paper_id stringlengths 9 24 | title stringlengths 10 192 | paper_url stringlengths 44 59 | authors listlengths 1 23 | type stringclasses 0
values | primary_area stringclasses 0
values | abstract large_stringlengths 82 4.49k | keywords listlengths 0 0 | TL;DR large_stringclasses 0
values | submission_number int64 1 1.18k | arxiv_id stringlengths 10 10 ⌀ | arxiv_id_source stringclasses 2
values |
|---|---|---|---|---|---|---|---|---|---|---|---|
v139/kim21a | Improving Predictors via Combination Across Diverse Task Categories | https://proceedings.mlr.press/v139/kim21a.html | [
"Kwang In Kim"
] | null | null | Predictor combination is the problem of improving a task predictor using predictors of other tasks when the forms of individual predictors are unknown. Previous work approached this problem by nonparametrically assessing predictor relationships based on their joint evaluations on a shared sample. This limits their appl... | [] | null | 501 | null | null |
v139/kim21b | Self-Improved Retrosynthetic Planning | https://proceedings.mlr.press/v139/kim21b.html | [
"Junsu Kim",
"Sungsoo Ahn",
"Hankook Lee",
"Jinwoo Shin"
] | null | null | Retrosynthetic planning is a fundamental problem in chemistry for finding a pathway of reactions to synthesize a target molecule. Recently, search algorithms have shown promising results for solving this problem by using deep neural networks (DNNs) to expand their candidate solutions, i.e., adding new reactions to reac... | [] | null | 502 | 2106.04880 | title_snapshot |
v139/kim21c | Reward Identification in Inverse Reinforcement Learning | https://proceedings.mlr.press/v139/kim21c.html | [
"Kuno Kim",
"Shivam Garg",
"Kirankumar Shiragur",
"Stefano Ermon"
] | null | null | We study the problem of reward identifiability in the context of Inverse Reinforcement Learning (IRL). The reward identifiability question is critical to answer when reasoning about the effectiveness of using Markov Decision Processes (MDPs) as computational models of real world decision makers in order to understand c... | [] | null | 503 | null | null |
v139/kim21d | I-BERT: Integer-only BERT Quantization | https://proceedings.mlr.press/v139/kim21d.html | [
"Sehoon Kim",
"Amir Gholami",
"Zhewei Yao",
"Michael W. Mahoney",
"Kurt Keutzer"
] | null | null | Transformer based models, like BERT and RoBERTa, have achieved state-of-the-art results in many Natural Language Processing tasks. However, their memory footprint, inference latency, and power consumption are prohibitive efficient inference at the edge, and even at the data center. While quantization can be a viable so... | [] | null | 504 | 2101.01321 | title_snapshot |
v139/kim21e | Message Passing Adaptive Resonance Theory for Online Active Semi-supervised Learning | https://proceedings.mlr.press/v139/kim21e.html | [
"Taehyeong Kim",
"Injune Hwang",
"Hyundo Lee",
"Hyunseo Kim",
"Won-Seok Choi",
"Joseph J Lim",
"Byoung-Tak Zhang"
] | null | null | Active learning is widely used to reduce labeling effort and training time by repeatedly querying only the most beneficial samples from unlabeled data. In real-world problems where data cannot be stored indefinitely due to limited storage or privacy issues, the query selection and the model update should be performed a... | [] | null | 505 | 2012.01227 | title_snapshot |
v139/kim21f | Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech | https://proceedings.mlr.press/v139/kim21f.html | [
"Jaehyeon Kim",
"Jungil Kong",
"Juhee Son"
] | null | null | Several recent end-to-end text-to-speech (TTS) models enabling single-stage training and parallel sampling have been proposed, but their sample quality does not match that of two-stage TTS systems. In this work, we present a parallel end-to-end TTS method that generates more natural sounding audio than current two-stag... | [] | null | 506 | 2106.06103 | title_snapshot |
v139/kim21g | A Policy Gradient Algorithm for Learning to Learn in Multiagent Reinforcement Learning | https://proceedings.mlr.press/v139/kim21g.html | [
"Dong Ki Kim",
"Miao Liu",
"Matthew D Riemer",
"Chuangchuang Sun",
"Marwa Abdulhai",
"Golnaz Habibi",
"Sebastian Lopez-Cot",
"Gerald Tesauro",
"Jonathan How"
] | null | null | A fundamental challenge in multiagent reinforcement learning is to learn beneficial behaviors in a shared environment with other simultaneously learning agents. In particular, each agent perceives the environment as effectively non-stationary due to the changing policies of other agents. Moreover, each agent is itself ... | [] | null | 507 | 2011.00382 | title_snapshot |
v139/kim21h | Inferring Latent Dynamics Underlying Neural Population Activity via Neural Differential Equations | https://proceedings.mlr.press/v139/kim21h.html | [
"Timothy D. Kim",
"Thomas Z. Luo",
"Jonathan W. Pillow",
"Carlos D. Brody"
] | null | null | An important problem in systems neuroscience is to identify the latent dynamics underlying neural population activity. Here we address this problem by introducing a low-dimensional nonlinear model for latent neural population dynamics using neural ordinary differential equations (neural ODEs), with noisy sensory inputs... | [] | null | 508 | null | null |
v139/kim21i | The Lipschitz Constant of Self-Attention | https://proceedings.mlr.press/v139/kim21i.html | [
"Hyunjik Kim",
"George Papamakarios",
"Andriy Mnih"
] | null | null | Lipschitz constants of neural networks have been explored in various contexts in deep learning, such as provable adversarial robustness, estimating Wasserstein distance, stabilising training of GANs, and formulating invertible neural networks. Such works have focused on bounding the Lipschitz constant of fully connecte... | [] | null | 509 | 2006.04710 | title_snapshot |
v139/kim21j | Unsupervised Skill Discovery with Bottleneck Option Learning | https://proceedings.mlr.press/v139/kim21j.html | [
"Jaekyeom Kim",
"Seohong Park",
"Gunhee Kim"
] | null | null | Having the ability to acquire inherent skills from environments without any external rewards or supervision like humans is an important problem. We propose a novel unsupervised skill discovery method named Information Bottleneck Option Learning (IBOL). On top of the linearization of environments that promotes more vari... | [] | null | 510 | 2106.14305 | title_snapshot |
v139/kim21k | ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision | https://proceedings.mlr.press/v139/kim21k.html | [
"Wonjae Kim",
"Bokyung Son",
"Ildoo Kim"
] | null | null | Vision-and-Language Pre-training (VLP) has improved performance on various joint vision-and-language downstream tasks. Current approaches to VLP heavily rely on image feature extraction processes, most of which involve region supervision (e.g., object detection) and the convolutional architecture (e.g., ResNet). Althou... | [] | null | 511 | 2102.03334 | title_snapshot |
v139/kirschner21a | Bias-Robust Bayesian Optimization via Dueling Bandits | https://proceedings.mlr.press/v139/kirschner21a.html | [
"Johannes Kirschner",
"Andreas Krause"
] | null | null | We consider Bayesian optimization in settings where observations can be adversarially biased, for example by an uncontrolled hidden confounder. Our first contribution is a reduction of the confounded setting to the dueling bandit model. Then we propose a novel approach for dueling bandits based on information-directed ... | [] | null | 512 | 2105.11802 | title_snapshot |
v139/kiyasseh21a | CLOCS: Contrastive Learning of Cardiac Signals Across Space, Time, and Patients | https://proceedings.mlr.press/v139/kiyasseh21a.html | [
"Dani Kiyasseh",
"Tingting Zhu",
"David A Clifton"
] | null | null | The healthcare industry generates troves of unlabelled physiological data. This data can be exploited via contrastive learning, a self-supervised pre-training method that encourages representations of instances to be similar to one another. We propose a family of contrastive learning methods, CLOCS, that encourages rep... | [] | null | 513 | 2005.13249 | title_snapshot |
v139/gasteiger21a | Scalable Optimal Transport in High Dimensions for Graph Distances, Embedding Alignment, and More | https://proceedings.mlr.press/v139/gasteiger21a.html | [
"Johannes Gasteiger",
"Marten Lienen",
"Stephan Günnemann"
] | null | null | The current best practice for computing optimal transport (OT) is via entropy regularization and Sinkhorn iterations. This algorithm runs in quadratic time as it requires the full pairwise cost matrix, which is prohibitively expensive for large sets of objects. In this work we propose two effective log-linear time appr... | [] | null | 514 | 2107.06876 | title_snapshot |
v139/koehler21a | Representational aspects of depth and conditioning in normalizing flows | https://proceedings.mlr.press/v139/koehler21a.html | [
"Frederic Koehler",
"Viraj Mehta",
"Andrej Risteski"
] | null | null | Normalizing flows are among the most popular paradigms in generative modeling, especially for images, primarily because we can efficiently evaluate the likelihood of a data point. This is desirable both for evaluating the fit of a model, and for ease of training, as maximizing the likelihood can be done by gradient des... | [] | null | 515 | 2010.01155 | title_snapshot |
v139/koh21a | WILDS: A Benchmark of in-the-Wild Distribution Shifts | https://proceedings.mlr.press/v139/koh21a.html | [
"Pang Wei Koh",
"Shiori Sagawa",
"Henrik Marklund",
"Sang Michael Xie",
"Marvin Zhang",
"Akshay Balsubramani",
"Weihua Hu",
"Michihiro Yasunaga",
"Richard Lanas Phillips",
"Irena Gao",
"Tony Lee",
"Etienne David",
"Ian Stavness",
"Wei Guo",
"Berton Earnshaw",
"Imran Haque",
"Sara M B... | null | null | Distribution shifts—where the training distribution differs from the test distribution—can substantially degrade the accuracy of machine learning (ML) systems deployed in the wild. Despite their ubiquity in the real-world deployments, these distribution shifts are under-represented in the datasets widely used in the ML... | [] | null | 516 | 2012.07421 | title_snapshot |
v139/kolmogorov21a | One-sided Frank-Wolfe algorithms for saddle problems | https://proceedings.mlr.press/v139/kolmogorov21a.html | [
"Vladimir Kolmogorov",
"Thomas Pock"
] | null | null | We study a class of convex-concave saddle-point problems of the form $\min_x\max_y ⟨Kx,y⟩+f_{\cal P}(x)-h^*(y)$ where $K$ is a linear operator, $f_{\cal P}$ is the sum of a convex function $f$ with a Lipschitz-continuous gradient and the indicator function of a bounded convex polytope ${\cal P}$, and $h^\ast$ is a conv... | [] | null | 517 | 2101.12617 | title_snapshot |
v139/komanduru21a | A Lower Bound for the Sample Complexity of Inverse Reinforcement Learning | https://proceedings.mlr.press/v139/komanduru21a.html | [
"Abi Komanduru",
"Jean Honorio"
] | null | null | Inverse reinforcement learning (IRL) is the task of finding a reward function that generates a desired optimal policy for a given Markov Decision Process (MDP). This paper develops an information-theoretic lower bound for the sample complexity of the finite state, finite action IRL problem. A geometric construction of ... | [] | null | 518 | 2103.04446 | title_snapshot |
v139/kong21a | Consensus Control for Decentralized Deep Learning | https://proceedings.mlr.press/v139/kong21a.html | [
"Lingjing Kong",
"Tao Lin",
"Anastasia Koloskova",
"Martin Jaggi",
"Sebastian Stich"
] | null | null | Decentralized training of deep learning models enables on-device learning over networks, as well as efficient scaling to large compute clusters. Experiments in earlier works reveal that, even in a data-center setup, decentralized training often suffers from the degradation in the quality of the model: the training and ... | [] | null | 519 | 2102.04828 | title_snapshot |
v139/konobeev21a | A Distribution-dependent Analysis of Meta Learning | https://proceedings.mlr.press/v139/konobeev21a.html | [
"Mikhail Konobeev",
"Ilja Kuzborskij",
"Csaba Szepesvari"
] | null | null | A key problem in the theory of meta-learning is to understand how the task distributions influence transfer risk, the expected error of a meta-learner on a new task drawn from the unknown task distribution. In this paper, focusing on fixed design linear regression with Gaussian noise and a Gaussian task (or parameter) ... | [] | null | 520 | 2011.00344 | title_snapshot |
v139/kopetzki21a | Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichlet-based Models Reliable? | https://proceedings.mlr.press/v139/kopetzki21a.html | [
"Anna-Kathrin Kopetzki",
"Bertrand Charpentier",
"Daniel Zügner",
"Sandhya Giri",
"Stephan Günnemann"
] | null | null | Dirichlet-based uncertainty (DBU) models are a recent and promising class of uncertainty-aware models. DBU models predict the parameters of a Dirichlet distribution to provide fast, high-quality uncertainty estimates alongside with class predictions. In this work, we present the first large-scale, in-depth study of the... | [] | null | 521 | 2010.14986 | title_snapshot |
v139/korba21a | Kernel Stein Discrepancy Descent | https://proceedings.mlr.press/v139/korba21a.html | [
"Anna Korba",
"Pierre-Cyril Aubin-Frankowski",
"Szymon Majewski",
"Pierre Ablin"
] | null | null | Among dissimilarities between probability distributions, the Kernel Stein Discrepancy (KSD) has received much interest recently. We investigate the properties of its Wasserstein gradient flow to approximate a target probability distribution $\pi$ on $\mathbb{R}^d$, known up to a normalization constant. This leads to a ... | [] | null | 522 | 2105.09994 | title_snapshot |
v139/kosaian21a | Boosting the Throughput and Accelerator Utilization of Specialized CNN Inference Beyond Increasing Batch Size | https://proceedings.mlr.press/v139/kosaian21a.html | [
"Jack Kosaian",
"Amar Phanishayee",
"Matthai Philipose",
"Debadeepta Dey",
"Rashmi Vinayak"
] | null | null | Datacenter vision systems widely use small, specialized convolutional neural networks (CNNs) trained on specific tasks for high-throughput inference. These settings employ accelerators with massive computational capacity, but which specialized CNNs underutilize due to having low arithmetic intensity. This results in su... | [] | null | 523 | null | null |
v139/kosiorek21a | NeRF-VAE: A Geometry Aware 3D Scene Generative Model | https://proceedings.mlr.press/v139/kosiorek21a.html | [
"Adam R Kosiorek",
"Heiko Strathmann",
"Daniel Zoran",
"Pol Moreno",
"Rosalia Schneider",
"Sona Mokra",
"Danilo Jimenez Rezende"
] | null | null | We propose NeRF-VAE, a 3D scene generative model that incorporates geometric structure via Neural Radiance Fields (NeRF) and differentiable volume rendering. In contrast to NeRF, our model takes into account shared structure across scenes, and is able to infer the structure of a novel scene—without the need to re-train... | [] | null | 524 | 2104.00587 | title_snapshot |
v139/kossen21a | Active Testing: Sample-Efficient Model Evaluation | https://proceedings.mlr.press/v139/kossen21a.html | [
"Jannik Kossen",
"Sebastian Farquhar",
"Yarin Gal",
"Tom Rainforth"
] | null | null | We introduce a new framework for sample-efficient model evaluation that we call active testing. While approaches like active learning reduce the number of labels needed for model training, existing literature largely ignores the cost of labeling test data, typically unrealistically assuming large test sets for model ev... | [] | null | 525 | 2103.05331 | title_snapshot |
v139/kostas21a | High Confidence Generalization for Reinforcement Learning | https://proceedings.mlr.press/v139/kostas21a.html | [
"James Kostas",
"Yash Chandak",
"Scott M Jordan",
"Georgios Theocharous",
"Philip Thomas"
] | null | null | We present several classes of reinforcement learning algorithms that safely generalize to Markov decision processes (MDPs) not seen during training. Specifically, we study the setting in which some set of MDPs is accessible for training. The goal is to generalize safely to MDPs that are sampled from the same distributi... | [] | null | 526 | null | null |
v139/kostrikov21a | Offline Reinforcement Learning with Fisher Divergence Critic Regularization | https://proceedings.mlr.press/v139/kostrikov21a.html | [
"Ilya Kostrikov",
"Rob Fergus",
"Jonathan Tompson",
"Ofir Nachum"
] | null | null | Many modern approaches to offline Reinforcement Learning (RL) utilize behavior regularization, typically augmenting a model-free actor critic algorithm with a penalty measuring divergence of the policy from the offline data. In this work, we propose an alternative approach to encouraging the learned policy to stay clos... | [] | null | 527 | 2103.08050 | title_snapshot |
v139/kovalev21a | ADOM: Accelerated Decentralized Optimization Method for Time-Varying Networks | https://proceedings.mlr.press/v139/kovalev21a.html | [
"Dmitry Kovalev",
"Egor Shulgin",
"Peter Richtarik",
"Alexander V Rogozin",
"Alexander Gasnikov"
] | null | null | We propose ADOM – an accelerated method for smooth and strongly convex decentralized optimization over time-varying networks. ADOM uses a dual oracle, i.e., we assume access to the gradient of the Fenchel conjugate of the individual loss functions. Up to a constant factor, which depends on the network structure only, i... | [] | null | 528 | 2102.09234 | title_snapshot |
v139/kozuno21a | Revisiting Peng’s Q($λ$) for Modern Reinforcement Learning | https://proceedings.mlr.press/v139/kozuno21a.html | [
"Tadashi Kozuno",
"Yunhao Tang",
"Mark Rowland",
"Remi Munos",
"Steven Kapturowski",
"Will Dabney",
"Michal Valko",
"David Abel"
] | null | null | Off-policy multi-step reinforcement learning algorithms consist of conservative and non-conservative algorithms: the former actively cut traces, whereas the latter do not. Recently, Munos et al. (2016) proved the convergence of conservative algorithms to an optimal Q-function. In contrast, non-conservative algorithms a... | [] | null | 529 | 2103.00107 | title_snapshot |
v139/krishnamurthy21a | Adapting to misspecification in contextual bandits with offline regression oracles | https://proceedings.mlr.press/v139/krishnamurthy21a.html | [
"Sanath Kumar Krishnamurthy",
"Vitor Hadad",
"Susan Athey"
] | null | null | Computationally efficient contextual bandits are often based on estimating a predictive model of rewards given contexts and arms using past data. However, when the reward model is not well-specified, the bandit algorithm may incur unexpected regret, so recent work has focused on algorithms that are robust to misspecifi... | [] | null | 530 | 2102.13240 | title_snapshot |
v139/krueger21a | Out-of-Distribution Generalization via Risk Extrapolation (REx) | https://proceedings.mlr.press/v139/krueger21a.html | [
"David Krueger",
"Ethan Caballero",
"Joern-Henrik Jacobsen",
"Amy Zhang",
"Jonathan Binas",
"Dinghuai Zhang",
"Remi Le Priol",
"Aaron Courville"
] | null | null | Distributional shift is one of the major obstacles when transferring machine learning prediction systems from the lab to the real world. To tackle this problem, we assume that variation across training domains is representative of the variation we might encounter at test time, but also that shifts at test time may be m... | [] | null | 531 | 2003.00688 | title_snapshot |
v139/kuchibhotla21a | Near-Optimal Confidence Sequences for Bounded Random Variables | https://proceedings.mlr.press/v139/kuchibhotla21a.html | [
"Arun K Kuchibhotla",
"Qinqing Zheng"
] | null | null | Many inference problems, such as sequential decision problems like A/B testing, adaptive sampling schemes like bandit selection, are often online in nature. The fundamental problem for online inference is to provide a sequence of confidence intervals that are valid uniformly over the growing-into-infinity sample sizes.... | [] | null | 532 | 2006.05022 | title_snapshot |
v139/kulkarni21a | Differentially Private Bayesian Inference for Generalized Linear Models | https://proceedings.mlr.press/v139/kulkarni21a.html | [
"Tejas Kulkarni",
"Joonas Jälkö",
"Antti Koskela",
"Samuel Kaski",
"Antti Honkela"
] | null | null | Generalized linear models (GLMs) such as logistic regression are among the most widely used arms in data analyst’s repertoire and often used on sensitive datasets. A large body of prior works that investigate GLMs under differential privacy (DP) constraints provide only private point estimates of the regression coeffic... | [] | null | 533 | 2011.00467 | title_snapshot |
v139/kumar21a | Bayesian Structural Adaptation for Continual Learning | https://proceedings.mlr.press/v139/kumar21a.html | [
"Abhishek Kumar",
"Sunabha Chatterjee",
"Piyush Rai"
] | null | null | Continual Learning is a learning paradigm where learning systems are trained on a sequence of tasks. The goal here is to perform well on the current task without suffering from a performance drop on the previous tasks. Two notable directions among the recent advances in continual learning with neural networks are (1) v... | [] | null | 534 | null | null |
v139/kumar21b | Implicit rate-constrained optimization of non-decomposable objectives | https://proceedings.mlr.press/v139/kumar21b.html | [
"Abhishek Kumar",
"Harikrishna Narasimhan",
"Andrew Cotter"
] | null | null | We consider a popular family of constrained optimization problems arising in machine learning that involve optimizing a non-decomposable evaluation metric with a certain thresholded form, while constraining another metric of interest. Examples of such problems include optimizing false negative rate at a fixed false pos... | [] | null | 535 | 2107.10960 | title_snapshot |
v139/kummerle21a | A Scalable Second Order Method for Ill-Conditioned Matrix Completion from Few Samples | https://proceedings.mlr.press/v139/kummerle21a.html | [
"Christian Kümmerle",
"Claudio M. Verdun"
] | null | null | We propose an iterative algorithm for low-rank matrix completion with that can be interpreted as an iteratively reweighted least squares (IRLS) algorithm, a saddle-escaping smoothing Newton method or a variable metric proximal gradient method applied to a non-convex rank surrogate. It combines the favorable data-effici... | [] | null | 536 | 2106.02119 | title_snapshot |
v139/kveton21a | Meta-Thompson Sampling | https://proceedings.mlr.press/v139/kveton21a.html | [
"Branislav Kveton",
"Mikhail Konobeev",
"Manzil Zaheer",
"Chih-Wei Hsu",
"Martin Mladenov",
"Craig Boutilier",
"Csaba Szepesvari"
] | null | null | Efficient exploration in bandits is a fundamental online learning problem. We propose a variant of Thompson sampling that learns to explore better as it interacts with bandit instances drawn from an unknown prior. The algorithm meta-learns the prior and thus we call it MetaTS. We propose several efficient implementatio... | [] | null | 537 | 2102.06129 | title_snapshot |
v139/kwon21a | Targeted Data Acquisition for Evolving Negotiation Agents | https://proceedings.mlr.press/v139/kwon21a.html | [
"Minae Kwon",
"Siddharth Karamcheti",
"Mariano-Florentino Cuellar",
"Dorsa Sadigh"
] | null | null | Successful negotiators must learn how to balance optimizing for self-interest and cooperation. Yet current artificial negotiation agents often heavily depend on the quality of the static datasets they were trained on, limiting their capacity to fashion an adaptive response balancing self-interest and cooperation. For t... | [] | null | 538 | 2106.07728 | title_snapshot |
v139/kwon21b | ASAM: Adaptive Sharpness-Aware Minimization for Scale-Invariant Learning of Deep Neural Networks | https://proceedings.mlr.press/v139/kwon21b.html | [
"Jungmin Kwon",
"Jeongseop Kim",
"Hyunseo Park",
"In Kwon Choi"
] | null | null | Recently, learning algorithms motivated from sharpness of loss surface as an effective measure of generalization gap have shown state-of-the-art performances. Nevertheless, sharpness defined in a rigid region with a fixed radius, has a drawback in sensitivity to parameter re-scaling which leaves the loss unaffected, le... | [] | null | 539 | 2102.11600 | title_snapshot |
v139/laber21a | On the price of explainability for some clustering problems | https://proceedings.mlr.press/v139/laber21a.html | [
"Eduardo S Laber",
"Lucas Murtinho"
] | null | null | The price of explainability for a clustering task can be defined as the unavoidable loss, in terms of the objective function, if we force the final partition to be explainable. Here, we study this price for the following clustering problems: $k$-means, $k$-medians, $k$-centers and maximum-spacing. We provide upper and ... | [] | null | 540 | 2101.01576 | title_snapshot |
v139/lacotte21a | Adaptive Newton Sketch: Linear-time Optimization with Quadratic Convergence and Effective Hessian Dimensionality | https://proceedings.mlr.press/v139/lacotte21a.html | [
"Jonathan Lacotte",
"Yifei Wang",
"Mert Pilanci"
] | null | null | We propose a randomized algorithm with quadratic convergence rate for convex optimization problems with a self-concordant, composite, strongly convex objective function. Our method is based on performing an approximate Newton step using a random projection of the Hessian. Our first contribution is to show that, at each... | [] | null | 541 | 2105.07291 | title_snapshot |
v139/laforgue21a | Generalization Bounds in the Presence of Outliers: a Median-of-Means Study | https://proceedings.mlr.press/v139/laforgue21a.html | [
"Pierre Laforgue",
"Guillaume Staerman",
"Stephan Clémençon"
] | null | null | In contrast to the empirical mean, the Median-of-Means (MoM) is an estimator of the mean $\theta$ of a square integrable r.v. Z, around which accurate nonasymptotic confidence bounds can be built, even when Z does not exhibit a sub-Gaussian tail behavior. Thanks to the high confidence it achieves on heavy-tailed data, ... | [] | null | 542 | 2006.05240 | title_snapshot |
v139/lam21a | Model Fusion for Personalized Learning | https://proceedings.mlr.press/v139/lam21a.html | [
"Thanh Chi Lam",
"Nghia Hoang",
"Bryan Kian Hsiang Low",
"Patrick Jaillet"
] | null | null | Production systems operating on a growing domain of analytic services often require generating warm-start solution models for emerging tasks with limited data. One potential approach to address this warm-start challenge is to adopt meta learning to generate a base model that can be adapted to solve unseen tasks with mi... | [] | null | 543 | null | null |
v139/lam21b | Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrix | https://proceedings.mlr.press/v139/lam21b.html | [
"Maximilian Lam",
"Gu-Yeon Wei",
"David Brooks",
"Vijay Janapa Reddi",
"Michael Mitzenmacher"
] | null | null | We show that aggregated model updates in federated learning may be insecure. An untrusted central server may disaggregate user updates from sums of updates across participants given repeated observations, enabling the server to recover privileged information about individual users’ private training data via traditional... | [] | null | 544 | 2106.06089 | title_snapshot |
v139/lancewicki21a | Stochastic Multi-Armed Bandits with Unrestricted Delay Distributions | https://proceedings.mlr.press/v139/lancewicki21a.html | [
"Tal Lancewicki",
"Shahar Segal",
"Tomer Koren",
"Yishay Mansour"
] | null | null | We study the stochastic Multi-Armed Bandit (MAB) problem with random delays in the feedback received by the algorithm. We consider two settings: the {\it reward dependent} delay setting, where realized delays may depend on the stochastic rewards, and the {\it reward-independent} delay setting. Our main contribution is ... | [] | null | 545 | 2106.02436 | title_snapshot |
v139/landajuela21a | Discovering symbolic policies with deep reinforcement learning | https://proceedings.mlr.press/v139/landajuela21a.html | [
"Mikel Landajuela",
"Brenden K Petersen",
"Sookyung Kim",
"Claudio P Santiago",
"Ruben Glatt",
"Nathan Mundhenk",
"Jacob F Pettit",
"Daniel Faissol"
] | null | null | Deep reinforcement learning (DRL) has proven successful for many difficult control problems by learning policies represented by neural networks. However, the complexity of neural network-based policies{—}involving thousands of composed non-linear operators{—}can render them problematic to understand, trust, and deploy.... | [] | null | 546 | null | null |
v139/lang21a | Graph Cuts Always Find a Global Optimum for Potts Models (With a Catch) | https://proceedings.mlr.press/v139/lang21a.html | [
"Hunter Lang",
"David Sontag",
"Aravindan Vijayaraghavan"
] | null | null | We prove that the alpha-expansion algorithm for MAP inference always returns a globally optimal assignment for Markov Random Fields with Potts pairwise potentials, with a catch: the returned assignment is only guaranteed to be optimal for an instance within a small perturbation of the original problem instance. In othe... | [] | null | 547 | 2011.03639 | title_snapshot |
v139/lange21a | Efficient Message Passing for 0–1 ILPs with Binary Decision Diagrams | https://proceedings.mlr.press/v139/lange21a.html | [
"Jan-Hendrik Lange",
"Paul Swoboda"
] | null | null | We present a message passing method for 0{–}1 integer linear programs. Our algorithm is based on a decomposition of the original problem into subproblems that are represented as binary deci- sion diagrams. The resulting Lagrangean dual is solved iteratively by a series of efficient block coordinate ascent steps. Our me... | [] | null | 548 | 2009.00481 | title_snapshot |
v139/larsen21a | CountSketches, Feature Hashing and the Median of Three | https://proceedings.mlr.press/v139/larsen21a.html | [
"Kasper Green Larsen",
"Rasmus Pagh",
"Jakub Tětek"
] | null | null | In this paper, we revisit the classic CountSketch method, which is a sparse, random projection that transforms a (high-dimensional) Euclidean vector $v$ to a vector of dimension $(2t-1) s$, where $t, s > 0$ are integer parameters. It is known that a CountSketch allows estimating coordinates of $v$ with variance bounded... | [] | null | 549 | 2102.02193 | title_snapshot |
v139/laturnus21a | MorphVAE: Generating Neural Morphologies from 3D-Walks using a Variational Autoencoder with Spherical Latent Space | https://proceedings.mlr.press/v139/laturnus21a.html | [
"Sophie C. Laturnus",
"Philipp Berens"
] | null | null | For the past century, the anatomy of a neuron has been considered one of its defining features: The shape of a neuron’s dendrites and axon fundamentally determines what other neurons it can connect to. These neurites have been described using mathematical tools e.g. in the context of cell type classification, but gener... | [] | null | 550 | null | null |
v139/lazic21a | Improved Regret Bound and Experience Replay in Regularized Policy Iteration | https://proceedings.mlr.press/v139/lazic21a.html | [
"Nevena Lazic",
"Dong Yin",
"Yasin Abbasi-Yadkori",
"Csaba Szepesvari"
] | null | null | In this work, we study algorithms for learning in infinite-horizon undiscounted Markov decision processes (MDPs) with function approximation. We first show that the regret analysis of the Politex algorithm (a version of regularized policy iteration) can be sharpened from $O(T^{3/4})$ to $O(\sqrt{T})$ under nearly ident... | [] | null | 551 | 2102.12611 | title_snapshot |
v139/le21a | LAMDA: Label Matching Deep Domain Adaptation | https://proceedings.mlr.press/v139/le21a.html | [
"Trung Le",
"Tuan Nguyen",
"Nhat Ho",
"Hung Bui",
"Dinh Phung"
] | null | null | Deep domain adaptation (DDA) approaches have recently been shown to perform better than their shallow rivals with better modeling capacity on complex domains (e.g., image, structural data, and sequential data). The underlying idea is to learn domain invariant representations on a latent space that can bridge the gap be... | [] | null | 552 | null | null |
v139/lederer21a | Gaussian Process-Based Real-Time Learning for Safety Critical Applications | https://proceedings.mlr.press/v139/lederer21a.html | [
"Armin Lederer",
"Alejandro J Ordóñez Conejo",
"Korbinian A Maier",
"Wenxin Xiao",
"Jonas Umlauft",
"Sandra Hirche"
] | null | null | The safe operation of physical systems typically relies on high-quality models. Since a continuous stream of data is generated during run-time, such models are often obtained through the application of Gaussian process regression because it provides guarantees on the prediction error. Due to its high computational comp... | [] | null | 553 | null | null |
v139/lee21a | Sharing Less is More: Lifelong Learning in Deep Networks with Selective Layer Transfer | https://proceedings.mlr.press/v139/lee21a.html | [
"Seungwon Lee",
"Sima Behpour",
"Eric Eaton"
] | null | null | Effective lifelong learning across diverse tasks requires the transfer of diverse knowledge, yet transferring irrelevant knowledge may lead to interference and catastrophic forgetting. In deep networks, transferring the appropriate granularity of knowledge is as important as the transfer mechanism, and must be driven b... | [] | null | 554 | null | null |
v139/lee21b | Fair Selective Classification Via Sufficiency | https://proceedings.mlr.press/v139/lee21b.html | [
"Joshua K Lee",
"Yuheng Bu",
"Deepta Rajan",
"Prasanna Sattigeri",
"Rameswar Panda",
"Subhro Das",
"Gregory W Wornell"
] | null | null | Selective classification is a powerful tool for decision-making in scenarios where mistakes are costly but abstentions are allowed. In general, by allowing a classifier to abstain, one can improve the performance of a model at the cost of reducing coverage and classifying fewer samples. However, recent work has shown, ... | [] | null | 555 | null | null |
v139/lee21c | On-the-fly Rectification for Robust Large-Vocabulary Topic Inference | https://proceedings.mlr.press/v139/lee21c.html | [
"Moontae Lee",
"Sungjun Cho",
"Kun Dong",
"David Mimno",
"David Bindel"
] | null | null | Across many data domains, co-occurrence statistics about the joint appearance of objects are powerfully informative. By transforming unsupervised learning problems into decompositions of co-occurrence statistics, spectral algorithms provide transparent and efficient algorithms for posterior inference such as latent top... | [] | null | 556 | 2111.06580 | title_snapshot |
v139/lee21d | Unsupervised Embedding Adaptation via Early-Stage Feature Reconstruction for Few-Shot Classification | https://proceedings.mlr.press/v139/lee21d.html | [
"Dong Hoon Lee",
"Sae-Young Chung"
] | null | null | We propose unsupervised embedding adaptation for the downstream few-shot classification task. Based on findings that deep neural networks learn to generalize before memorizing, we develop Early-Stage Feature Reconstruction (ESFR) — a novel adaptation scheme with feature reconstruction and dimensionality-driven early st... | [] | null | 557 | 2106.11486 | title_snapshot |
v139/lee21e | Continual Learning in the Teacher-Student Setup: Impact of Task Similarity | https://proceedings.mlr.press/v139/lee21e.html | [
"Sebastian Lee",
"Sebastian Goldt",
"Andrew Saxe"
] | null | null | Continual learning{—}the ability to learn many tasks in sequence{—}is critical for artificial learning systems. Yet standard training methods for deep networks often suffer from catastrophic forgetting, where learning new tasks erases knowledge of the earlier tasks. While catastrophic forgetting labels the problem, the... | [] | null | 558 | 2107.04384 | title_snapshot |
v139/lee21f | OptiDICE: Offline Policy Optimization via Stationary Distribution Correction Estimation | https://proceedings.mlr.press/v139/lee21f.html | [
"Jongmin Lee",
"Wonseok Jeon",
"Byungjun Lee",
"Joelle Pineau",
"Kee-Eung Kim"
] | null | null | We consider the offline reinforcement learning (RL) setting where the agent aims to optimize the policy solely from the data without further environment interactions. In offline RL, the distributional shift becomes the primary source of difficulty, which arises from the deviation of the target policy being optimized fr... | [] | null | 559 | 2106.10783 | title_snapshot |
v139/lee21g | SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement Learning | https://proceedings.mlr.press/v139/lee21g.html | [
"Kimin Lee",
"Michael Laskin",
"Aravind Srinivas",
"Pieter Abbeel"
] | null | null | Off-policy deep reinforcement learning (RL) has been successful in a range of challenging domains. However, standard off-policy RL algorithms can suffer from several issues, such as instability in Q-learning and balancing exploration and exploitation. To mitigate these issues, we present SUNRISE, a simple unified ensem... | [] | null | 560 | 2007.04938 | title_snapshot |
v139/lee21h | Achieving Near Instance-Optimality and Minimax-Optimality in Stochastic and Adversarial Linear Bandits Simultaneously | https://proceedings.mlr.press/v139/lee21h.html | [
"Chung-Wei Lee",
"Haipeng Luo",
"Chen-Yu Wei",
"Mengxiao Zhang",
"Xiaojin Zhang"
] | null | null | In this work, we develop linear bandit algorithms that automatically adapt to different environments. By plugging a novel loss estimator into the optimization problem that characterizes the instance-optimal strategy, our first algorithm not only achieves nearly instance-optimal regret in stochastic environments, but al... | [] | null | 561 | 2102.05858 | title_snapshot |
v139/lee21i | PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-training | https://proceedings.mlr.press/v139/lee21i.html | [
"Kimin Lee",
"Laura M Smith",
"Pieter Abbeel"
] | null | null | Conveying complex objectives to reinforcement learning (RL) agents can often be difficult, involving meticulous design of reward functions that are sufficiently informative yet easy enough to provide. Human-in-the-loop RL methods allow practitioners to instead interactively teach agents through tailored feedback; howev... | [] | null | 562 | 2106.05091 | title_snapshot |
v139/lei21a | Near-Optimal Linear Regression under Distribution Shift | https://proceedings.mlr.press/v139/lei21a.html | [
"Qi Lei",
"Wei Hu",
"Jason Lee"
] | null | null | Transfer learning is essential when sufficient data comes from the source domain, with scarce labeled data from the target domain. We develop estimators that achieve minimax linear risk for linear regression problems under distribution shift. Our algorithms cover different transfer learning settings including covariate... | [] | null | 563 | 2106.12108 | title_snapshot |
v139/lei21b | Stability and Generalization of Stochastic Gradient Methods for Minimax Problems | https://proceedings.mlr.press/v139/lei21b.html | [
"Yunwen Lei",
"Zhenhuan Yang",
"Tianbao Yang",
"Yiming Ying"
] | null | null | Many machine learning problems can be formulated as minimax problems such as Generative Adversarial Networks (GANs), AUC maximization and robust estimation, to mention but a few. A substantial amount of studies are devoted to studying the convergence behavior of their stochastic gradient-type algorithms. In contrast, t... | [] | null | 564 | 2105.03793 | title_snapshot |
v139/leibo21a | Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Pot | https://proceedings.mlr.press/v139/leibo21a.html | [
"Joel Z Leibo",
"Edgar A Dueñez-Guzman",
"Alexander Vezhnevets",
"John P Agapiou",
"Peter Sunehag",
"Raphael Koster",
"Jayd Matyas",
"Charlie Beattie",
"Igor Mordatch",
"Thore Graepel"
] | null | null | Existing evaluation suites for multi-agent reinforcement learning (MARL) do not assess generalization to novel situations as their primary objective (unlike supervised learning benchmarks). Our contribution, Melting Pot, is a MARL evaluation suite that fills this gap and uses reinforcement learning to reduce the human ... | [] | null | 565 | 2107.06857 | title_snapshot |
v139/leimkuhler21a | Better Training using Weight-Constrained Stochastic Dynamics | https://proceedings.mlr.press/v139/leimkuhler21a.html | [
"Benedict Leimkuhler",
"Tiffany J Vlaar",
"Timothée Pouchon",
"Amos Storkey"
] | null | null | We employ constraints to control the parameter space of deep neural networks throughout training. The use of customised, appropriately designed constraints can reduce the vanishing/exploding gradients problem, improve smoothness of classification boundaries, control weight magnitudes and stabilize deep neural networks,... | [] | null | 566 | 2106.10704 | title_snapshot |
v139/leino21a | Globally-Robust Neural Networks | https://proceedings.mlr.press/v139/leino21a.html | [
"Klas Leino",
"Zifan Wang",
"Matt Fredrikson"
] | null | null | The threat of adversarial examples has motivated work on training certifiably robust neural networks to facilitate efficient verification of local robustness at inference time. We formalize a notion of global robustness, which captures the operational properties of on-line local robustness certification while yielding ... | [] | null | 567 | 2102.08452 | title_snapshot |
v139/leme21a | Learning to Price Against a Moving Target | https://proceedings.mlr.press/v139/leme21a.html | [
"Renato Paes Leme",
"Balasubramanian Sivan",
"Yifeng Teng",
"Pratik Worah"
] | null | null | In the Learning to Price setting, a seller posts prices over time with the goal of maximizing revenue while learning the buyer’s valuation. This problem is very well understood when values are stationary (fixed or iid). Here we study the problem where the buyer’s value is a moving target, i.e., they change over time ei... | [] | null | 568 | 2106.04689 | title_snapshot |
v139/lemercier21a | SigGPDE: Scaling Sparse Gaussian Processes on Sequential Data | https://proceedings.mlr.press/v139/lemercier21a.html | [
"Maud Lemercier",
"Cristopher Salvi",
"Thomas Cass",
"Edwin V. Bonilla",
"Theodoros Damoulas",
"Terry J Lyons"
] | null | null | Making predictions and quantifying their uncertainty when the input data is sequential is a fundamental learning challenge, recently attracting increasing attention. We develop SigGPDE, a new scalable sparse variational inference framework for Gaussian Processes (GPs) on sequential data. Our contribution is twofold. Fi... | [] | null | 569 | 2105.04211 | title_snapshot |
v139/levanon21a | Strategic Classification Made Practical | https://proceedings.mlr.press/v139/levanon21a.html | [
"Sagi Levanon",
"Nir Rosenfeld"
] | null | null | Strategic classification regards the problem of learning in settings where users can strategically modify their features to improve outcomes. This setting applies broadly, and has received much recent attention. But despite its practical significance, work in this space has so far been predominantly theoretical. In thi... | [] | null | 570 | 2103.01826 | title_snapshot |
v139/levine21a | Improved, Deterministic Smoothing for L_1 Certified Robustness | https://proceedings.mlr.press/v139/levine21a.html | [
"Alexander J Levine",
"Soheil Feizi"
] | null | null | Randomized smoothing is a general technique for computing sample-dependent robustness guarantees against adversarial attacks for deep classifiers. Prior works on randomized smoothing against L_1 adversarial attacks use additive smoothing noise and provide probabilistic robustness guarantees. In this work, we propose a ... | [] | null | 571 | 2103.10834 | title_snapshot |
v139/lewis21a | BASE Layers: Simplifying Training of Large, Sparse Models | https://proceedings.mlr.press/v139/lewis21a.html | [
"Mike Lewis",
"Shruti Bhosale",
"Tim Dettmers",
"Naman Goyal",
"Luke Zettlemoyer"
] | null | null | We introduce a new balanced assignment of experts (BASE) layer for large language models that greatly simplifies existing high capacity sparse layers. Sparse layers can dramatically improve the efficiency of training and inference by routing each token to specialized expert modules that contain only a small fraction of... | [] | null | 572 | 2103.16716 | title_snapshot |
v139/lezama21a | Run-Sort-ReRun: Escaping Batch Size Limitations in Sliced Wasserstein Generative Models | https://proceedings.mlr.press/v139/lezama21a.html | [
"Jose Lezama",
"Wei Chen",
"Qiang Qiu"
] | null | null | When training an implicit generative model, ideally one would like the generator to reproduce all the different modes and subtleties of the target distribution. Naturally, when comparing two empirical distributions, the larger the sample population, the more these statistical nuances can be captured. However, existing ... | [] | null | 573 | null | null |
v139/li21a | PAGE: A Simple and Optimal Probabilistic Gradient Estimator for Nonconvex Optimization | https://proceedings.mlr.press/v139/li21a.html | [
"Zhize Li",
"Hongyan Bao",
"Xiangliang Zhang",
"Peter Richtarik"
] | null | null | In this paper, we propose a novel stochastic gradient estimator—ProbAbilistic Gradient Estimator (PAGE)—for nonconvex optimization. PAGE is easy to implement as it is designed via a small adjustment to vanilla SGD: in each iteration, PAGE uses the vanilla minibatch SGD update with probability $p_t$ or reuses the previo... | [] | null | 574 | 2008.10898 | title_snapshot |
v139/li21b | Tightening the Dependence on Horizon in the Sample Complexity of Q-Learning | https://proceedings.mlr.press/v139/li21b.html | [
"Gen Li",
"Changxiao Cai",
"Yuxin Chen",
"Yuantao Gu",
"Yuting Wei",
"Yuejie Chi"
] | null | null | Q-learning, which seeks to learn the optimal Q-function of a Markov decision process (MDP) in a model-free fashion, lies at the heart of reinforcement learning. Focusing on the synchronous setting (such that independent samples for all state-action pairs are queried via a generative model in each iteration), substantia... | [] | null | 575 | null | null |
v139/li21c | Winograd Algorithm for AdderNet | https://proceedings.mlr.press/v139/li21c.html | [
"Wenshuo Li",
"Hanting Chen",
"Mingqiang Huang",
"Xinghao Chen",
"Chunjing Xu",
"Yunhe Wang"
] | null | null | Adder neural network (AdderNet) is a new kind of deep model that replaces the original massive multiplications in convolutions by additions while preserving the high performance. Since the hardware complexity of additions is much lower than that of multiplications, the overall energy consumption is thus reduced signifi... | [] | null | 576 | 2105.05530 | title_snapshot |
v139/li21d | A Free Lunch From ANN: Towards Efficient, Accurate Spiking Neural Networks Calibration | https://proceedings.mlr.press/v139/li21d.html | [
"Yuhang Li",
"Shikuang Deng",
"Xin Dong",
"Ruihao Gong",
"Shi Gu"
] | null | null | Spiking Neural Network (SNN) has been recognized as one of the next generation of neural networks. Conventionally, SNN can be converted from a pre-trained ANN by only replacing the ReLU activation to spike activation while keeping the parameters intact. Perhaps surprisingly, in this work we show that a proper way to ca... | [] | null | 577 | 2106.06984 | title_snapshot |
v139/li21e | Privacy-Preserving Feature Selection with Secure Multiparty Computation | https://proceedings.mlr.press/v139/li21e.html | [
"Xiling Li",
"Rafael Dowsley",
"Martine De Cock"
] | null | null | Existing work on privacy-preserving machine learning with Secure Multiparty Computation (MPC) is almost exclusively focused on model training and on inference with trained models, thereby overlooking the important data pre-processing stage. In this work, we propose the first MPC based protocol for private feature selec... | [] | null | 578 | 2102.03517 | title_snapshot |
v139/li21f | Theory of Spectral Method for Union of Subspaces-Based Random Geometry Graph | https://proceedings.mlr.press/v139/li21f.html | [
"Gen Li",
"Yuantao Gu"
] | null | null | Spectral method is a commonly used scheme to cluster data points lying close to Union of Subspaces, a task known as Subspace Clustering. The typical usage is to construct a Random Geometry Graph first and then apply spectral method to the graph to obtain clustering result. The latter step has been coined the name Spect... | [] | null | 579 | 1907.10906 | title_snapshot |
v139/li21g | MURAL: Meta-Learning Uncertainty-Aware Rewards for Outcome-Driven Reinforcement Learning | https://proceedings.mlr.press/v139/li21g.html | [
"Kevin Li",
"Abhishek Gupta",
"Ashwin Reddy",
"Vitchyr H Pong",
"Aurick Zhou",
"Justin Yu",
"Sergey Levine"
] | null | null | Exploration in reinforcement learning is, in general, a challenging problem. A common technique to make learning easier is providing demonstrations from a human supervisor, but such demonstrations can be expensive and time-consuming to acquire. In this work, we study a more tractable class of reinforcement learning pro... | [] | null | 580 | 2107.07184 | title_snapshot |
v139/li21h | Ditto: Fair and Robust Federated Learning Through Personalization | https://proceedings.mlr.press/v139/li21h.html | [
"Tian Li",
"Shengyuan Hu",
"Ahmad Beirami",
"Virginia Smith"
] | null | null | Fairness and robustness are two important concerns for federated learning systems. In this work, we identify that robustness to data and model poisoning attacks and fairness, measured as the uniformity of performance across devices, are competing constraints in statistically heterogeneous networks. To address these con... | [] | null | 581 | 2012.04221 | title_snapshot |
v139/li21i | Quantization Algorithms for Random Fourier Features | https://proceedings.mlr.press/v139/li21i.html | [
"Xiaoyun Li",
"Ping Li"
] | null | null | The method of random projection (RP) is the standard technique for dimensionality reduction, approximate near neighbor search, compressed sensing, etc., which provides a simple and effective scheme for approximating pairwise inner products and Euclidean distances in massive data. Closely related to RP, the method of ra... | [] | null | 582 | 2102.13079 | title_snapshot |
v139/li21j | Approximate Group Fairness for Clustering | https://proceedings.mlr.press/v139/li21j.html | [
"Bo Li",
"Lijun Li",
"Ankang Sun",
"Chenhao Wang",
"Yingfan Wang"
] | null | null | We incorporate group fairness into the algorithmic centroid clustering problem, where $k$ centers are to be located to serve $n$ agents distributed in a metric space. We refine the notion of proportional fairness proposed in [Chen et al., ICML 2019] as {\em core fairness}. A $k$-clustering is in the core if no coalitio... | [] | null | 583 | 2203.17146 | title_snapshot |
v139/li21k | Sharper Generalization Bounds for Clustering | https://proceedings.mlr.press/v139/li21k.html | [
"Shaojie Li",
"Yong Liu"
] | null | null | Existing generalization analysis of clustering mainly focuses on specific instantiations, such as (kernel) $k$-means, and a unified framework for studying clustering performance is still lacking. Besides, the existing excess clustering risk bounds are mostly of order $\mathcal{O}(K/\sqrt{n})$ provided that the underlyi... | [] | null | 584 | null | null |
v139/li21l | Provably End-to-end Label-noise Learning without Anchor Points | https://proceedings.mlr.press/v139/li21l.html | [
"Xuefeng Li",
"Tongliang Liu",
"Bo Han",
"Gang Niu",
"Masashi Sugiyama"
] | null | null | In label-noise learning, the transition matrix plays a key role in building statistically consistent classifiers. Existing consistent estimators for the transition matrix have been developed by exploiting anchor points. However, the anchor-point assumption is not always satisfied in real scenarios. In this paper, we pr... | [] | null | 585 | 2102.02400 | title_snapshot |
v139/li21m | A Novel Method to Solve Neural Knapsack Problems | https://proceedings.mlr.press/v139/li21m.html | [
"Duanshun Li",
"Jing Liu",
"Dongeun Lee",
"Ali Seyedmazloom",
"Giridhar Kaushik",
"Kookjin Lee",
"Noseong Park"
] | null | null | 0-1 knapsack is of fundamental importance across many fields. In this paper, we present a game-theoretic method to solve 0-1 knapsack problems (KPs) where the number of items (products) is large and the values of items are not predetermined but decided by an external value assignment function (e.g., a neural network in... | [] | null | 586 | null | null |
v139/li21n | Mixed Cross Entropy Loss for Neural Machine Translation | https://proceedings.mlr.press/v139/li21n.html | [
"Haoran Li",
"Wei Lu"
] | null | null | In neural machine translation, Cross Entropy loss (CE) is the standard loss function in two training methods of auto-regressive models, i.e., teacher forcing and scheduled sampling. In this paper, we propose mixed Cross Entropy loss (mixed CE) as a substitute for CE in both training approaches. In teacher forcing, the ... | [] | null | 587 | 2106.15880 | title_snapshot |
v139/li21o | Training Graph Neural Networks with 1000 Layers | https://proceedings.mlr.press/v139/li21o.html | [
"Guohao Li",
"Matthias Müller",
"Bernard Ghanem",
"Vladlen Koltun"
] | null | null | Deep graph neural networks (GNNs) have achieved excellent results on various tasks on increasingly large graph datasets with millions of nodes and edges. However, memory complexity has become a major obstacle when training deep GNNs for practical applications due to the immense number of nodes, edges, and intermediate ... | [] | null | 588 | 2106.07476 | title_snapshot |
v139/li21p | Active Feature Acquisition with Generative Surrogate Models | https://proceedings.mlr.press/v139/li21p.html | [
"Yang Li",
"Junier Oliva"
] | null | null | Many real-world situations allow for the acquisition of additional relevant information when making an assessment with limited or uncertain data. However, traditional ML approaches either require all features to be acquired beforehand or regard part of them as missing data that cannot be acquired. In this work, we cons... | [] | null | 589 | 2010.02433 | title_snapshot |
v139/li21q | Partially Observed Exchangeable Modeling | https://proceedings.mlr.press/v139/li21q.html | [
"Yang Li",
"Junier Oliva"
] | null | null | Modeling dependencies among features is fundamental for many machine learning tasks. Although there are often multiple related instances that may be leveraged to inform conditional dependencies, typical approaches only model conditional dependencies over individual instances. In this work, we propose a novel framework,... | [] | null | 590 | 2102.06083 | title_snapshot |
v139/li21r | Testing DNN-based Autonomous Driving Systems under Critical Environmental Conditions | https://proceedings.mlr.press/v139/li21r.html | [
"Zhong Li",
"Minxue Pan",
"Tian Zhang",
"Xuandong Li"
] | null | null | Due to the increasing usage of Deep Neural Network (DNN) based autonomous driving systems (ADS) where erroneous or unexpected behaviours can lead to catastrophic accidents, testing such systems is of growing importance. Existing approaches often just focus on finding erroneous behaviours and have not thoroughly studied... | [] | null | 591 | null | null |
v139/li21s | The Symmetry between Arms and Knapsacks: A Primal-Dual Approach for Bandits with Knapsacks | https://proceedings.mlr.press/v139/li21s.html | [
"Xiaocheng Li",
"Chunlin Sun",
"Yinyu Ye"
] | null | null | In this paper, we study the bandits with knapsacks (BwK) problem and develop a primal-dual based algorithm that achieves a problem-dependent logarithmic regret bound. The BwK problem extends the multi-arm bandit (MAB) problem to model the resource consumption, and the existing BwK literature has been mainly focused on ... | [] | null | 592 | 2102.06385 | title_snapshot |
v139/li21t | Distributionally Robust Optimization with Markovian Data | https://proceedings.mlr.press/v139/li21t.html | [
"Mengmeng Li",
"Tobias Sutter",
"Daniel Kuhn"
] | null | null | We study a stochastic program where the probability distribution of the uncertain problem parameters is unknown and only indirectly observed via finitely many correlated samples generated by an unknown Markov chain with $d$ states. We propose a data-driven distributionally robust optimization model to estimate the prob... | [] | null | 593 | 2106.06741 | title_snapshot |
v139/li21u | Communication-Efficient Distributed SVD via Local Power Iterations | https://proceedings.mlr.press/v139/li21u.html | [
"Xiang Li",
"Shusen Wang",
"Kun Chen",
"Zhihua Zhang"
] | null | null | We study distributed computing of the truncated singular value decomposition (SVD). We develop an algorithm that we call \texttt{LocalPower} for improving communication efficiency. Specifically, we uniformly partition the dataset among $m$ nodes and alternate between multiple (precisely $p$) local power iterations and ... | [] | null | 594 | 2002.08014 | title_snapshot |
v139/li21v | FILTRA: Rethinking Steerable CNN by Filter Transform | https://proceedings.mlr.press/v139/li21v.html | [
"Bo Li",
"Qili Wang",
"Gim Hee Lee"
] | null | null | Steerable CNN imposes the prior knowledge of transformation invariance or equivariance in the network architecture to enhance the the network robustness on geometry transformation of data and reduce overfitting. It has been an intuitive and widely used technique to construct a steerable filter by augmenting a filter wi... | [] | null | 595 | 2105.11636 | title_snapshot |
v139/li21w | Online Unrelated Machine Load Balancing with Predictions Revisited | https://proceedings.mlr.press/v139/li21w.html | [
"Shi Li",
"Jiayi Xian"
] | null | null | We study the online load balancing problem with machine learned predictions, and give results that improve upon and extend those in a recent paper by Lattanzi et al. (2020). First, we design deterministic and randomized online rounding algorithms for the problem in the unrelated machine setting, with $O(\frac{\log m}{\... | [] | null | 596 | null | null |
v139/li21x | Asymptotic Normality and Confidence Intervals for Prediction Risk of the Min-Norm Least Squares Estimator | https://proceedings.mlr.press/v139/li21x.html | [
"Zeng Li",
"Chuanlong Xie",
"Qinwen Wang"
] | null | null | This paper quantifies the uncertainty of prediction risk for the min-norm least squares estimator in high-dimensional linear regression models. We establish the asymptotic normality of prediction risk when both the sample size and the number of features tend to infinity. Based on the newly established central limit the... | [] | null | 597 | null | null |
v139/li21y | TeraPipe: Token-Level Pipeline Parallelism for Training Large-Scale Language Models | https://proceedings.mlr.press/v139/li21y.html | [
"Zhuohan Li",
"Siyuan Zhuang",
"Shiyuan Guo",
"Danyang Zhuo",
"Hao Zhang",
"Dawn Song",
"Ion Stoica"
] | null | null | Model parallelism has become a necessity for training modern large-scale deep language models. In this work, we identify a new and orthogonal dimension from existing model parallel approaches: it is possible to perform pipeline parallelism within a single training sequence for Transformer-based language models thanks t... | [] | null | 598 | 2102.07988 | title_snapshot |
v139/li21z | A Second look at Exponential and Cosine Step Sizes: Simplicity, Adaptivity, and Performance | https://proceedings.mlr.press/v139/li21z.html | [
"Xiaoyu Li",
"Zhenxun Zhuang",
"Francesco Orabona"
] | null | null | Stochastic Gradient Descent (SGD) is a popular tool in training large-scale machine learning models. Its performance, however, is highly variable, depending crucially on the choice of the step sizes. Accordingly, a variety of strategies for tuning the step sizes have been proposed, ranging from coordinate-wise approach... | [] | null | 599 | 2002.05273 | title_snapshot |
v139/liang21a | Towards Understanding and Mitigating Social Biases in Language Models | https://proceedings.mlr.press/v139/liang21a.html | [
"Paul Pu Liang",
"Chiyu Wu",
"Louis-Philippe Morency",
"Ruslan Salakhutdinov"
] | null | null | As machine learning methods are deployed in real-world settings such as healthcare, legal systems, and social science, it is crucial to recognize how they shape social biases and stereotypes in these sensitive decision-making processes. Among such real-world deployments are large-scale pretrained language models (LMs) ... | [] | null | 600 | 2106.13219 | title_snapshot |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.