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v139/feng21g | Understanding Noise Injection in GANs | https://proceedings.mlr.press/v139/feng21g.html | [
"Ruili Feng",
"Deli Zhao",
"Zheng-Jun Zha"
] | null | null | Noise injection is an effective way of circumventing overfitting and enhancing generalization in machine learning, the rationale of which has been validated in deep learning as well. Recently, noise injection exhibits surprising effectiveness when generating high-fidelity images in Generative Adversarial Networks (GANs... | [] | null | 301 | null | null |
v139/fey21a | GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings | https://proceedings.mlr.press/v139/fey21a.html | [
"Matthias Fey",
"Jan E. Lenssen",
"Frank Weichert",
"Jure Leskovec"
] | null | null | We present GNNAutoScale (GAS), a framework for scaling arbitrary message-passing GNNs to large graphs. GAS prunes entire sub-trees of the computation graph by utilizing historical embeddings from prior training iterations, leading to constant GPU memory consumption in respect to input node size without dropping any dat... | [] | null | 302 | 2106.05609 | title_snapshot |
v139/filos21a | PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learning | https://proceedings.mlr.press/v139/filos21a.html | [
"Angelos Filos",
"Clare Lyle",
"Yarin Gal",
"Sergey Levine",
"Natasha Jaques",
"Gregory Farquhar"
] | null | null | We study reinforcement learning (RL) with no-reward demonstrations, a setting in which an RL agent has access to additional data from the interaction of other agents with the same environment. However, it has no access to the rewards or goals of these agents, and their objectives and levels of expertise may vary widely... | [] | null | 303 | 2102.12560 | title_snapshot |
v139/finzi21a | A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groups | https://proceedings.mlr.press/v139/finzi21a.html | [
"Marc Finzi",
"Max Welling",
"Andrew Gordon Wilson"
] | null | null | Symmetries and equivariance are fundamental to the generalization of neural networks on domains such as images, graphs, and point clouds. Existing work has primarily focused on a small number of groups, such as the translation, rotation, and permutation groups. In this work we provide a completely general algorithm for... | [] | null | 304 | 2104.09459 | title_snapshot |
v139/fisch21a | Few-Shot Conformal Prediction with Auxiliary Tasks | https://proceedings.mlr.press/v139/fisch21a.html | [
"Adam Fisch",
"Tal Schuster",
"Tommi Jaakkola",
"Dr.Regina Barzilay"
] | null | null | We develop a novel approach to conformal prediction when the target task has limited data available for training. Conformal prediction identifies a small set of promising output candidates in place of a single prediction, with guarantees that the set contains the correct answer with high probability. When training data... | [] | null | 305 | 2102.08898 | title_snapshot |
v139/fischer21a | Scalable Certified Segmentation via Randomized Smoothing | https://proceedings.mlr.press/v139/fischer21a.html | [
"Marc Fischer",
"Maximilian Baader",
"Martin Vechev"
] | null | null | We present a new certification method for image and point cloud segmentation based on randomized smoothing. The method leverages a novel scalable algorithm for prediction and certification that correctly accounts for multiple testing, necessary for ensuring statistical guarantees. The key to our approach is reliance on... | [] | null | 306 | 2107.00228 | title_snapshot |
v139/fischer21b | What’s in the Box? Exploring the Inner Life of Neural Networks with Robust Rules | https://proceedings.mlr.press/v139/fischer21b.html | [
"Jonas Fischer",
"Anna Olah",
"Jilles Vreeken"
] | null | null | We propose a novel method for exploring how neurons within neural networks interact. In particular, we consider activation values of a network for given data, and propose to mine noise-robust rules of the form X {\rightarrow} Y , where X and Y are sets of neurons in different layers. We identify the best set of rules b... | [] | null | 307 | null | null |
v139/flaspohler21a | Online Learning with Optimism and Delay | https://proceedings.mlr.press/v139/flaspohler21a.html | [
"Genevieve E Flaspohler",
"Francesco Orabona",
"Judah Cohen",
"Soukayna Mouatadid",
"Miruna Oprescu",
"Paulo Orenstein",
"Lester Mackey"
] | null | null | Inspired by the demands of real-time climate and weather forecasting, we develop optimistic online learning algorithms that require no parameter tuning and have optimal regret guarantees under delayed feedback. Our algorithms—DORM, DORM+, and AdaHedgeD—arise from a novel reduction of delayed online learning to optimist... | [] | null | 308 | 2106.06885 | title_snapshot |
v139/fontaine21a | Online A-Optimal Design and Active Linear Regression | https://proceedings.mlr.press/v139/fontaine21a.html | [
"Xavier Fontaine",
"Pierre Perrault",
"Michal Valko",
"Vianney Perchet"
] | null | null | We consider in this paper the problem of optimal experiment design where a decision maker can choose which points to sample to obtain an estimate $\hat{\beta}$ of the hidden parameter $\beta^{\star}$ of an underlying linear model. The key challenge of this work lies in the heteroscedasticity assumption that we make, me... | [] | null | 309 | 1906.08509 | title_snapshot |
v139/foster21a | Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design | https://proceedings.mlr.press/v139/foster21a.html | [
"Adam Foster",
"Desi R Ivanova",
"Ilyas Malik",
"Tom Rainforth"
] | null | null | We introduce Deep Adaptive Design (DAD), a method for amortizing the cost of adaptive Bayesian experimental design that allows experiments to be run in real-time. Traditional sequential Bayesian optimal experimental design approaches require substantial computation at each stage of the experiment. This makes them unsui... | [] | null | 310 | 2103.02438 | title_snapshot |
v139/fotakis21a | Efficient Online Learning for Dynamic k-Clustering | https://proceedings.mlr.press/v139/fotakis21a.html | [
"Dimitris Fotakis",
"Georgios Piliouras",
"Stratis Skoulakis"
] | null | null | In this work, we study dynamic clustering problems from the perspective of online learning. We consider an online learning problem, called \textit{Dynamic $k$-Clustering}, in which $k$ centers are maintained in a metric space over time (centers may change positions) such as a dynamically changing set of $r$ clients is ... | [] | null | 311 | 2106.04336 | title_snapshot |
v139/fraboni21a | Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning | https://proceedings.mlr.press/v139/fraboni21a.html | [
"Yann Fraboni",
"Richard Vidal",
"Laetitia Kameni",
"Marco Lorenzi"
] | null | null | This work addresses the problem of optimizing communications between server and clients in federated learning (FL). Current sampling approaches in FL are either biased, or non optimal in terms of server-clients communications and training stability. To overcome this issue, we introduce clustered sampling for clients se... | [] | null | 312 | 2105.05883 | title_snapshot |
v139/frei21a | Agnostic Learning of Halfspaces with Gradient Descent via Soft Margins | https://proceedings.mlr.press/v139/frei21a.html | [
"Spencer Frei",
"Yuan Cao",
"Quanquan Gu"
] | null | null | We analyze the properties of gradient descent on convex surrogates for the zero-one loss for the agnostic learning of halfspaces. We show that when a quantity we refer to as the \textit{soft margin} is well-behaved—a condition satisfied by log-concave isotropic distributions among others—minimizers of convex surrogates... | [] | null | 313 | 2010.00539 | title_snapshot |
v139/frei21b | Provable Generalization of SGD-trained Neural Networks of Any Width in the Presence of Adversarial Label Noise | https://proceedings.mlr.press/v139/frei21b.html | [
"Spencer Frei",
"Yuan Cao",
"Quanquan Gu"
] | null | null | We consider a one-hidden-layer leaky ReLU network of arbitrary width trained by stochastic gradient descent (SGD) following an arbitrary initialization. We prove that SGD produces neural networks that have classification accuracy competitive with that of the best halfspace over the distribution for a broad class of dis... | [] | null | 314 | 2101.01152 | title_snapshot |
v139/freidling21a | Post-selection inference with HSIC-Lasso | https://proceedings.mlr.press/v139/freidling21a.html | [
"Tobias Freidling",
"Benjamin Poignard",
"Héctor Climente-González",
"Makoto Yamada"
] | null | null | Detecting influential features in non-linear and/or high-dimensional data is a challenging and increasingly important task in machine learning. Variable selection methods have thus been gaining much attention as well as post-selection inference. Indeed, the selected features can be significantly flawed when the selecti... | [] | null | 315 | 2010.15659 | title_snapshot |
v139/frerix21a | Variational Data Assimilation with a Learned Inverse Observation Operator | https://proceedings.mlr.press/v139/frerix21a.html | [
"Thomas Frerix",
"Dmitrii Kochkov",
"Jamie Smith",
"Daniel Cremers",
"Michael Brenner",
"Stephan Hoyer"
] | null | null | Variational data assimilation optimizes for an initial state of a dynamical system such that its evolution fits observational data. The physical model can subsequently be evolved into the future to make predictions. This principle is a cornerstone of large scale forecasting applications such as numerical weather predic... | [] | null | 316 | 2102.11192 | title_snapshot |
v139/frohlich21a | Bayesian Quadrature on Riemannian Data Manifolds | https://proceedings.mlr.press/v139/frohlich21a.html | [
"Christian Fröhlich",
"Alexandra Gessner",
"Philipp Hennig",
"Bernhard Schölkopf",
"Georgios Arvanitidis"
] | null | null | Riemannian manifolds provide a principled way to model nonlinear geometric structure inherent in data. A Riemannian metric on said manifolds determines geometry-aware shortest paths and provides the means to define statistical models accordingly. However, these operations are typically computationally demanding. To eas... | [] | null | 317 | 2102.06645 | title_snapshot |
v139/fu21a | Learn-to-Share: A Hardware-friendly Transfer Learning Framework Exploiting Computation and Parameter Sharing | https://proceedings.mlr.press/v139/fu21a.html | [
"Cheng Fu",
"Hanxian Huang",
"Xinyun Chen",
"Yuandong Tian",
"Jishen Zhao"
] | null | null | Task-specific fine-tuning on pre-trained transformers has achieved performance breakthroughs in multiple NLP tasks. Yet, as both computation and parameter size grows linearly with the number of sub-tasks, it is increasingly difficult to adopt such methods to the real world due to unrealistic memory and computation over... | [] | null | 318 | null | null |
v139/fu21b | Learning Task Informed Abstractions | https://proceedings.mlr.press/v139/fu21b.html | [
"Xiang Fu",
"Ge Yang",
"Pulkit Agrawal",
"Tommi Jaakkola"
] | null | null | Current model-based reinforcement learning methods struggle when operating from complex visual scenes due to their inability to prioritize task-relevant features. To mitigate this problem, we propose learning Task Informed Abstractions (TIA) that explicitly separates reward-correlated visual features from distractors. ... | [] | null | 319 | 2106.15612 | title_snapshot |
v139/fu21c | Double-Win Quant: Aggressively Winning Robustness of Quantized Deep Neural Networks via Random Precision Training and Inference | https://proceedings.mlr.press/v139/fu21c.html | [
"Yonggan Fu",
"Qixuan Yu",
"Meng Li",
"Vikas Chandra",
"Yingyan Lin"
] | null | null | Quantization is promising in enabling powerful yet complex deep neural networks (DNNs) to be deployed into resource constrained platforms. However, quantized DNNs are vulnerable to adversarial attacks unless being equipped with sophisticated techniques, leading to a dilemma of struggling between DNNs’ efficiency and ro... | [] | null | 320 | null | null |
v139/fu21d | Auto-NBA: Efficient and Effective Search Over the Joint Space of Networks, Bitwidths, and Accelerators | https://proceedings.mlr.press/v139/fu21d.html | [
"Yonggan Fu",
"Yongan Zhang",
"Yang Zhang",
"David Cox",
"Yingyan Lin"
] | null | null | While maximizing deep neural networks’ (DNNs’) acceleration efficiency requires a joint search/design of three different yet highly coupled aspects, including the networks, bitwidths, and accelerators, the challenges associated with such a joint search have not yet been fully understood and addressed. The key challenge... | [] | null | 321 | 2106.06575 | title_snapshot |
v139/fujimoto21a | A Deep Reinforcement Learning Approach to Marginalized Importance Sampling with the Successor Representation | https://proceedings.mlr.press/v139/fujimoto21a.html | [
"Scott Fujimoto",
"David Meger",
"Doina Precup"
] | null | null | Marginalized importance sampling (MIS), which measures the density ratio between the state-action occupancy of a target policy and that of a sampling distribution, is a promising approach for off-policy evaluation. However, current state-of-the-art MIS methods rely on complex optimization tricks and succeed mostly on s... | [] | null | 322 | 2106.06854 | title_snapshot |
v139/fumero21a | Learning disentangled representations via product manifold projection | https://proceedings.mlr.press/v139/fumero21a.html | [
"Marco Fumero",
"Luca Cosmo",
"Simone Melzi",
"Emanuele Rodola"
] | null | null | We propose a novel approach to disentangle the generative factors of variation underlying a given set of observations. Our method builds upon the idea that the (unknown) low-dimensional manifold underlying the data space can be explicitly modeled as a product of submanifolds. This definition of disentanglement gives ri... | [] | null | 323 | 2103.01638 | title_snapshot |
v139/furuta21a | Policy Information Capacity: Information-Theoretic Measure for Task Complexity in Deep Reinforcement Learning | https://proceedings.mlr.press/v139/furuta21a.html | [
"Hiroki Furuta",
"Tatsuya Matsushima",
"Tadashi Kozuno",
"Yutaka Matsuo",
"Sergey Levine",
"Ofir Nachum",
"Shixiang Shane Gu"
] | null | null | Progress in deep reinforcement learning (RL) research is largely enabled by benchmark task environments. However, analyzing the nature of those environments is often overlooked. In particular, we still do not have agreeable ways to measure the difficulty or solvability of a task, given that each has fundamentally diffe... | [] | null | 324 | 2103.12726 | title_snapshot |
v139/gao21a | An Information-Geometric Distance on the Space of Tasks | https://proceedings.mlr.press/v139/gao21a.html | [
"Yansong Gao",
"Pratik Chaudhari"
] | null | null | This paper prescribes a distance between learning tasks modeled as joint distributions on data and labels. Using tools in information geometry, the distance is defined to be the length of the shortest weight trajectory on a Riemannian manifold as a classifier is fitted on an interpolated task. The interpolated task evo... | [] | null | 325 | 2011.00613 | title_snapshot |
v139/gao21b | Maximum Mean Discrepancy Test is Aware of Adversarial Attacks | https://proceedings.mlr.press/v139/gao21b.html | [
"Ruize Gao",
"Feng Liu",
"Jingfeng Zhang",
"Bo Han",
"Tongliang Liu",
"Gang Niu",
"Masashi Sugiyama"
] | null | null | The maximum mean discrepancy (MMD) test could in principle detect any distributional discrepancy between two datasets. However, it has been shown that the MMD test is unaware of adversarial attacks–the MMD test failed to detect the discrepancy between natural data and adversarial data. Given this phenomenon, we raise a... | [] | null | 326 | 2010.11415 | title_snapshot |
v139/gao21c | Unsupervised Co-part Segmentation through Assembly | https://proceedings.mlr.press/v139/gao21c.html | [
"Qingzhe Gao",
"Bin Wang",
"Libin Liu",
"Baoquan Chen"
] | null | null | Co-part segmentation is an important problem in computer vision for its rich applications. We propose an unsupervised learning approach for co-part segmentation from images. For the training stage, we leverage motion information embedded in videos and explicitly extract latent representations to segment meaningful obje... | [] | null | 327 | 2106.05897 | title_snapshot |
v139/gao21d | Discriminative Complementary-Label Learning with Weighted Loss | https://proceedings.mlr.press/v139/gao21d.html | [
"Yi Gao",
"Min-Ling Zhang"
] | null | null | Complementary-label learning (CLL) deals with the weak supervision scenario where each training instance is associated with one \emph{complementary} label, which specifies the class label that the instance does \emph{not} belong to. Given the training instance ${\bm x}$, existing CLL approaches aim at modeling the \emp... | [] | null | 328 | null | null |
v139/garg21a | RATT: Leveraging Unlabeled Data to Guarantee Generalization | https://proceedings.mlr.press/v139/garg21a.html | [
"Saurabh Garg",
"Sivaraman Balakrishnan",
"Zico Kolter",
"Zachary Lipton"
] | null | null | To assess generalization, machine learning scientists typically either (i) bound the generalization gap and then (after training) plug in the empirical risk to obtain a bound on the true risk; or (ii) validate empirically on holdout data. However, (i) typically yields vacuous guarantees for overparameterized models; an... | [] | null | 329 | 2105.00303 | title_snapshot |
v139/garg21b | On Proximal Policy Optimization’s Heavy-tailed Gradients | https://proceedings.mlr.press/v139/garg21b.html | [
"Saurabh Garg",
"Joshua Zhanson",
"Emilio Parisotto",
"Adarsh Prasad",
"Zico Kolter",
"Zachary Lipton",
"Sivaraman Balakrishnan",
"Ruslan Salakhutdinov",
"Pradeep Ravikumar"
] | null | null | Modern policy gradient algorithms such as Proximal Policy Optimization (PPO) rely on an arsenal of heuristics, including loss clipping and gradient clipping, to ensure successful learning. These heuristics are reminiscent of techniques from robust statistics, commonly used for estimation in outlier-rich ("heavy-tailed"... | [] | null | 330 | 2102.10264 | title_snapshot |
v139/garreau21a | What does LIME really see in images? | https://proceedings.mlr.press/v139/garreau21a.html | [
"Damien Garreau",
"Dina Mardaoui"
] | null | null | The performance of modern algorithms on certain computer vision tasks such as object recognition is now close to that of humans. This success was achieved at the price of complicated architectures depending on millions of parameters and it has become quite challenging to understand how particular predictions are made. ... | [] | null | 331 | 2102.06307 | title_snapshot |
v139/gauthier21a | Parametric Graph for Unimodal Ranking Bandit | https://proceedings.mlr.press/v139/gauthier21a.html | [
"Camille-Sovanneary Gauthier",
"Romaric Gaudel",
"Elisa Fromont",
"Boammani Aser Lompo"
] | null | null | We tackle the online ranking problem of assigning $L$ items to $K$ positions on a web page in order to maximize the number of user clicks. We propose an original algorithm, easy to implement and with strong theoretical guarantees to tackle this problem in the Position-Based Model (PBM) setting, well suited for applicat... | [] | null | 332 | null | null |
v139/geerts21a | Let’s Agree to Degree: Comparing Graph Convolutional Networks in the Message-Passing Framework | https://proceedings.mlr.press/v139/geerts21a.html | [
"Floris Geerts",
"Filip Mazowiecki",
"Guillermo Perez"
] | null | null | In this paper we cast neural networks defined on graphs as message-passing neural networks (MPNNs) to study the distinguishing power of different classes of such models. We are interested in when certain architectures are able to tell vertices apart based on the feature labels given as input with the graph. We consider... | [] | null | 333 | 2004.02593 | title_snapshot |
v139/geffner21a | On the difficulty of unbiased alpha divergence minimization | https://proceedings.mlr.press/v139/geffner21a.html | [
"Tomas Geffner",
"Justin Domke"
] | null | null | Several approximate inference algorithms have been proposed to minimize an alpha-divergence between an approximating distribution and a target distribution. Many of these algorithms introduce bias, the magnitude of which becomes problematic in high dimensions. Other algorithms are unbiased. These often seem to suffer f... | [] | null | 334 | 2010.09541 | title_snapshot |
v139/gentzel21a | How and Why to Use Experimental Data to Evaluate Methods for Observational Causal Inference | https://proceedings.mlr.press/v139/gentzel21a.html | [
"Amanda M Gentzel",
"Purva Pruthi",
"David Jensen"
] | null | null | Methods that infer causal dependence from observational data are central to many areas of science, including medicine, economics, and the social sciences. A variety of theoretical properties of these methods have been proven, but empirical evaluation remains a challenge, largely due to the lack of observational data se... | [] | null | 335 | 2010.03051 | title_snapshot |
v139/ghalme21a | Strategic Classification in the Dark | https://proceedings.mlr.press/v139/ghalme21a.html | [
"Ganesh Ghalme",
"Vineet Nair",
"Itay Eilat",
"Inbal Talgam-Cohen",
"Nir Rosenfeld"
] | null | null | Strategic classification studies the interaction between a classification rule and the strategic agents it governs. Agents respond by manipulating their features, under the assumption that the classifier is known. However, in many real-life scenarios of high-stake classification (e.g., credit scoring), the classifier i... | [] | null | 336 | 2102.11592 | title_snapshot |
v139/ghasemipour21a | EMaQ: Expected-Max Q-Learning Operator for Simple Yet Effective Offline and Online RL | https://proceedings.mlr.press/v139/ghasemipour21a.html | [
"Seyed Kamyar Seyed Ghasemipour",
"Dale Schuurmans",
"Shixiang Shane Gu"
] | null | null | Off-policy reinforcement learning (RL) holds the promise of sample-efficient learning of decision-making policies by leveraging past experience. However, in the offline RL setting – where a fixed collection of interactions are provided and no further interactions are allowed – it has been shown that standard off-policy... | [] | null | 337 | 2007.11091 | title_snapshot |
v139/ghazi21a | Differentially Private Aggregation in the Shuffle Model: Almost Central Accuracy in Almost a Single Message | https://proceedings.mlr.press/v139/ghazi21a.html | [
"Badih Ghazi",
"Ravi Kumar",
"Pasin Manurangsi",
"Rasmus Pagh",
"Amer Sinha"
] | null | null | The shuffle model of differential privacy has attracted attention in the literature due to it being a middle ground between the well-studied central and local models. In this work, we study the problem of summing (aggregating) real numbers or integers, a basic primitive in numerous machine learning tasks, in the shuffl... | [] | null | 338 | 2109.13158 | title_snapshot |
v139/ghuge21a | The Power of Adaptivity for Stochastic Submodular Cover | https://proceedings.mlr.press/v139/ghuge21a.html | [
"Rohan Ghuge",
"Anupam Gupta",
"Viswanath Nagarajan"
] | null | null | In the stochastic submodular cover problem, the goal is to select a subset of stochastic items of minimum expected cost to cover a submodular function. Solutions in this setting correspond to a sequential decision process that selects items one by one “adaptively” (depending on prior observations). While such adaptive ... | [] | null | 339 | 2106.16115 | title_snapshot |
v139/gillenwater21a | Differentially Private Quantiles | https://proceedings.mlr.press/v139/gillenwater21a.html | [
"Jennifer Gillenwater",
"Matthew Joseph",
"Alex Kulesza"
] | null | null | Quantiles are often used for summarizing and understanding data. If that data is sensitive, it may be necessary to compute quantiles in a way that is differentially private, providing theoretical guarantees that the result does not reveal private information. However, when multiple quantiles are needed, existing differ... | [] | null | 340 | 2102.08244 | title_snapshot |
v139/gluch21a | Query Complexity of Adversarial Attacks | https://proceedings.mlr.press/v139/gluch21a.html | [
"Grzegorz Gluch",
"Rüdiger Urbanke"
] | null | null | There are two main attack models considered in the adversarial robustness literature: black-box and white-box. We consider these threat models as two ends of a fine-grained spectrum, indexed by the number of queries the adversary can ask. Using this point of view we investigate how many queries the adversary needs to m... | [] | null | 341 | 2010.01039 | title_snapshot |
v139/gogianu21a | Spectral Normalisation for Deep Reinforcement Learning: An Optimisation Perspective | https://proceedings.mlr.press/v139/gogianu21a.html | [
"Florin Gogianu",
"Tudor Berariu",
"Mihaela C Rosca",
"Claudia Clopath",
"Lucian Busoniu",
"Razvan Pascanu"
] | null | null | Most of the recent deep reinforcement learning advances take an RL-centric perspective and focus on refinements of the training objective. We diverge from this view and show we can recover the performance of these developments not by changing the objective, but by regularising the value-function estimator. Constraining... | [] | null | 342 | 2105.05246 | title_snapshot |
v139/golany21a | 12-Lead ECG Reconstruction via Koopman Operators | https://proceedings.mlr.press/v139/golany21a.html | [
"Tomer Golany",
"Kira Radinsky",
"Daniel Freedman",
"Saar Minha"
] | null | null | 32% of all global deaths in the world are caused by cardiovascular diseases. Early detection, especially for patients with ischemia or cardiac arrhythmia, is crucial. To reduce the time between symptoms onset and treatment, wearable ECG sensors were developed to allow for the recording of the full 12-lead ECG signal at... | [] | null | 343 | null | null |
v139/gondal21a | Function Contrastive Learning of Transferable Meta-Representations | https://proceedings.mlr.press/v139/gondal21a.html | [
"Muhammad Waleed Gondal",
"Shruti Joshi",
"Nasim Rahaman",
"Stefan Bauer",
"Manuel Wuthrich",
"Bernhard Schölkopf"
] | null | null | Meta-learning algorithms adapt quickly to new tasks that are drawn from the same task distribution as the training tasks. The mechanism leading to fast adaptation is the conditioning of a downstream predictive model on the inferred representation of the task’s underlying data generative process, or \emph{function}. Thi... | [] | null | 344 | 2010.07093 | title_snapshot |
v139/gong21a | Active Slices for Sliced Stein Discrepancy | https://proceedings.mlr.press/v139/gong21a.html | [
"Wenbo Gong",
"Kaibo Zhang",
"Yingzhen Li",
"Jose Miguel Hernandez-Lobato"
] | null | null | Sliced Stein discrepancy (SSD) and its kernelized variants have demonstrated promising successes in goodness-of-fit tests and model learning in high dimensions. Despite the theoretical elegance, their empirical performance depends crucially on the search of the optimal slicing directions to discriminate between two dis... | [] | null | 345 | 2102.03159 | title_snapshot |
v139/gorantla21a | On the Problem of Underranking in Group-Fair Ranking | https://proceedings.mlr.press/v139/gorantla21a.html | [
"Sruthi Gorantla",
"Amit Deshpande",
"Anand Louis"
] | null | null | Bias in ranking systems, especially among the top ranks, can worsen social and economic inequalities, polarize opinions, and reinforce stereotypes. On the other hand, a bias correction for minority groups can cause more harm if perceived as favoring group-fair outcomes over meritocracy. Most group-fair ranking algorith... | [] | null | 346 | 2010.06986 | title_snapshot |
v139/gorbunov21a | MARINA: Faster Non-Convex Distributed Learning with Compression | https://proceedings.mlr.press/v139/gorbunov21a.html | [
"Eduard Gorbunov",
"Konstantin P. Burlachenko",
"Zhize Li",
"Peter Richtarik"
] | null | null | We develop and analyze MARINA: a new communication efficient method for non-convex distributed learning over heterogeneous datasets. MARINA employs a novel communication compression strategy based on the compression of gradient differences that is reminiscent of but different from the strategy employed in the DIANA met... | [] | null | 347 | 2102.07845 | title_snapshot |
v139/gosgens21a | Systematic Analysis of Cluster Similarity Indices: How to Validate Validation Measures | https://proceedings.mlr.press/v139/gosgens21a.html | [
"Martijn M Gösgens",
"Alexey Tikhonov",
"Liudmila Prokhorenkova"
] | null | null | Many cluster similarity indices are used to evaluate clustering algorithms, and choosing the best one for a particular task remains an open problem. We demonstrate that this problem is crucial: there are many disagreements among the indices, these disagreements do affect which algorithms are preferred in applications, ... | [] | null | 348 | 1911.04773 | title_snapshot |
v139/goyal21a | Revisiting Point Cloud Shape Classification with a Simple and Effective Baseline | https://proceedings.mlr.press/v139/goyal21a.html | [
"Ankit Goyal",
"Hei Law",
"Bowei Liu",
"Alejandro Newell",
"Jia Deng"
] | null | null | Processing point cloud data is an important component of many real-world systems. As such, a wide variety of point-based approaches have been proposed, reporting steady benchmark improvements over time. We study the key ingredients of this progress and uncover two critical results. First, we find that auxiliary factors... | [] | null | 349 | 2106.05304 | title_snapshot |
v139/graf21a | Dissecting Supervised Contrastive Learning | https://proceedings.mlr.press/v139/graf21a.html | [
"Florian Graf",
"Christoph Hofer",
"Marc Niethammer",
"Roland Kwitt"
] | null | null | Minimizing cross-entropy over the softmax scores of a linear map composed with a high-capacity encoder is arguably the most popular choice for training neural networks on supervised learning tasks. However, recent works show that one can directly optimize the encoder instead, to obtain equally (or even more) discrimina... | [] | null | 350 | 2102.08817 | title_snapshot |
v139/grathwohl21a | Oops I Took A Gradient: Scalable Sampling for Discrete Distributions | https://proceedings.mlr.press/v139/grathwohl21a.html | [
"Will Grathwohl",
"Kevin Swersky",
"Milad Hashemi",
"David Duvenaud",
"Chris Maddison"
] | null | null | We propose a general and scalable approximate sampling strategy for probabilistic models with discrete variables. Our approach uses gradients of the likelihood function with respect to its discrete inputs to propose updates in a Metropolis-Hastings sampler. We show empirically that this approach outperforms generic sam... | [] | null | 351 | 2102.04509 | title_snapshot |
v139/greenberg21a | Detecting Rewards Deterioration in Episodic Reinforcement Learning | https://proceedings.mlr.press/v139/greenberg21a.html | [
"Ido Greenberg",
"Shie Mannor"
] | null | null | In many RL applications, once training ends, it is vital to detect any deterioration in the agent performance as soon as possible. Furthermore, it often has to be done without modifying the policy and under minimal assumptions regarding the environment. In this paper, we address this problem by focusing directly on the... | [] | null | 352 | 2010.11660 | title_snapshot |
v139/gu21a | Crystallization Learning with the Delaunay Triangulation | https://proceedings.mlr.press/v139/gu21a.html | [
"Jiaqi Gu",
"Guosheng Yin"
] | null | null | Based on the Delaunay triangulation, we propose the crystallization learning to estimate the conditional expectation function in the framework of nonparametric regression. By conducting the crystallization search for the Delaunay simplices closest to the target point in a hierarchical way, the crystallization learning ... | [] | null | 353 | null | null |
v139/guan21a | AutoAttend: Automated Attention Representation Search | https://proceedings.mlr.press/v139/guan21a.html | [
"Chaoyu Guan",
"Xin Wang",
"Wenwu Zhu"
] | null | null | Self-attention mechanisms have been widely adopted in many machine learning areas, including Natural Language Processing (NLP) and Graph Representation Learning (GRL), etc. However, existing works heavily rely on hand-crafted design to obtain customized attention mechanisms. In this paper, we automate Key, Query and Va... | [] | null | 354 | null | null |
v139/gultchin21a | Operationalizing Complex Causes: A Pragmatic View of Mediation | https://proceedings.mlr.press/v139/gultchin21a.html | [
"Limor Gultchin",
"David Watson",
"Matt Kusner",
"Ricardo Silva"
] | null | null | We examine the problem of causal response estimation for complex objects (e.g., text, images, genomics). In this setting, classical \emph{atomic} interventions are often not available (e.g., changes to characters, pixels, DNA base-pairs). Instead, we only have access to indirect or \emph{crude} interventions (e.g., enr... | [] | null | 355 | 2106.05074 | title_snapshot |
v139/guminov21a | On a Combination of Alternating Minimization and Nesterov’s Momentum | https://proceedings.mlr.press/v139/guminov21a.html | [
"Sergey Guminov",
"Pavel Dvurechensky",
"Nazarii Tupitsa",
"Alexander Gasnikov"
] | null | null | Alternating minimization (AM) procedures are practically efficient in many applications for solving convex and non-convex optimization problems. On the other hand, Nesterov’s accelerated gradient is theoretically optimal first-order method for convex optimization. In this paper we combine AM and Nesterov’s acceleration... | [] | null | 356 | 1906.03622 | title_snapshot |
v139/guo21a | Decentralized Single-Timescale Actor-Critic on Zero-Sum Two-Player Stochastic Games | https://proceedings.mlr.press/v139/guo21a.html | [
"Hongyi Guo",
"Zuyue Fu",
"Zhuoran Yang",
"Zhaoran Wang"
] | null | null | We study the global convergence and global optimality of the actor-critic algorithm applied for the zero-sum two-player stochastic games in a decentralized manner. We focus on the single-timescale setting where the critic is updated by applying the Bellman operator only once and the actor is updated by policy gradient ... | [] | null | 357 | null | null |
v139/guo21b | Adversarial Policy Learning in Two-player Competitive Games | https://proceedings.mlr.press/v139/guo21b.html | [
"Wenbo Guo",
"Xian Wu",
"Sui Huang",
"Xinyu Xing"
] | null | null | In a two-player deep reinforcement learning task, recent work shows an attacker could learn an adversarial policy that triggers a target agent to perform poorly and even react in an undesired way. However, its efficacy heavily relies upon the zero-sum assumption made in the two-player game. In this work, we propose a n... | [] | null | 358 | null | null |
v139/guo21c | Soft then Hard: Rethinking the Quantization in Neural Image Compression | https://proceedings.mlr.press/v139/guo21c.html | [
"Zongyu Guo",
"Zhizheng Zhang",
"Runsen Feng",
"Zhibo Chen"
] | null | null | Quantization is one of the core components in lossy image compression. For neural image compression, end-to-end optimization requires differentiable approximations of quantization, which can generally be grouped into three categories: additive uniform noise, straight-through estimator and soft-to-hard annealing. Traini... | [] | null | 359 | 2104.05168 | title_snapshot |
v139/gupta21a | UneVEn: Universal Value Exploration for Multi-Agent Reinforcement Learning | https://proceedings.mlr.press/v139/gupta21a.html | [
"Tarun Gupta",
"Anuj Mahajan",
"Bei Peng",
"Wendelin Boehmer",
"Shimon Whiteson"
] | null | null | VDN and QMIX are two popular value-based algorithms for cooperative MARL that learn a centralized action value function as a monotonic mixing of per-agent utilities. While this enables easy decentralization of the learned policy, the restricted joint action value function can prevent them from solving tasks that requir... | [] | null | 360 | 2010.02974 | title_snapshot |
v139/gupta21b | Distribution-Free Calibration Guarantees for Histogram Binning without Sample Splitting | https://proceedings.mlr.press/v139/gupta21b.html | [
"Chirag Gupta",
"Aaditya Ramdas"
] | null | null | We prove calibration guarantees for the popular histogram binning (also called uniform-mass binning) method of Zadrozny and Elkan (2001). Histogram binning has displayed strong practical performance, but theoretical guarantees have only been shown for sample split versions that avoid ’double dipping’ the data. We demon... | [] | null | 361 | 2105.04656 | title_snapshot |
v139/gupta21c | Correcting Exposure Bias for Link Recommendation | https://proceedings.mlr.press/v139/gupta21c.html | [
"Shantanu Gupta",
"Hao Wang",
"Zachary Lipton",
"Yuyang Wang"
] | null | null | Link prediction methods are frequently applied in recommender systems, e.g., to suggest citations for academic papers or friends in social networks. However, exposure bias can arise when users are systematically underexposed to certain relevant items. For example, in citation networks, authors might be more likely to e... | [] | null | 362 | 2106.07041 | title_snapshot |
v139/gurbuzbalaban21a | The Heavy-Tail Phenomenon in SGD | https://proceedings.mlr.press/v139/gurbuzbalaban21a.html | [
"Mert Gurbuzbalaban",
"Umut Simsekli",
"Lingjiong Zhu"
] | null | null | In recent years, various notions of capacity and complexity have been proposed for characterizing the generalization properties of stochastic gradient descent (SGD) in deep learning. Some of the popular notions that correlate well with the performance on unseen data are (i) the ‘flatness’ of the local minimum found by ... | [] | null | 363 | 2006.04740 | title_snapshot |
v139/gurel21a | Knowledge Enhanced Machine Learning Pipeline against Diverse Adversarial Attacks | https://proceedings.mlr.press/v139/gurel21a.html | [
"Nezihe Merve Gürel",
"Xiangyu Qi",
"Luka Rimanic",
"Ce Zhang",
"Bo Li"
] | null | null | Despite the great successes achieved by deep neural networks (DNNs), recent studies show that they are vulnerable against adversarial examples, which aim to mislead DNNs by adding small adversarial perturbations. Several defenses have been proposed against such attacks, while many of them have been adaptively attacked.... | [] | null | 364 | 2106.06235 | title_snapshot |
v139/gyorgy21a | Adapting to Delays and Data in Adversarial Multi-Armed Bandits | https://proceedings.mlr.press/v139/gyorgy21a.html | [
"Andras Gyorgy",
"Pooria Joulani"
] | null | null | We consider the adversarial multi-armed bandit problem under delayed feedback. We analyze variants of the Exp3 algorithm that tune their step size using only information (about the losses and delays) available at the time of the decisions, and obtain regret guarantees that adapt to the observed (rather than the worst-c... | [] | null | 365 | 2010.06022 | title_snapshot |
v139/hafez-kolahi21a | Rate-Distortion Analysis of Minimum Excess Risk in Bayesian Learning | https://proceedings.mlr.press/v139/hafez-kolahi21a.html | [
"Hassan Hafez-Kolahi",
"Behrad Moniri",
"Shohreh Kasaei",
"Mahdieh Soleymani Baghshah"
] | null | null | In parametric Bayesian learning, a prior is assumed on the parameter $W$ which determines the distribution of samples. In this setting, Minimum Excess Risk (MER) is defined as the difference between the minimum expected loss achievable when learning from data and the minimum expected loss that could be achieved if $W$ ... | [] | null | 366 | 2105.04180 | title_snapshot |
v139/hallak21a | Regret Minimization in Stochastic Non-Convex Learning via a Proximal-Gradient Approach | https://proceedings.mlr.press/v139/hallak21a.html | [
"Nadav Hallak",
"Panayotis Mertikopoulos",
"Volkan Cevher"
] | null | null | This paper develops a methodology for regret minimization with stochastic first-order oracle feedback in online, constrained, non-smooth, non-convex problems. In this setting, the minimization of external regret is beyond reach for first-order methods, and there are no gradient-based algorithmic frameworks capable of p... | [] | null | 367 | 2010.06250 | title_snapshot |
v139/han21a | Diversity Actor-Critic: Sample-Aware Entropy Regularization for Sample-Efficient Exploration | https://proceedings.mlr.press/v139/han21a.html | [
"Seungyul Han",
"Youngchul Sung"
] | null | null | In this paper, sample-aware policy entropy regularization is proposed to enhance the conventional policy entropy regularization for better exploration. Exploiting the sample distribution obtainable from the replay buffer, the proposed sample-aware entropy regularization maximizes the entropy of the weighted sum of the ... | [] | null | 368 | 2006.01419 | title_snapshot |
v139/han21b | Adversarial Combinatorial Bandits with General Non-linear Reward Functions | https://proceedings.mlr.press/v139/han21b.html | [
"Yanjun Han",
"Yining Wang",
"Xi Chen"
] | null | null | In this paper we study the adversarial combinatorial bandit with a known non-linear reward function, extending existing work on adversarial linear combinatorial bandit. {The adversarial combinatorial bandit with general non-linear reward is an important open problem in bandit literature, and it is still unclear whether... | [] | null | 369 | 2101.01301 | title_snapshot |
v139/hang21a | A Collective Learning Framework to Boost GNN Expressiveness for Node Classification | https://proceedings.mlr.press/v139/hang21a.html | [
"Mengyue Hang",
"Jennifer Neville",
"Bruno Ribeiro"
] | null | null | Collective Inference (CI) is a procedure designed to boost weak relational classifiers, specially for node classification tasks. Graph Neural Networks (GNNs) are strong classifiers that have been used with great success. Unfortunately, most existing practical GNNs are not most-expressive (universal). Thus, it is an ope... | [] | null | 370 | 2003.12169 | title_judge |
v139/hanjie21a | Grounding Language to Entities and Dynamics for Generalization in Reinforcement Learning | https://proceedings.mlr.press/v139/hanjie21a.html | [
"Austin W. Hanjie",
"Victor Y Zhong",
"Karthik Narasimhan"
] | null | null | We investigate the use of natural language to drive the generalization of control policies and introduce the new multi-task environment Messenger with free-form text manuals describing the environment dynamics. Unlike previous work, Messenger does not assume prior knowledge connecting text and state observations {—} th... | [] | null | 371 | 2101.07393 | title_snapshot |
v139/hao21a | Sparse Feature Selection Makes Batch Reinforcement Learning More Sample Efficient | https://proceedings.mlr.press/v139/hao21a.html | [
"Botao Hao",
"Yaqi Duan",
"Tor Lattimore",
"Csaba Szepesvari",
"Mengdi Wang"
] | null | null | This paper provides a statistical analysis of high-dimensional batch reinforcement learning (RL) using sparse linear function approximation. When there is a large number of candidate features, our result sheds light on the fact that sparsity-aware methods can make batch RL more sample efficient. We first consider the o... | [] | null | 372 | 2011.04019 | title_snapshot |
v139/hao21b | Bootstrapping Fitted Q-Evaluation for Off-Policy Inference | https://proceedings.mlr.press/v139/hao21b.html | [
"Botao Hao",
"Xiang Ji",
"Yaqi Duan",
"Hao Lu",
"Csaba Szepesvari",
"Mengdi Wang"
] | null | null | Bootstrapping provides a flexible and effective approach for assessing the quality of batch reinforcement learning, yet its theoretical properties are poorly understood. In this paper, we study the use of bootstrapping in off-policy evaluation (OPE), and in particular, we focus on the fitted Q-evaluation (FQE) that is ... | [] | null | 373 | 2102.03607 | title_snapshot |
v139/hao21c | Compressed Maximum Likelihood | https://proceedings.mlr.press/v139/hao21c.html | [
"Yi Hao",
"Alon Orlitsky"
] | null | null | Maximum likelihood (ML) is one of the most fundamental and general statistical estimation techniques. Inspired by recent advances in estimating distribution functionals, we propose $\textit{compressed maximum likelihood}$ (CML) that applies ML to the compressed samples. We then show that CML is sample-efficient for sev... | [] | null | 374 | null | null |
v139/hartford21a | Valid Causal Inference with (Some) Invalid Instruments | https://proceedings.mlr.press/v139/hartford21a.html | [
"Jason S Hartford",
"Victor Veitch",
"Dhanya Sridhar",
"Kevin Leyton-Brown"
] | null | null | Instrumental variable methods provide a powerful approach to estimating causal effects in the presence of unobserved confounding. But a key challenge when applying them is the reliance on untestable "exclusion" assumptions that rule out any relationship between the instrument variable and the response that is not media... | [] | null | 375 | 2006.11386 | title_snapshot |
v139/hashimoto21a | Model Performance Scaling with Multiple Data Sources | https://proceedings.mlr.press/v139/hashimoto21a.html | [
"Tatsunori Hashimoto"
] | null | null | Real-world machine learning systems are often trained using a mix of data sources with varying cost and quality. Understanding how the size and composition of a training dataset affect model performance is critical for advancing our understanding of generalization, as well as designing more effective data collection po... | [] | null | 376 | null | null |
v139/havtorn21a | Hierarchical VAEs Know What They Don’t Know | https://proceedings.mlr.press/v139/havtorn21a.html | [
"Jakob D. Havtorn",
"Jes Frellsen",
"Søren Hauberg",
"Lars Maaløe"
] | null | null | Deep generative models have been demonstrated as state-of-the-art density estimators. Yet, recent work has found that they often assign a higher likelihood to data from outside the training distribution. This seemingly paradoxical behavior has caused concerns over the quality of the attained density estimates. In the c... | [] | null | 377 | 2102.08248 | title_snapshot |
v139/hayase21a | SPECTRE: defending against backdoor attacks using robust statistics | https://proceedings.mlr.press/v139/hayase21a.html | [
"Jonathan Hayase",
"Weihao Kong",
"Raghav Somani",
"Sewoong Oh"
] | null | null | Modern machine learning increasingly requires training on a large collection of data from multiple sources, not all of which can be trusted. A particularly frightening scenario is when a small fraction of corrupted data changes the behavior of the trained model when triggered by an attacker-specified watermark. Such a ... | [] | null | 378 | 2104.11315 | title_snapshot |
v139/hazan21a | Boosting for Online Convex Optimization | https://proceedings.mlr.press/v139/hazan21a.html | [
"Elad Hazan",
"Karan Singh"
] | null | null | We consider the decision-making framework of online convex optimization with a very large number of experts. This setting is ubiquitous in contextual and reinforcement learning problems, where the size of the policy class renders enumeration and search within the policy class infeasible. Instead, we consider generalizi... | [] | null | 379 | 2102.09305 | title_snapshot |
v139/he21a | PipeTransformer: Automated Elastic Pipelining for Distributed Training of Large-scale Models | https://proceedings.mlr.press/v139/he21a.html | [
"Chaoyang He",
"Shen Li",
"Mahdi Soltanolkotabi",
"Salman Avestimehr"
] | null | null | The size of Transformer models is growing at an unprecedented rate. It has taken less than one year to reach trillion-level parameters since the release of GPT-3 (175B). Training such models requires both substantial engineering efforts and enormous computing resources, which are luxuries most research teams cannot aff... | [] | null | 380 | 2102.03161 | title_judge |
v139/he21b | SoundDet: Polyphonic Moving Sound Event Detection and Localization from Raw Waveform | https://proceedings.mlr.press/v139/he21b.html | [
"Yuhang He",
"Niki Trigoni",
"Andrew Markham"
] | null | null | We present a new framework SoundDet, which is an end-to-end trainable and light-weight framework, for polyphonic moving sound event detection and localization. Prior methods typically approach this problem by preprocessing raw waveform into time-frequency representations, which is more amenable to process with well-est... | [] | null | 381 | 2106.06969 | title_snapshot |
v139/he21c | Logarithmic Regret for Reinforcement Learning with Linear Function Approximation | https://proceedings.mlr.press/v139/he21c.html | [
"Jiafan He",
"Dongruo Zhou",
"Quanquan Gu"
] | null | null | Reinforcement learning (RL) with linear function approximation has received increasing attention recently. However, existing work has focused on obtaining $\sqrt{T}$-type regret bound, where $T$ is the number of interactions with the MDP. In this paper, we show that logarithmic regret is attainable under two recently p... | [] | null | 382 | 2011.11566 | title_snapshot |
v139/heidari21a | Finding Relevant Information via a Discrete Fourier Expansion | https://proceedings.mlr.press/v139/heidari21a.html | [
"Mohsen Heidari",
"Jithin Sreedharan",
"Gil I Shamir",
"Wojciech Szpankowski"
] | null | null | A fundamental obstacle in learning information from data is the presence of nonlinear redundancies and dependencies in it. To address this, we propose a Fourier-based approach to extract relevant information in the supervised setting. We first develop a novel Fourier expansion for functions of correlated binary random ... | [] | null | 383 | null | null |
v139/heliou21a | Zeroth-Order Non-Convex Learning via Hierarchical Dual Averaging | https://proceedings.mlr.press/v139/heliou21a.html | [
"Amélie Héliou",
"Matthieu Martin",
"Panayotis Mertikopoulos",
"Thibaud Rahier"
] | null | null | We propose a hierarchical version of dual averaging for zeroth-order online non-convex optimization {–} i.e., learning processes where, at each stage, the optimizer is facing an unknown non-convex loss function and only receives the incurred loss as feedback. The proposed class of policies relies on the construction of... | [] | null | 384 | 2109.05829 | title_snapshot |
v139/henderson21a | Improving Molecular Graph Neural Network Explainability with Orthonormalization and Induced Sparsity | https://proceedings.mlr.press/v139/henderson21a.html | [
"Ryan Henderson",
"Djork-Arné Clevert",
"Floriane Montanari"
] | null | null | Rationalizing which parts of a molecule drive the predictions of a molecular graph convolutional neural network (GCNN) can be difficult. To help, we propose two simple regularization techniques to apply during the training of GCNNs: Batch Representation Orthonormalization (BRO) and Gini regularization. BRO, inspired by... | [] | null | 385 | 2105.04854 | title_snapshot |
v139/hessel21a | Muesli: Combining Improvements in Policy Optimization | https://proceedings.mlr.press/v139/hessel21a.html | [
"Matteo Hessel",
"Ivo Danihelka",
"Fabio Viola",
"Arthur Guez",
"Simon Schmitt",
"Laurent Sifre",
"Theophane Weber",
"David Silver",
"Hado Van Hasselt"
] | null | null | We propose a novel policy update that combines regularized policy optimization with model learning as an auxiliary loss. The update (henceforth Muesli) matches MuZero’s state-of-the-art performance on Atari. Notably, Muesli does so without using deep search: it acts directly with a policy network and has computation sp... | [] | null | 386 | 2104.06159 | title_snapshot |
v139/hilgard21a | Learning Representations by Humans, for Humans | https://proceedings.mlr.press/v139/hilgard21a.html | [
"Sophie Hilgard",
"Nir Rosenfeld",
"Mahzarin R Banaji",
"Jack Cao",
"David Parkes"
] | null | null | When machine predictors can achieve higher performance than the human decision-makers they support, improving the performance of human decision-makers is often conflated with improving machine accuracy. Here we propose a framework to directly support human decision-making, in which the role of machines is to reframe pr... | [] | null | 387 | 1905.12686 | title_snapshot |
v139/hiranandani21a | Optimizing Black-box Metrics with Iterative Example Weighting | https://proceedings.mlr.press/v139/hiranandani21a.html | [
"Gaurush Hiranandani",
"Jatin Mathur",
"Harikrishna Narasimhan",
"Mahdi Milani Fard",
"Sanmi Koyejo"
] | null | null | We consider learning to optimize a classification metric defined by a black-box function of the confusion matrix. Such black-box learning settings are ubiquitous, for example, when the learner only has query access to the metric of interest, or in noisy-label and domain adaptation applications where the learner must ev... | [] | null | 388 | 2102.09492 | title_snapshot |
v139/hirsch21a | Trees with Attention for Set Prediction Tasks | https://proceedings.mlr.press/v139/hirsch21a.html | [
"Roy Hirsch",
"Ran Gilad-Bachrach"
] | null | null | In many machine learning applications, each record represents a set of items. For example, when making predictions from medical records, the medications prescribed to a patient are a set whose size is not fixed and whose order is arbitrary. However, most machine learning algorithms are not designed to handle set struct... | [] | null | 389 | null | null |
v139/hodgkinson21a | Multiplicative Noise and Heavy Tails in Stochastic Optimization | https://proceedings.mlr.press/v139/hodgkinson21a.html | [
"Liam Hodgkinson",
"Michael Mahoney"
] | null | null | Although stochastic optimization is central to modern machine learning, the precise mechanisms underlying its success, and in particular, the precise role of the stochasticity, still remain unclear. Modeling stochastic optimization algorithms as discrete random recurrence relations, we show that multiplicative noise, a... | [] | null | 390 | 2006.06293 | title_snapshot |
v139/hoedt21a | MC-LSTM: Mass-Conserving LSTM | https://proceedings.mlr.press/v139/hoedt21a.html | [
"Pieter-Jan Hoedt",
"Frederik Kratzert",
"Daniel Klotz",
"Christina Halmich",
"Markus Holzleitner",
"Grey S Nearing",
"Sepp Hochreiter",
"Guenter Klambauer"
] | null | null | The success of Convolutional Neural Networks (CNNs) in computer vision is mainly driven by their strong inductive bias, which is strong enough to allow CNNs to solve vision-related tasks with random weights, meaning without learning. Similarly, Long Short-Term Memory (LSTM) has a strong inductive bias towards storing i... | [] | null | 391 | 2101.05186 | title_snapshot |
v139/hoiem21a | Learning Curves for Analysis of Deep Networks | https://proceedings.mlr.press/v139/hoiem21a.html | [
"Derek Hoiem",
"Tanmay Gupta",
"Zhizhong Li",
"Michal Shlapentokh-Rothman"
] | null | null | Learning curves model a classifier’s test error as a function of the number of training samples. Prior works show that learning curves can be used to select model parameters and extrapolate performance. We investigate how to use learning curves to evaluate design choices, such as pretraining, architecture, and data aug... | [] | null | 392 | 2010.11029 | title_snapshot |
v139/holderrieth21a | Equivariant Learning of Stochastic Fields: Gaussian Processes and Steerable Conditional Neural Processes | https://proceedings.mlr.press/v139/holderrieth21a.html | [
"Peter Holderrieth",
"Michael J Hutchinson",
"Yee Whye Teh"
] | null | null | Motivated by objects such as electric fields or fluid streams, we study the problem of learning stochastic fields, i.e. stochastic processes whose samples are fields like those occurring in physics and engineering. Considering general transformations such as rotations and reflections, we show that spatial invariance of... | [] | null | 393 | 2011.12916 | title_snapshot |
v139/hong21a | Latent Programmer: Discrete Latent Codes for Program Synthesis | https://proceedings.mlr.press/v139/hong21a.html | [
"Joey Hong",
"David Dohan",
"Rishabh Singh",
"Charles Sutton",
"Manzil Zaheer"
] | null | null | A key problem in program synthesis is searching over the large space of possible programs. Human programmers might decide the high-level structure of the desired program before thinking about the details; motivated by this intuition, we consider two-level search for program synthesis, in which the synthesizer first gen... | [] | null | 394 | 2012.00377 | title_snapshot |
v139/hong21b | Chebyshev Polynomial Codes: Task Entanglement-based Coding for Distributed Matrix Multiplication | https://proceedings.mlr.press/v139/hong21b.html | [
"Sangwoo Hong",
"Heecheol Yang",
"Youngseok Yoon",
"Taehyun Cho",
"Jungwoo Lee"
] | null | null | Distributed computing has been a prominent solution to efficiently process massive datasets in parallel. However, the existence of stragglers is one of the major concerns that slows down the overall speed of distributed computing. To deal with this problem, we consider a distributed matrix multiplication scenario where... | [] | null | 395 | null | null |
v139/hosseini21a | Federated Learning of User Verification Models Without Sharing Embeddings | https://proceedings.mlr.press/v139/hosseini21a.html | [
"Hossein Hosseini",
"Hyunsin Park",
"Sungrack Yun",
"Christos Louizos",
"Joseph Soriaga",
"Max Welling"
] | null | null | We consider the problem of training User Verification (UV) models in federated setup, where each user has access to the data of only one class and user embeddings cannot be shared with the server or other users. To address this problem, we propose Federated User Verification (FedUV), a framework in which users jointly ... | [] | null | 396 | 2104.08776 | title_snapshot |
v139/hsieh21a | The Limits of Min-Max Optimization Algorithms: Convergence to Spurious Non-Critical Sets | https://proceedings.mlr.press/v139/hsieh21a.html | [
"Ya-Ping Hsieh",
"Panayotis Mertikopoulos",
"Volkan Cevher"
] | null | null | Compared to minimization, the min-max optimization in machine learning applications is considerably more convoluted because of the existence of cycles and similar phenomena. Such oscillatory behaviors are well-understood in the convex-concave regime, and many algorithms are known to overcome them. In this paper, we go ... | [] | null | 397 | 2006.09065 | title_snapshot |
v139/hu21a | Near-Optimal Representation Learning for Linear Bandits and Linear RL | https://proceedings.mlr.press/v139/hu21a.html | [
"Jiachen Hu",
"Xiaoyu Chen",
"Chi Jin",
"Lihong Li",
"Liwei Wang"
] | null | null | This paper studies representation learning for multi-task linear bandits and multi-task episodic RL with linear value function approximation. We first consider the setting where we play $M$ linear bandits with dimension $d$ concurrently, and these bandits share a common $k$-dimensional linear representation so that $k\... | [] | null | 398 | 2102.04132 | title_snapshot |
v139/hu21b | On the Random Conjugate Kernel and Neural Tangent Kernel | https://proceedings.mlr.press/v139/hu21b.html | [
"Zhengmian Hu",
"Heng Huang"
] | null | null | We investigate the distributions of Conjugate Kernel (CK) and Neural Tangent Kernel (NTK) for ReLU networks with random initialization. We derive the precise distributions and moments of the diagonal elements of these kernels. For a feedforward network, these values converge in law to a log-normal distribution when the... | [] | null | 399 | null | null |
v139/hu21c | Off-Belief Learning | https://proceedings.mlr.press/v139/hu21c.html | [
"Hengyuan Hu",
"Adam Lerer",
"Brandon Cui",
"Luis Pineda",
"Noam Brown",
"Jakob Foerster"
] | null | null | The standard problem setting in Dec-POMDPs is self-play, where the goal is to find a set of policies that play optimally together. Policies learned through self-play may adopt arbitrary conventions and implicitly rely on multi-step reasoning based on fragile assumptions about other agents’ actions and thus fail when pa... | [] | null | 400 | 2103.04000 | title_snapshot |
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