy. Motivated by stochastic optimization and the federated learning (FL) paradigm, we focus on the case where a small fraction of data samples ... | [
"Differential privacy",
"Renyi divergence",
"distributed learning",
"privacy amplification via shuffling",
"privacy composition."
] | We characterize the renyi differential privacy of the sampled shuffle model to provide tighter privacy composition for DP-SGD algorithm. | 7,043 | 2107.08763 | title_snapshot |
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