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v139/creager21a | Environment Inference for Invariant Learning | https://proceedings.mlr.press/v139/creager21a.html | [
"Elliot Creager",
"Joern-Henrik Jacobsen",
"Richard Zemel"
] | null | null | Learning models that gracefully handle distribution shifts is central to research on domain generalization, robust optimization, and fairness. A promising formulation is domain-invariant learning, which identifies the key issue of learning which features are domain-specific versus domain-invariant. An important assumpt... | [] | null | 201 | 2010.07249 | title_snapshot |
v139/croce21a | Mind the Box: $l_1$-APGD for Sparse Adversarial Attacks on Image Classifiers | https://proceedings.mlr.press/v139/croce21a.html | [
"Francesco Croce",
"Matthias Hein"
] | null | null | We show that when taking into account also the image domain $[0,1]^d$, established $l_1$-projected gradient descent (PGD) attacks are suboptimal as they do not consider that the effective threat model is the intersection of the $l_1$-ball and $[0,1]^d$. We study the expected sparsity of the steepest descent step for th... | [] | null | 202 | 2103.01208 | title_snapshot |
v139/cui21a | Parameterless Transductive Feature Re-representation for Few-Shot Learning | https://proceedings.mlr.press/v139/cui21a.html | [
"Wentao Cui",
"Yuhong Guo"
] | null | null | Recent literature in few-shot learning (FSL) has shown that transductive methods often outperform their inductive counterparts. However, most transductive solutions, particularly the meta-learning based ones, require inserting trainable parameters on top of some inductive baselines to facilitate transduction. In this p... | [] | null | 203 | null | null |
v139/cui21b | Randomized Algorithms for Submodular Function Maximization with a $k$-System Constraint | https://proceedings.mlr.press/v139/cui21b.html | [
"Shuang Cui",
"Kai Han",
"Tianshuai Zhu",
"Jing Tang",
"Benwei Wu",
"He Huang"
] | null | null | Submodular optimization has numerous applications such as crowdsourcing and viral marketing. In this paper, we study the problem of non-negative submodular function maximization subject to a $k$-system constraint, which generalizes many other important constraints in submodular optimization such as cardinality constrai... | [] | null | 204 | null | null |
v139/cui21c | GBHT: Gradient Boosting Histogram Transform for Density Estimation | https://proceedings.mlr.press/v139/cui21c.html | [
"Jingyi Cui",
"Hanyuan Hang",
"Yisen Wang",
"Zhouchen Lin"
] | null | null | In this paper, we propose a density estimation algorithm called \textit{Gradient Boosting Histogram Transform} (GBHT), where we adopt the \textit{Negative Log Likelihood} as the loss function to make the boosting procedure available for the unsupervised tasks. From a learning theory viewpoint, we first prove fast conve... | [] | null | 205 | 2106.05738 | title_snapshot |
v139/cummins21a | ProGraML: A Graph-based Program Representation for Data Flow Analysis and Compiler Optimizations | https://proceedings.mlr.press/v139/cummins21a.html | [
"Chris Cummins",
"Zacharias V. Fisches",
"Tal Ben-Nun",
"Torsten Hoefler",
"Michael F P O’Boyle",
"Hugh Leather"
] | null | null | Machine learning (ML) is increasingly seen as a viable approach for building compiler optimization heuristics, but many ML methods cannot replicate even the simplest of the data flow analyses that are critical to making good optimization decisions. We posit that if ML cannot do that, then it is insufficiently able to r... | [] | null | 206 | null | null |
v139/curi21a | Combining Pessimism with Optimism for Robust and Efficient Model-Based Deep Reinforcement Learning | https://proceedings.mlr.press/v139/curi21a.html | [
"Sebastian Curi",
"Ilija Bogunovic",
"Andreas Krause"
] | null | null | In real-world tasks, reinforcement learning (RL) agents frequently encounter situations that are not present during training time. To ensure reliable performance, the RL agents need to exhibit robustness to such worst-case situations. The robust-RL framework addresses this challenge via a minimax optimization between a... | [] | null | 207 | 2103.10369 | title_snapshot |
v139/curmei21a | Quantifying Availability and Discovery in Recommender Systems via Stochastic Reachability | https://proceedings.mlr.press/v139/curmei21a.html | [
"Mihaela Curmei",
"Sarah Dean",
"Benjamin Recht"
] | null | null | In this work, we consider how preference models in interactive recommendation systems determine the availability of content and users’ opportunities for discovery. We propose an evaluation procedure based on stochastic reachability to quantify the maximum probability of recommending a target piece of content to an user... | [] | null | 208 | 2107.00833 | title_snapshot |
v139/cutkosky21a | Dynamic Balancing for Model Selection in Bandits and RL | https://proceedings.mlr.press/v139/cutkosky21a.html | [
"Ashok Cutkosky",
"Christoph Dann",
"Abhimanyu Das",
"Claudio Gentile",
"Aldo Pacchiano",
"Manish Purohit"
] | null | null | We propose a framework for model selection by combining base algorithms in stochastic bandits and reinforcement learning. We require a candidate regret bound for each base algorithm that may or may not hold. We select base algorithms to play in each round using a “balancing condition” on the candidate regret bounds. Ou... | [] | null | 209 | null | null |
v139/d-ascoli21a | ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases | https://proceedings.mlr.press/v139/d-ascoli21a.html | [
"Stéphane D’Ascoli",
"Hugo Touvron",
"Matthew L Leavitt",
"Ari S Morcos",
"Giulio Biroli",
"Levent Sagun"
] | null | null | Convolutional architectures have proven extremely successful for vision tasks. Their hard inductive biases enable sample-efficient learning, but come at the cost of a potentially lower performance ceiling. Vision Transformers (ViTs) rely on more flexible self-attention layers, and have recently outperformed CNNs for im... | [] | null | 210 | 2103.10697 | title_snapshot |
v139/d-orsi21a | Consistent regression when oblivious outliers overwhelm | https://proceedings.mlr.press/v139/d-orsi21a.html | [
"Tommaso D’Orsi",
"Gleb Novikov",
"David Steurer"
] | null | null | We consider a robust linear regression model $y=X\beta^* + \eta$, where an adversary oblivious to the design $X\in \mathbb{R}^{n\times d}$ may choose $\eta$ to corrupt all but an $\alpha$ fraction of the observations $y$ in an arbitrary way. Prior to our work, even for Gaussian $X$, no estimator for $\beta^*$ was known... | [] | null | 211 | 2009.14774 | title_snapshot |
v139/dadashi21a | Offline Reinforcement Learning with Pseudometric Learning | https://proceedings.mlr.press/v139/dadashi21a.html | [
"Robert Dadashi",
"Shideh Rezaeifar",
"Nino Vieillard",
"Léonard Hussenot",
"Olivier Pietquin",
"Matthieu Geist"
] | null | null | Offline Reinforcement Learning methods seek to learn a policy from logged transitions of an environment, without any interaction. In the presence of function approximation, and under the assumption of limited coverage of the state-action space of the environment, it is necessary to enforce the policy to visit state-act... | [] | null | 212 | 2103.01948 | title_snapshot |
v139/daghaghi21a | A Tale of Two Efficient and Informative Negative Sampling Distributions | https://proceedings.mlr.press/v139/daghaghi21a.html | [
"Shabnam Daghaghi",
"Tharun Medini",
"Nicholas Meisburger",
"Beidi Chen",
"Mengnan Zhao",
"Anshumali Shrivastava"
] | null | null | Softmax classifiers with a very large number of classes naturally occur in many applications such as natural language processing and information retrieval. The calculation of full softmax is costly from the computational and energy perspective. There have been various sampling approaches to overcome this challenge, pop... | [] | null | 213 | 2012.15843 | title_snapshot |
v139/dahiya21a | SiameseXML: Siamese Networks meet Extreme Classifiers with 100M Labels | https://proceedings.mlr.press/v139/dahiya21a.html | [
"Kunal Dahiya",
"Ananye Agarwal",
"Deepak Saini",
"Gururaj K",
"Jian Jiao",
"Amit Singh",
"Sumeet Agarwal",
"Purushottam Kar",
"Manik Varma"
] | null | null | Deep extreme multi-label learning (XML) requires training deep architectures that can tag a data point with its most relevant subset of labels from an extremely large label set. XML applications such as ad and product recommendation involve labels rarely seen during training but which nevertheless hold the key to recom... | [] | null | 214 | null | null |
v139/dahiya21b | Fixed-Parameter and Approximation Algorithms for PCA with Outliers | https://proceedings.mlr.press/v139/dahiya21b.html | [
"Yogesh Dahiya",
"Fedor Fomin",
"Fahad Panolan",
"Kirill Simonov"
] | null | null | PCA with Outliers is the fundamental problem of identifying an underlying low-dimensional subspace in a data set corrupted with outliers. A large body of work is devoted to the information-theoretic aspects of this problem. However, from the computational perspective, its complexity is still not well-understood. We stu... | [] | null | 215 | null | null |
v139/dai21a | Sliced Iterative Normalizing Flows | https://proceedings.mlr.press/v139/dai21a.html | [
"Biwei Dai",
"Uros Seljak"
] | null | null | We develop an iterative (greedy) deep learning (DL) algorithm which is able to transform an arbitrary probability distribution function (PDF) into the target PDF. The model is based on iterative Optimal Transport of a series of 1D slices, matching on each slice the marginal PDF to the target. The axes of the orthogonal... | [] | null | 216 | 2007.00674 | title_snapshot |
v139/dam21a | Convex Regularization in Monte-Carlo Tree Search | https://proceedings.mlr.press/v139/dam21a.html | [
"Tuan Q Dam",
"Carlo D’Eramo",
"Jan Peters",
"Joni Pajarinen"
] | null | null | Monte-Carlo planning and Reinforcement Learning (RL) are essential to sequential decision making. The recent AlphaGo and AlphaZero algorithms have shown how to successfully combine these two paradigms to solve large-scale sequential decision problems. These methodologies exploit a variant of the well-known UCT algorith... | [] | null | 217 | 2007.00391 | title_snapshot |
v139/dance21a | Demonstration-Conditioned Reinforcement Learning for Few-Shot Imitation | https://proceedings.mlr.press/v139/dance21a.html | [
"Christopher R. Dance",
"Julien Perez",
"Théo Cachet"
] | null | null | In few-shot imitation, an agent is given a few demonstrations of a previously unseen task, and must then successfully perform that task. We propose a novel approach to learning few-shot-imitation agents that we call demonstration-conditioned reinforcement learning (DCRL). Given a training set consisting of demonstratio... | [] | null | 218 | null | null |
v139/danesh21a | Re-understanding Finite-State Representations of Recurrent Policy Networks | https://proceedings.mlr.press/v139/danesh21a.html | [
"Mohamad H Danesh",
"Anurag Koul",
"Alan Fern",
"Saeed Khorram"
] | null | null | We introduce an approach for understanding control policies represented as recurrent neural networks. Recent work has approached this problem by transforming such recurrent policy networks into finite-state machines (FSM) and then analyzing the equivalent minimized FSM. While this led to interesting insights, the minim... | [] | null | 219 | 2006.03745 | title_snapshot |
v139/daneshmand21a | Newton Method over Networks is Fast up to the Statistical Precision | https://proceedings.mlr.press/v139/daneshmand21a.html | [
"Amir Daneshmand",
"Gesualdo Scutari",
"Pavel Dvurechensky",
"Alexander Gasnikov"
] | null | null | We propose a distributed cubic regularization of the Newton method for solving (constrained) empirical risk minimization problems over a network of agents, modeled as undirected graph. The algorithm employs an inexact, preconditioned Newton step at each agent’s side: the gradient of the centralized loss is iteratively ... | [] | null | 220 | 2102.06780 | title_snapshot |
v139/danks21a | BasisDeVAE: Interpretable Simultaneous Dimensionality Reduction and Feature-Level Clustering with Derivative-Based Variational Autoencoders | https://proceedings.mlr.press/v139/danks21a.html | [
"Dominic Danks",
"Christopher Yau"
] | null | null | The Variational Autoencoder (VAE) performs effective nonlinear dimensionality reduction in a variety of problem settings. However, the black-box neural network decoder function typically employed limits the ability of the decoder function to be constrained and interpreted, making the use of VAEs problematic in settings... | [] | null | 221 | null | null |
v139/daras21a | Intermediate Layer Optimization for Inverse Problems using Deep Generative Models | https://proceedings.mlr.press/v139/daras21a.html | [
"Giannis Daras",
"Joseph Dean",
"Ajil Jalal",
"Alex Dimakis"
] | null | null | We propose Intermediate Layer Optimization (ILO), a novel optimization algorithm for solving inverse problems with deep generative models. Instead of optimizing only over the initial latent code, we progressively change the input layer obtaining successively more expressive generators. To explore the higher dimensional... | [] | null | 222 | 2102.07364 | title_snapshot |
v139/darestani21a | Measuring Robustness in Deep Learning Based Compressive Sensing | https://proceedings.mlr.press/v139/darestani21a.html | [
"Mohammad Zalbagi Darestani",
"Akshay S Chaudhari",
"Reinhard Heckel"
] | null | null | Deep neural networks give state-of-the-art accuracy for reconstructing images from few and noisy measurements, a problem arising for example in accelerated magnetic resonance imaging (MRI). However, recent works have raised concerns that deep-learning-based image reconstruction methods are sensitive to perturbations an... | [] | null | 223 | 2102.06103 | title_snapshot |
v139/das21a | SAINT-ACC: Safety-Aware Intelligent Adaptive Cruise Control for Autonomous Vehicles Using Deep Reinforcement Learning | https://proceedings.mlr.press/v139/das21a.html | [
"Lokesh Chandra Das",
"Myounggyu Won"
] | null | null | We present a novel adaptive cruise control (ACC) system namely SAINT-ACC: {S}afety-{A}ware {Int}elligent {ACC} system (SAINT-ACC) that is designed to achieve simultaneous optimization of traffic efficiency, driving safety, and driving comfort through dynamic adaptation of the inter-vehicle gap based on deep reinforceme... | [] | null | 224 | 2104.06506 | title_snapshot |
v139/dasoulas21a | Lipschitz normalization for self-attention layers with application to graph neural networks | https://proceedings.mlr.press/v139/dasoulas21a.html | [
"George Dasoulas",
"Kevin Scaman",
"Aladin Virmaux"
] | null | null | Attention based neural networks are state of the art in a large range of applications. However, their performance tends to degrade when the number of layers increases. In this work, we show that enforcing Lipschitz continuity by normalizing the attention scores can significantly improve the performance of deep attentio... | [] | null | 225 | 2103.04886 | title_snapshot |
v139/dass21a | Householder Sketch for Accurate and Accelerated Least-Mean-Squares Solvers | https://proceedings.mlr.press/v139/dass21a.html | [
"Jyotikrishna Dass",
"Rabi Mahapatra"
] | null | null | Least-Mean-Squares (\textsc{LMS}) solvers comprise a class of fundamental optimization problems such as linear regression, and regularized regressions such as Ridge, LASSO, and Elastic-Net. Data summarization techniques for big data generate summaries called coresets and sketches to speed up model learning under stream... | [] | null | 226 | null | null |
v139/data21a | Byzantine-Resilient High-Dimensional SGD with Local Iterations on Heterogeneous Data | https://proceedings.mlr.press/v139/data21a.html | [
"Deepesh Data",
"Suhas Diggavi"
] | null | null | We study stochastic gradient descent (SGD) with local iterations in the presence of Byzantine clients, motivated by the federated learning. The clients, instead of communicating with the server in every iteration, maintain their local models, which they update by taking several SGD iterations based on their own dataset... | [] | null | 227 | null | null |
v139/davis21a | Catformer: Designing Stable Transformers via Sensitivity Analysis | https://proceedings.mlr.press/v139/davis21a.html | [
"Jared Q Davis",
"Albert Gu",
"Krzysztof Choromanski",
"Tri Dao",
"Christopher Re",
"Chelsea Finn",
"Percy Liang"
] | null | null | Transformer architectures are widely used, but training them is non-trivial, requiring custom learning rate schedules, scaling terms, residual connections, careful placement of submodules such as normalization, and so on. In this paper, we improve upon recent analysis of Transformers and formalize a notion of sensitivi... | [] | null | 228 | null | null |
v139/dawkins21a | Diffusion Source Identification on Networks with Statistical Confidence | https://proceedings.mlr.press/v139/dawkins21a.html | [
"Quinlan E Dawkins",
"Tianxi Li",
"Haifeng Xu"
] | null | null | Diffusion source identification on networks is a problem of fundamental importance in a broad class of applications, including controlling the spreading of rumors on social media, identifying a computer virus over cyber networks, or identifying the disease center during epidemiology. Though this problem has received si... | [] | null | 229 | 2106.04800 | title_snapshot |
v139/daxberger21a | Bayesian Deep Learning via Subnetwork Inference | https://proceedings.mlr.press/v139/daxberger21a.html | [
"Erik Daxberger",
"Eric Nalisnick",
"James U Allingham",
"Javier Antoran",
"Jose Miguel Hernandez-Lobato"
] | null | null | The Bayesian paradigm has the potential to solve core issues of deep neural networks such as poor calibration and data inefficiency. Alas, scaling Bayesian inference to large weight spaces often requires restrictive approximations. In this work, we show that it suffices to perform inference over a small subset of model... | [] | null | 230 | 2010.14689 | title_snapshot |
v139/de-palma21a | Adversarial Robustness Guarantees for Random Deep Neural Networks | https://proceedings.mlr.press/v139/de-palma21a.html | [
"Giacomo De Palma",
"Bobak Kiani",
"Seth Lloyd"
] | null | null | The reliability of deep learning algorithms is fundamentally challenged by the existence of adversarial examples, which are incorrectly classified inputs that are extremely close to a correctly classified input. We explore the properties of adversarial examples for deep neural networks with random weights and biases, a... | [] | null | 231 | 2004.05923 | title_snapshot |
v139/de-roos21a | High-Dimensional Gaussian Process Inference with Derivatives | https://proceedings.mlr.press/v139/de-roos21a.html | [
"Filip de Roos",
"Alexandra Gessner",
"Philipp Hennig"
] | null | null | Although it is widely known that Gaussian processes can be conditioned on observations of the gradient, this functionality is of limited use due to the prohibitive computational cost of $\mathcal{O}(N^3 D^3)$ in data points $N$ and dimension $D$. The dilemma of gradient observations is that a single one of them comes a... | [] | null | 232 | 2102.07542 | title_snapshot |
v139/deecke21a | Transfer-Based Semantic Anomaly Detection | https://proceedings.mlr.press/v139/deecke21a.html | [
"Lucas Deecke",
"Lukas Ruff",
"Robert A. Vandermeulen",
"Hakan Bilen"
] | null | null | Detecting semantic anomalies is challenging due to the countless ways in which they may appear in real-world data. While enhancing the robustness of networks may be sufficient for modeling simplistic anomalies, there is no good known way of preparing models for all potential and unseen anomalies that can potentially oc... | [] | null | 233 | null | null |
v139/dehesa21a | Grid-Functioned Neural Networks | https://proceedings.mlr.press/v139/dehesa21a.html | [
"Javier Dehesa",
"Andrew Vidler",
"Julian Padget",
"Christof Lutteroth"
] | null | null | We introduce a new neural network architecture that we call "grid-functioned" neural networks. It utilises a grid structure of network parameterisations that can be specialised for different subdomains of the problem, while maintaining smooth, continuous behaviour. The grid gives the user flexibility to prevent gross f... | [] | null | 234 | null | null |
v139/demaine21a | Multidimensional Scaling: Approximation and Complexity | https://proceedings.mlr.press/v139/demaine21a.html | [
"Erik Demaine",
"Adam Hesterberg",
"Frederic Koehler",
"Jayson Lynch",
"John Urschel"
] | null | null | Metric Multidimensional scaling (MDS) is a classical method for generating meaningful (non-linear) low-dimensional embeddings of high-dimensional data. MDS has a long history in the statistics, machine learning, and graph drawing communities. In particular, the Kamada-Kawai force-directed graph drawing method is equiva... | [] | null | 235 | 2109.11505 | title_snapshot |
v139/deng21a | What Does Rotation Prediction Tell Us about Classifier Accuracy under Varying Testing Environments? | https://proceedings.mlr.press/v139/deng21a.html | [
"Weijian Deng",
"Stephen Gould",
"Liang Zheng"
] | null | null | Understanding classifier decision under novel environments is central to the community, and a common practice is evaluating it on labeled test sets. However, in real-world testing, image annotations are difficult and expensive to obtain, especially when the test environment is changing. A natural question then arises: ... | [] | null | 236 | 2106.05961 | title_snapshot |
v139/deng21b | Toward Better Generalization Bounds with Locally Elastic Stability | https://proceedings.mlr.press/v139/deng21b.html | [
"Zhun Deng",
"Hangfeng He",
"Weijie Su"
] | null | null | Algorithmic stability is a key characteristic to ensure the generalization ability of a learning algorithm. Among different notions of stability, \emph{uniform stability} is arguably the most popular one, which yields exponential generalization bounds. However, uniform stability only considers the worst-case loss chang... | [] | null | 237 | 2010.13988 | title_snapshot |
v139/deng21c | Revenue-Incentive Tradeoffs in Dynamic Reserve Pricing | https://proceedings.mlr.press/v139/deng21c.html | [
"Yuan Deng",
"Sebastien Lahaie",
"Vahab Mirrokni",
"Song Zuo"
] | null | null | Online advertisements are primarily sold via repeated auctions with reserve prices. In this paper, we study how to set reserves to boost revenue based on the historical bids of strategic buyers, while controlling the impact of such a policy on the incentive compatibility of the repeated auctions. Adopting an incentive ... | [] | null | 238 | null | null |
v139/dennis21a | Heterogeneity for the Win: One-Shot Federated Clustering | https://proceedings.mlr.press/v139/dennis21a.html | [
"Don Kurian Dennis",
"Tian Li",
"Virginia Smith"
] | null | null | In this work, we explore the unique challenges—and opportunities—of unsupervised federated learning (FL). We develop and analyze a one-shot federated clustering scheme, kfed, based on the widely-used Lloyd’s method for $k$-means clustering. In contrast to many supervised problems, we show that the issue of statistical ... | [] | null | 239 | 2103.00697 | title_snapshot |
v139/derakhshani21a | Kernel Continual Learning | https://proceedings.mlr.press/v139/derakhshani21a.html | [
"Mohammad Mahdi Derakhshani",
"Xiantong Zhen",
"Ling Shao",
"Cees Snoek"
] | null | null | This paper introduces kernel continual learning, a simple but effective variant of continual learning that leverages the non-parametric nature of kernel methods to tackle catastrophic forgetting. We deploy an episodic memory unit that stores a subset of samples for each task to learn task-specific classifiers based on ... | [] | null | 240 | 2107.05757 | title_snapshot |
v139/deshwal21a | Bayesian Optimization over Hybrid Spaces | https://proceedings.mlr.press/v139/deshwal21a.html | [
"Aryan Deshwal",
"Syrine Belakaria",
"Janardhan Rao Doppa"
] | null | null | We consider the problem of optimizing hybrid structures (mixture of discrete and continuous input variables) via expensive black-box function evaluations. This problem arises in many real-world applications. For example, in materials design optimization via lab experiments, discrete and continuous variables correspond ... | [] | null | 241 | 2106.04682 | title_snapshot |
v139/devlin21a | Navigation Turing Test (NTT): Learning to Evaluate Human-Like Navigation | https://proceedings.mlr.press/v139/devlin21a.html | [
"Sam Devlin",
"Raluca Georgescu",
"Ida Momennejad",
"Jaroslaw Rzepecki",
"Evelyn Zuniga",
"Gavin Costello",
"Guy Leroy",
"Ali Shaw",
"Katja Hofmann"
] | null | null | A key challenge on the path to developing agents that learn complex human-like behavior is the need to quickly and accurately quantify human-likeness. While human assessments of such behavior can be highly accurate, speed and scalability are limited. We address these limitations through a novel automated Navigation Tur... | [] | null | 242 | 2105.09637 | title_snapshot |
v139/devos21a | Versatile Verification of Tree Ensembles | https://proceedings.mlr.press/v139/devos21a.html | [
"Laurens Devos",
"Wannes Meert",
"Jesse Davis"
] | null | null | Machine learned models often must abide by certain requirements (e.g., fairness or legal). This has spurred interested in developing approaches that can provably verify whether a model satisfies certain properties. This paper introduces a generic algorithm called Veritas that enables tackling multiple different verific... | [] | null | 243 | 2010.13880 | title_snapshot |
v139/dhifallah21a | On the Inherent Regularization Effects of Noise Injection During Training | https://proceedings.mlr.press/v139/dhifallah21a.html | [
"Oussama Dhifallah",
"Yue Lu"
] | null | null | Randomly perturbing networks during the training process is a commonly used approach to improving generalization performance. In this paper, we present a theoretical study of one particular way of random perturbation, which corresponds to injecting artificial noise to the training data. We provide a precise asymptotic ... | [] | null | 244 | 2102.07379 | title_snapshot |
v139/dhulipala21a | Hierarchical Agglomerative Graph Clustering in Nearly-Linear Time | https://proceedings.mlr.press/v139/dhulipala21a.html | [
"Laxman Dhulipala",
"David Eisenstat",
"Jakub Łącki",
"Vahab Mirrokni",
"Jessica Shi"
] | null | null | We study the widely-used hierarchical agglomerative clustering (HAC) algorithm on edge-weighted graphs. We define an algorithmic framework for hierarchical agglomerative graph clustering that provides the first efficient $\tilde{O}(m)$ time exact algorithms for classic linkage measures, such as complete- and WPGMA-link... | [] | null | 245 | 2106.05610 | title_snapshot |
v139/diakonikolas21a | Learning Online Algorithms with Distributional Advice | https://proceedings.mlr.press/v139/diakonikolas21a.html | [
"Ilias Diakonikolas",
"Vasilis Kontonis",
"Christos Tzamos",
"Ali Vakilian",
"Nikos Zarifis"
] | null | null | We study the problem of designing online algorithms given advice about the input. While prior work had focused on deterministic advice, we only assume distributional access to the instances of interest, and the goal is to learn a competitive algorithm given access to i.i.d. samples. We aim to be competitive against an ... | [] | null | 246 | null | null |
v139/diamandis21a | A Wasserstein Minimax Framework for Mixed Linear Regression | https://proceedings.mlr.press/v139/diamandis21a.html | [
"Theo Diamandis",
"Yonina Eldar",
"Alireza Fallah",
"Farzan Farnia",
"Asuman Ozdaglar"
] | null | null | Multi-modal distributions are commonly used to model clustered data in statistical learning tasks. In this paper, we consider the Mixed Linear Regression (MLR) problem. We propose an optimal transport-based framework for MLR problems, Wasserstein Mixed Linear Regression (WMLR), which minimizes the Wasserstein distance ... | [] | null | 247 | 2106.07537 | title_snapshot |
v139/dickens21a | Context-Aware Online Collective Inference for Templated Graphical Models | https://proceedings.mlr.press/v139/dickens21a.html | [
"Charles Dickens",
"Connor Pryor",
"Eriq Augustine",
"Alexander Miller",
"Lise Getoor"
] | null | null | In this work, we examine online collective inference, the problem of maintaining and performing inference over a sequence of evolving graphical models. We utilize templated graphical models (TGM), a general class of graphical models expressed via templates and instantiated with data. A key challenge is minimizing the c... | [] | null | 248 | null | null |
v139/dimitriev21a | ARMS: Antithetic-REINFORCE-Multi-Sample Gradient for Binary Variables | https://proceedings.mlr.press/v139/dimitriev21a.html | [
"Aleksandar Dimitriev",
"Mingyuan Zhou"
] | null | null | Estimating the gradients for binary variables is a task that arises frequently in various domains, such as training discrete latent variable models. What has been commonly used is a REINFORCE based Monte Carlo estimation method that uses either independent samples or pairs of negatively correlated samples. To better ut... | [] | null | 249 | 2105.14141 | title_snapshot |
v139/ding21a | XOR-CD: Linearly Convergent Constrained Structure Generation | https://proceedings.mlr.press/v139/ding21a.html | [
"Fan Ding",
"Jianzhu Ma",
"Jinbo Xu",
"Yexiang Xue"
] | null | null | We propose XOR-Contrastive Divergence learning (XOR-CD), a provable approach for constrained structure generation, which remains difficult for state-of-the-art neural network and constraint reasoning approaches. XOR-CD harnesses XOR-Sampling to generate samples from the model distribution in CD learning and is guarante... | [] | null | 250 | null | null |
v139/ding21b | Dual Principal Component Pursuit for Robust Subspace Learning: Theory and Algorithms for a Holistic Approach | https://proceedings.mlr.press/v139/ding21b.html | [
"Tianyu Ding",
"Zhihui Zhu",
"Rene Vidal",
"Daniel P Robinson"
] | null | null | The Dual Principal Component Pursuit (DPCP) method has been proposed to robustly recover a subspace of high-relative dimension from corrupted data. Existing analyses and algorithms of DPCP, however, mainly focus on finding a normal to a single hyperplane that contains the inliers. Although these algorithms can be exten... | [] | null | 251 | null | null |
v139/dinh21a | Coded-InvNet for Resilient Prediction Serving Systems | https://proceedings.mlr.press/v139/dinh21a.html | [
"Tuan Dinh",
"Kangwook Lee"
] | null | null | Inspired by a new coded computation algorithm for invertible functions, we propose Coded-InvNet a new approach to design resilient prediction serving systems that can gracefully handle stragglers or node failures. Coded-InvNet leverages recent findings in the deep learning literature such as invertible neural networks,... | [] | null | 252 | 2106.06445 | title_snapshot |
v139/divol21a | Estimation and Quantization of Expected Persistence Diagrams | https://proceedings.mlr.press/v139/divol21a.html | [
"Vincent Divol",
"Theo Lacombe"
] | null | null | Persistence diagrams (PDs) are the most common descriptors used to encode the topology of structured data appearing in challenging learning tasks; think e.g. of graphs, time series or point clouds sampled close to a manifold. Given random objects and the corresponding distribution of PDs, one may want to build a statis... | [] | null | 253 | 2105.04852 | title_snapshot |
v139/domingo-enrich21a | On Energy-Based Models with Overparametrized Shallow Neural Networks | https://proceedings.mlr.press/v139/domingo-enrich21a.html | [
"Carles Domingo-Enrich",
"Alberto Bietti",
"Eric Vanden-Eijnden",
"Joan Bruna"
] | null | null | Energy-based models (EBMs) are a simple yet powerful framework for generative modeling. They are based on a trainable energy function which defines an associated Gibbs measure, and they can be trained and sampled from via well-established statistical tools, such as MCMC. Neural networks may be used as energy function a... | [] | null | 254 | 2104.07531 | title_snapshot |
v139/domingues21a | Kernel-Based Reinforcement Learning: A Finite-Time Analysis | https://proceedings.mlr.press/v139/domingues21a.html | [
"Omar Darwiche Domingues",
"Pierre Menard",
"Matteo Pirotta",
"Emilie Kaufmann",
"Michal Valko"
] | null | null | We consider the exploration-exploitation dilemma in finite-horizon reinforcement learning problems whose state-action space is endowed with a metric. We introduce Kernel-UCBVI, a model-based optimistic algorithm that leverages the smoothness of the MDP and a non-parametric kernel estimator of the rewards and transition... | [] | null | 255 | 2004.05599 | title_snapshot |
v139/dong21a | Attention is not all you need: pure attention loses rank doubly exponentially with depth | https://proceedings.mlr.press/v139/dong21a.html | [
"Yihe Dong",
"Jean-Baptiste Cordonnier",
"Andreas Loukas"
] | null | null | Attention-based architectures have become ubiquitous in machine learning. Yet, our understanding of the reasons for their effectiveness remains limited. This work proposes a new way to understand self-attention networks: we show that their output can be decomposed into a sum of smaller terms—or paths—each involving the... | [] | null | 256 | 2103.03404 | title_snapshot |
v139/donhauser21a | How rotational invariance of common kernels prevents generalization in high dimensions | https://proceedings.mlr.press/v139/donhauser21a.html | [
"Konstantin Donhauser",
"Mingqi Wu",
"Fanny Yang"
] | null | null | Kernel ridge regression is well-known to achieve minimax optimal rates in low-dimensional settings. However, its behavior in high dimensions is much less understood. Recent work establishes consistency for high-dimensional kernel regression for a number of specific assumptions on the data distribution. In this paper, w... | [] | null | 257 | 2104.04244 | title_snapshot |
v139/dragomir21a | Fast Stochastic Bregman Gradient Methods: Sharp Analysis and Variance Reduction | https://proceedings.mlr.press/v139/dragomir21a.html | [
"Radu Alexandru Dragomir",
"Mathieu Even",
"Hadrien Hendrikx"
] | null | null | We study the problem of minimizing a relatively-smooth convex function using stochastic Bregman gradient methods. We first prove the convergence of Bregman Stochastic Gradient Descent (BSGD) to a region that depends on the noise (magnitude of the gradients) at the optimum. In particular, BSGD quickly converges to the e... | [] | null | 258 | 2104.09813 | title_snapshot |
v139/du21a | Bilinear Classes: A Structural Framework for Provable Generalization in RL | https://proceedings.mlr.press/v139/du21a.html | [
"Simon Du",
"Sham Kakade",
"Jason Lee",
"Shachar Lovett",
"Gaurav Mahajan",
"Wen Sun",
"Ruosong Wang"
] | null | null | This work introduces Bilinear Classes, a new structural framework, which permit generalization in reinforcement learning in a wide variety of settings through the use of function approximation. The framework incorporates nearly all existing models in which a polynomial sample complexity is achievable, and, notably, als... | [] | null | 259 | 2103.10897 | title_snapshot |
v139/du21b | Improved Contrastive Divergence Training of Energy-Based Models | https://proceedings.mlr.press/v139/du21b.html | [
"Yilun Du",
"Shuang Li",
"Joshua Tenenbaum",
"Igor Mordatch"
] | null | null | Contrastive divergence is a popular method of training energy-based models, but is known to have difficulties with training stability. We propose an adaptation to improve contrastive divergence training by scrutinizing a gradient term that is difficult to calculate and is often left out for convenience. We show that th... | [] | null | 260 | 2012.01316 | title_snapshot |
v139/du21c | Order-Agnostic Cross Entropy for Non-Autoregressive Machine Translation | https://proceedings.mlr.press/v139/du21c.html | [
"Cunxiao Du",
"Zhaopeng Tu",
"Jing Jiang"
] | null | null | We propose a new training objective named order-agnostic cross entropy (OaXE) for fully non-autoregressive translation (NAT) models. OaXE improves the standard cross-entropy loss to ameliorate the effect of word reordering, which is a common source of the critical multimodality problem in NAT. Concretely, OaXE removes ... | [] | null | 261 | 2106.05093 | title_snapshot |
v139/du21d | Putting the “Learning" into Learning-Augmented Algorithms for Frequency Estimation | https://proceedings.mlr.press/v139/du21d.html | [
"Elbert Du",
"Franklyn Wang",
"Michael Mitzenmacher"
] | null | null | In learning-augmented algorithms, algorithms are enhanced using information from a machine learning algorithm. In turn, this suggests that we should tailor our machine-learning approach for the target algorithm. We here consider this synergy in the context of the learned count-min sketch from (Hsu et al., 2019). Learni... | [] | null | 262 | null | null |
v139/du21e | Estimating $α$-Rank from A Few Entries with Low Rank Matrix Completion | https://proceedings.mlr.press/v139/du21e.html | [
"Yali Du",
"Xue Yan",
"Xu Chen",
"Jun Wang",
"Haifeng Zhang"
] | null | null | Multi-agent evaluation aims at the assessment of an agent’s strategy on the basis of interaction with others. Typically, existing methods such as $\alpha$-rank and its approximation still require to exhaustively compare all pairs of joint strategies for an accurate ranking, which in practice is computationally expensiv... | [] | null | 263 | null | null |
v139/du21f | Learning Diverse-Structured Networks for Adversarial Robustness | https://proceedings.mlr.press/v139/du21f.html | [
"Xuefeng Du",
"Jingfeng Zhang",
"Bo Han",
"Tongliang Liu",
"Yu Rong",
"Gang Niu",
"Junzhou Huang",
"Masashi Sugiyama"
] | null | null | In adversarial training (AT), the main focus has been the objective and optimizer while the model has been less studied, so that the models being used are still those classic ones in standard training (ST). Classic network architectures (NAs) are generally worse than searched NA in ST, which should be the same in AT. I... | [] | null | 264 | 2102.01886 | title_snapshot |
v139/duan21a | Risk Bounds and Rademacher Complexity in Batch Reinforcement Learning | https://proceedings.mlr.press/v139/duan21a.html | [
"Yaqi Duan",
"Chi Jin",
"Zhiyuan Li"
] | null | null | This paper considers batch Reinforcement Learning (RL) with general value function approximation. Our study investigates the minimal assumptions to reliably estimate/minimize Bellman error, and characterizes the generalization performance by (local) Rademacher complexities of general function classes, which makes initi... | [] | null | 265 | 2103.13883 | title_snapshot |
v139/duan21b | Sawtooth Factorial Topic Embeddings Guided Gamma Belief Network | https://proceedings.mlr.press/v139/duan21b.html | [
"Zhibin Duan",
"Dongsheng Wang",
"Bo Chen",
"Chaojie Wang",
"Wenchao Chen",
"Yewen Li",
"Jie Ren",
"Mingyuan Zhou"
] | null | null | Hierarchical topic models such as the gamma belief network (GBN) have delivered promising results in mining multi-layer document representations and discovering interpretable topic taxonomies. However, they often assume in the prior that the topics at each layer are independently drawn from the Dirichlet distribution, ... | [] | null | 266 | 2107.02757 | title_snapshot |
v139/dutt21a | Exponential Reduction in Sample Complexity with Learning of Ising Model Dynamics | https://proceedings.mlr.press/v139/dutt21a.html | [
"Arkopal Dutt",
"Andrey Lokhov",
"Marc D Vuffray",
"Sidhant Misra"
] | null | null | The usual setting for learning the structure and parameters of a graphical model assumes the availability of independent samples produced from the corresponding multivariate probability distribution. However, for many models the mixing time of the respective Markov chain can be very large and i.i.d. samples may not be ... | [] | null | 267 | 2104.00995 | title_snapshot |
v139/ecoffet21a | Reinforcement Learning Under Moral Uncertainty | https://proceedings.mlr.press/v139/ecoffet21a.html | [
"Adrien Ecoffet",
"Joel Lehman"
] | null | null | An ambitious goal for machine learning is to create agents that behave ethically: The capacity to abide by human moral norms would greatly expand the context in which autonomous agents could be practically and safely deployed, e.g. fully autonomous vehicles will encounter charged moral decisions that complicate their d... | [] | null | 268 | 2006.04734 | title_snapshot |
v139/efroni21a | Confidence-Budget Matching for Sequential Budgeted Learning | https://proceedings.mlr.press/v139/efroni21a.html | [
"Yonathan Efroni",
"Nadav Merlis",
"Aadirupa Saha",
"Shie Mannor"
] | null | null | A core element in decision-making under uncertainty is the feedback on the quality of the performed actions. However, in many applications, such feedback is restricted. For example, in recommendation systems, repeatedly asking the user to provide feedback on the quality of recommendations will annoy them. In this work,... | [] | null | 269 | 2102.03400 | title_snapshot |
v139/eimer21a | Self-Paced Context Evaluation for Contextual Reinforcement Learning | https://proceedings.mlr.press/v139/eimer21a.html | [
"Theresa Eimer",
"André Biedenkapp",
"Frank Hutter",
"Marius Lindauer"
] | null | null | Reinforcement learning (RL) has made a lot of advances for solving a single problem in a given environment; but learning policies that generalize to unseen variations of a problem remains challenging. To improve sample efficiency for learning on such instances of a problem domain, we present Self-Paced Context Evaluati... | [] | null | 270 | 2106.05110 | title_snapshot |
v139/elesedy21a | Provably Strict Generalisation Benefit for Equivariant Models | https://proceedings.mlr.press/v139/elesedy21a.html | [
"Bryn Elesedy",
"Sheheryar Zaidi"
] | null | null | It is widely believed that engineering a model to be invariant/equivariant improves generalisation. Despite the growing popularity of this approach, a precise characterisation of the generalisation benefit is lacking. By considering the simplest case of linear models, this paper provides the first provably non-zero imp... | [] | null | 271 | 2102.10333 | title_snapshot |
v139/emami21a | Efficient Iterative Amortized Inference for Learning Symmetric and Disentangled Multi-Object Representations | https://proceedings.mlr.press/v139/emami21a.html | [
"Patrick Emami",
"Pan He",
"Sanjay Ranka",
"Anand Rangarajan"
] | null | null | Unsupervised multi-object representation learning depends on inductive biases to guide the discovery of object-centric representations that generalize. However, we observe that methods for learning these representations are either impractical due to long training times and large memory consumption or forego key inducti... | [] | null | 272 | 2106.03630 | title_snapshot |
v139/emami21b | Implicit Bias of Linear RNNs | https://proceedings.mlr.press/v139/emami21b.html | [
"Melikasadat Emami",
"Mojtaba Sahraee-Ardakan",
"Parthe Pandit",
"Sundeep Rangan",
"Alyson K Fletcher"
] | null | null | Contemporary wisdom based on empirical studies suggests that standard recurrent neural networks (RNNs) do not perform well on tasks requiring long-term memory. However, RNNs’ poor ability to capture long-term dependencies has not been fully understood. This paper provides a rigorous explanation of this property in the ... | [] | null | 273 | 2101.07833 | title_snapshot |
v139/ergen21a | Global Optimality Beyond Two Layers: Training Deep ReLU Networks via Convex Programs | https://proceedings.mlr.press/v139/ergen21a.html | [
"Tolga Ergen",
"Mert Pilanci"
] | null | null | Understanding the fundamental mechanism behind the success of deep neural networks is one of the key challenges in the modern machine learning literature. Despite numerous attempts, a solid theoretical analysis is yet to be developed. In this paper, we develop a novel unified framework to reveal a hidden regularization... | [] | null | 274 | 2110.05518 | title_snapshot |
v139/ergen21b | Revealing the Structure of Deep Neural Networks via Convex Duality | https://proceedings.mlr.press/v139/ergen21b.html | [
"Tolga Ergen",
"Mert Pilanci"
] | null | null | We study regularized deep neural networks (DNNs) and introduce a convex analytic framework to characterize the structure of the hidden layers. We show that a set of optimal hidden layer weights for a norm regularized DNN training problem can be explicitly found as the extreme points of a convex set. For the special cas... | [] | null | 275 | 2002.09773 | title_snapshot |
v139/ermolov21a | Whitening for Self-Supervised Representation Learning | https://proceedings.mlr.press/v139/ermolov21a.html | [
"Aleksandr Ermolov",
"Aliaksandr Siarohin",
"Enver Sangineto",
"Nicu Sebe"
] | null | null | Most of the current self-supervised representation learning (SSL) methods are based on the contrastive loss and the instance-discrimination task, where augmented versions of the same image instance ("positives") are contrasted with instances extracted from other images ("negatives"). For the learning to be effective, m... | [] | null | 276 | 2007.06346 | title_snapshot |
v139/errica21a | Graph Mixture Density Networks | https://proceedings.mlr.press/v139/errica21a.html | [
"Federico Errica",
"Davide Bacciu",
"Alessio Micheli"
] | null | null | We introduce the Graph Mixture Density Networks, a new family of machine learning models that can fit multimodal output distributions conditioned on graphs of arbitrary topology. By combining ideas from mixture models and graph representation learning, we address a broader class of challenging conditional density estim... | [] | null | 277 | 2012.03085 | title_snapshot |
v139/esfandiari21a | Cross-Gradient Aggregation for Decentralized Learning from Non-IID Data | https://proceedings.mlr.press/v139/esfandiari21a.html | [
"Yasaman Esfandiari",
"Sin Yong Tan",
"Zhanhong Jiang",
"Aditya Balu",
"Ethan Herron",
"Chinmay Hegde",
"Soumik Sarkar"
] | null | null | Decentralized learning enables a group of collaborative agents to learn models using a distributed dataset without the need for a central parameter server. Recently, decentralized learning algorithms have demonstrated state-of-the-art results on benchmark data sets, comparable with centralized algorithms. However, the ... | [] | null | 278 | 2103.02051 | title_snapshot |
v139/eustratiadis21a | Weight-covariance alignment for adversarially robust neural networks | https://proceedings.mlr.press/v139/eustratiadis21a.html | [
"Panagiotis Eustratiadis",
"Henry Gouk",
"Da Li",
"Timothy Hospedales"
] | null | null | Stochastic Neural Networks (SNNs) that inject noise into their hidden layers have recently been shown to achieve strong robustness against adversarial attacks. However, existing SNNs are usually heuristically motivated, and often rely on adversarial training, which is computationally costly. We propose a new SNN that a... | [] | null | 279 | 2010.08852 | title_snapshot |
v139/fabian21a | Data augmentation for deep learning based accelerated MRI reconstruction with limited data | https://proceedings.mlr.press/v139/fabian21a.html | [
"Zalan Fabian",
"Reinhard Heckel",
"Mahdi Soltanolkotabi"
] | null | null | Deep neural networks have emerged as very successful tools for image restoration and reconstruction tasks. These networks are often trained end-to-end to directly reconstruct an image from a noisy or corrupted measurement of that image. To achieve state-of-the-art performance, training on large and diverse sets of imag... | [] | null | 280 | 2106.14947 | title_snapshot |
v139/fan21a | Poisson-Randomised DirBN: Large Mutation is Needed in Dirichlet Belief Networks | https://proceedings.mlr.press/v139/fan21a.html | [
"Xuhui Fan",
"Bin Li",
"Yaqiong Li",
"Scott A. Sisson"
] | null | null | The Dirichlet Belief Network (DirBN) was recently proposed as a promising deep generative model to learn interpretable deep latent distributions for objects. However, its current representation capability is limited since its latent distributions across different layers is prone to form similar patterns and can thus ha... | [] | null | 281 | null | null |
v139/fan21b | Model-based Reinforcement Learning for Continuous Control with Posterior Sampling | https://proceedings.mlr.press/v139/fan21b.html | [
"Ying Fan",
"Yifei Ming"
] | null | null | Balancing exploration and exploitation is crucial in reinforcement learning (RL). In this paper, we study model-based posterior sampling for reinforcement learning (PSRL) in continuous state-action spaces theoretically and empirically. First, we show the first regret bound of PSRL in continuous spaces which is polynomi... | [] | null | 282 | 2012.09613 | title_snapshot |
v139/fan21c | SECANT: Self-Expert Cloning for Zero-Shot Generalization of Visual Policies | https://proceedings.mlr.press/v139/fan21c.html | [
"Linxi Fan",
"Guanzhi Wang",
"De-An Huang",
"Zhiding Yu",
"Li Fei-Fei",
"Yuke Zhu",
"Animashree Anandkumar"
] | null | null | Generalization has been a long-standing challenge for reinforcement learning (RL). Visual RL, in particular, can be easily distracted by irrelevant factors in high-dimensional observation space. In this work, we consider robust policy learning which targets zero-shot generalization to unseen visual environments with la... | [] | null | 283 | 2106.09678 | title_snapshot |
v139/fang21a | On Estimation in Latent Variable Models | https://proceedings.mlr.press/v139/fang21a.html | [
"Guanhua Fang",
"Ping Li"
] | null | null | Latent variable models have been playing a central role in statistics, econometrics, machine learning with applications to repeated observation study, panel data inference, user behavior analysis, etc. In many modern applications, the inference based on latent variable models involves one or several of the following fe... | [] | null | 284 | null | null |
v139/fang21b | On Variational Inference in Biclustering Models | https://proceedings.mlr.press/v139/fang21b.html | [
"Guanhua Fang",
"Ping Li"
] | null | null | Biclustering structures exist ubiquitously in data matrices and the biclustering problem was first formalized by John Hartigan (1972) to cluster rows and columns simultaneously. In this paper, we develop a theory for the estimation of general biclustering models, where the data is assumed to follow certain statistical ... | [] | null | 285 | null | null |
v139/fang21c | Learning Bounds for Open-Set Learning | https://proceedings.mlr.press/v139/fang21c.html | [
"Zhen Fang",
"Jie Lu",
"Anjin Liu",
"Feng Liu",
"Guangquan Zhang"
] | null | null | Traditional supervised learning aims to train a classifier in the closed-set world, where training and test samples share the same label space. In this paper, we target a more challenging and re_x0002_alistic setting: open-set learning (OSL), where there exist test samples from the classes that are unseen during traini... | [] | null | 286 | 2106.15792 | title_snapshot |
v139/fang21d | Streaming Bayesian Deep Tensor Factorization | https://proceedings.mlr.press/v139/fang21d.html | [
"Shikai Fang",
"Zheng Wang",
"Zhimeng Pan",
"Ji Liu",
"Shandian Zhe"
] | null | null | Despite the success of existing tensor factorization methods, most of them conduct a multilinear decomposition, and rarely exploit powerful modeling frameworks, like deep neural networks, to capture a variety of complicated interactions in data. More important, for highly expressive, deep factorization, we lack an effe... | [] | null | 287 | null | null |
v139/farahmand21a | PID Accelerated Value Iteration Algorithm | https://proceedings.mlr.press/v139/farahmand21a.html | [
"Amir-Massoud Farahmand",
"Mohammad Ghavamzadeh"
] | null | null | The convergence rate of Value Iteration (VI), a fundamental procedure in dynamic programming and reinforcement learning, for solving MDPs can be slow when the discount factor is close to one. We propose modifications to VI in order to potentially accelerate its convergence behaviour. The key insight is the realization ... | [] | null | 288 | null | null |
v139/farias21a | Near-Optimal Entrywise Anomaly Detection for Low-Rank Matrices with Sub-Exponential Noise | https://proceedings.mlr.press/v139/farias21a.html | [
"Vivek Farias",
"Andrew A Li",
"Tianyi Peng"
] | null | null | We study the problem of identifying anomalies in a low-rank matrix observed with sub-exponential noise, motivated by applications in retail and inventory management. State of the art approaches to anomaly detection in low-rank matrices apparently fall short, since they require that non-anomalous entries be observed wit... | [] | null | 289 | null | null |
v139/farina21a | Connecting Optimal Ex-Ante Collusion in Teams to Extensive-Form Correlation: Faster Algorithms and Positive Complexity Results | https://proceedings.mlr.press/v139/farina21a.html | [
"Gabriele Farina",
"Andrea Celli",
"Nicola Gatti",
"Tuomas Sandholm"
] | null | null | We focus on the problem of finding an optimal strategy for a team of players that faces an opponent in an imperfect-information zero-sum extensive-form game. Team members are not allowed to communicate during play but can coordinate before the game. In this setting, it is known that the best the team can do is sample a... | [] | null | 290 | 2009.10061 | title_judge |
v139/farnia21a | Train simultaneously, generalize better: Stability of gradient-based minimax learners | https://proceedings.mlr.press/v139/farnia21a.html | [
"Farzan Farnia",
"Asuman Ozdaglar"
] | null | null | The success of minimax learning problems of generative adversarial networks (GANs) has been observed to depend on the minimax optimization algorithm used for their training. This dependence is commonly attributed to the convergence speed and robustness properties of the underlying optimization algorithm. In this paper,... | [] | null | 291 | 2010.12561 | title_snapshot |
v139/fatras21a | Unbalanced minibatch Optimal Transport; applications to Domain Adaptation | https://proceedings.mlr.press/v139/fatras21a.html | [
"Kilian Fatras",
"Thibault Sejourne",
"Rémi Flamary",
"Nicolas Courty"
] | null | null | Optimal transport distances have found many applications in machine learning for their capacity to compare non-parametric probability distributions. Yet their algorithmic complexity generally prevents their direct use on large scale datasets. Among the possible strategies to alleviate this issue, practitioners can rely... | [] | null | 292 | 2103.03606 | title_snapshot |
v139/fei21a | Risk-Sensitive Reinforcement Learning with Function Approximation: A Debiasing Approach | https://proceedings.mlr.press/v139/fei21a.html | [
"Yingjie Fei",
"Zhuoran Yang",
"Zhaoran Wang"
] | null | null | We study function approximation for episodic reinforcement learning with entropic risk measure. We first propose an algorithm with linear function approximation. Compared to existing algorithms, which suffer from improper regularization and regression biases, this algorithm features debiasing transformations in backwar... | [] | null | 293 | null | null |
v139/feldman21a | Lossless Compression of Efficient Private Local Randomizers | https://proceedings.mlr.press/v139/feldman21a.html | [
"Vitaly Feldman",
"Kunal Talwar"
] | null | null | Locally Differentially Private (LDP) Reports are commonly used for collection of statistics and machine learning in the federated setting. In many cases the best known LDP algorithms require sending prohibitively large messages from the client device to the server (such as when constructing histograms over a large doma... | [] | null | 294 | 2102.12099 | title_snapshot |
v139/feng21a | Dimensionality Reduction for the Sum-of-Distances Metric | https://proceedings.mlr.press/v139/feng21a.html | [
"Zhili Feng",
"Praneeth Kacham",
"David Woodruff"
] | null | null | We give a dimensionality reduction procedure to approximate the sum of distances of a given set of $n$ points in $R^d$ to any “shape” that lies in a $k$-dimensional subspace. Here, by “shape” we mean any set of points in $R^d$. Our algorithm takes an input in the form of an $n \times d$ matrix $A$, where each row of $A... | [] | null | 295 | 1912.12003 | title_judge |
v139/feng21b | Reserve Price Optimization for First Price Auctions in Display Advertising | https://proceedings.mlr.press/v139/feng21b.html | [
"Zhe Feng",
"Sebastien Lahaie",
"Jon Schneider",
"Jinchao Ye"
] | null | null | The display advertising industry has recently transitioned from second- to first-price auctions as its primary mechanism for ad allocation and pricing. In light of this, publishers need to re-evaluate and optimize their auction parameters, notably reserve prices. In this paper, we propose a gradient-based algorithm to ... | [] | null | 296 | 2006.06519 | title_judge |
v139/feng21c | Uncertainty Principles of Encoding GANs | https://proceedings.mlr.press/v139/feng21c.html | [
"Ruili Feng",
"Zhouchen Lin",
"Jiapeng Zhu",
"Deli Zhao",
"Jingren Zhou",
"Zheng-Jun Zha"
] | null | null | The compelling synthesis results of Generative Adversarial Networks (GANs) demonstrate rich semantic knowledge in their latent codes. To obtain this knowledge for downstream applications, encoding GANs has been proposed to learn encoders, such that real world data can be encoded to latent codes, which can be fed to gen... | [] | null | 297 | null | null |
v139/feng21d | Pointwise Binary Classification with Pairwise Confidence Comparisons | https://proceedings.mlr.press/v139/feng21d.html | [
"Lei Feng",
"Senlin Shu",
"Nan Lu",
"Bo Han",
"Miao Xu",
"Gang Niu",
"Bo An",
"Masashi Sugiyama"
] | null | null | To alleviate the data requirement for training effective binary classifiers in binary classification, many weakly supervised learning settings have been proposed. Among them, some consider using pairwise but not pointwise labels, when pointwise labels are not accessible due to privacy, confidentiality, or security reas... | [] | null | 298 | 2010.01875 | title_snapshot |
v139/feng21e | Provably Correct Optimization and Exploration with Non-linear Policies | https://proceedings.mlr.press/v139/feng21e.html | [
"Fei Feng",
"Wotao Yin",
"Alekh Agarwal",
"Lin Yang"
] | null | null | Policy optimization methods remain a powerful workhorse in empirical Reinforcement Learning (RL), with a focus on neural policies that can easily reason over complex and continuous state and/or action spaces. Theoretical understanding of strategic exploration in policy-based methods with non-linear function approximati... | [] | null | 299 | 2103.11559 | title_snapshot |
v139/feng21f | KD3A: Unsupervised Multi-Source Decentralized Domain Adaptation via Knowledge Distillation | https://proceedings.mlr.press/v139/feng21f.html | [
"Haozhe Feng",
"Zhaoyang You",
"Minghao Chen",
"Tianye Zhang",
"Minfeng Zhu",
"Fei Wu",
"Chao Wu",
"Wei Chen"
] | null | null | Conventional unsupervised multi-source domain adaptation (UMDA) methods assume all source domains can be accessed directly. However, this assumption neglects the privacy-preserving policy, where all the data and computations must be kept decentralized. There exist three challenges in this scenario: (1) Minimizing the d... | [] | null | 300 | 2011.09757 | title_snapshot |
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