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Selective Dyna-Style Planning Under Limited Model Capacity | https://proceedings.mlr.press/v119/abbas20a.html | [
"Zaheer Abbas",
"Samuel Sokota",
"Erin Talvitie",
"Martha White"
] | null | null | In model-based reinforcement learning, planning with an imperfect model of the environment has the potential to harm learning progress. But even when a model is imperfect, it may still contain information that is useful for planning. In this paper, we investigate the idea of using an imperfect model selectively. The ag... | [] | null | 1 | 2007.02418 | title_snapshot | [
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A distributional view on multi-objective policy optimization | https://proceedings.mlr.press/v119/abdolmaleki20a.html | [
"Abbas Abdolmaleki",
"Sandy Huang",
"Leonard Hasenclever",
"Michael Neunert",
"Francis Song",
"Martina Zambelli",
"Murilo Martins",
"Nicolas Heess",
"Raia Hadsell",
"Martin Riedmiller"
] | null | null | Many real-world problems require trading off multiple competing objectives. However, these objectives are often in different units and/or scales, which can make it challenging for practitioners to express numerical preferences over objectives in their native units. In this paper we propose a novel algorithm for multi-o... | [] | null | 2 | 2005.07513 | title_snapshot | [
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Efficient Optimistic Exploration in Linear-Quadratic Regulators via Lagrangian Relaxation | https://proceedings.mlr.press/v119/abeille20a.html | [
"Marc Abeille",
"Alessandro Lazaric"
] | null | null | We study the exploration-exploitation dilemma in the linear quadratic regulator (LQR) setting. Inspired by the extended value iteration algorithm used in optimistic algorithms for finite MDPs, we propose to relax the optimistic optimization of \ofulq and cast it into a constrained \emph{extended} LQR problem, where an ... | [] | null | 3 | 2007.06482 | title_snapshot | [
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Super-efficiency of automatic differentiation for functions defined as a minimum | https://proceedings.mlr.press/v119/ablin20a.html | [
"Pierre Ablin",
"Gabriel Peyré",
"Thomas Moreau"
] | null | null | In min-min optimization or max-min optimization, one has to compute the gradient of a function defined as a minimum. In most cases, the minimum has no closed-form, and an approximation is obtained via an iterative algorithm. There are two usual ways of estimating the gradient of the function: using either an analytic f... | [] | null | 4 | 2002.03722 | title_snapshot | [
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A Geometric Approach to Archetypal Analysis via Sparse Projections | https://proceedings.mlr.press/v119/abrol20a.html | [
"Vinayak Abrol",
"Pulkit Sharma"
] | null | null | Archetypal analysis (AA) aims to extract patterns using self-expressive decomposition of data as convex combinations of extremal points (on the convex hull) of the data. This work presents a computationally efficient greedy AA (GAA) algorithm. GAA leverages the underlying geometry of AA, is scalable to larger datasets,... | [] | null | 5 | null | null | [
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Context Aware Local Differential Privacy | https://proceedings.mlr.press/v119/acharya20a.html | [
"Jayadev Acharya",
"Kallista Bonawitz",
"Peter Kairouz",
"Daniel Ramage",
"Ziteng Sun"
] | null | null | Local differential privacy (LDP) is a strong notion of privacy that often leads to a significant drop in utility. The original definition of LDP assumes that all the elements in the data domain are equally sensitive. However, in many real-life applications, some elements are more sensitive than others. We propose a con... | [] | null | 6 | 1911.00038 | title_snapshot | [
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Efficient Intervention Design for Causal Discovery with Latents | https://proceedings.mlr.press/v119/addanki20a.html | [
"Raghavendra Addanki",
"Shiva Kasiviswanathan",
"Andrew Mcgregor",
"Cameron Musco"
] | null | null | We consider recovering a causal graph in presence of latent variables, where we seek to minimize the cost of interventions used in the recovery process. We consider two intervention cost models: (1) a linear cost model where the cost of an intervention on a subset of variables has a linear form, and (2) an identity cos... | [] | null | 7 | 2005.11736 | title_snapshot | [
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The Neural Tangent Kernel in High Dimensions: Triple Descent and a Multi-Scale Theory of Generalization | https://proceedings.mlr.press/v119/adlam20a.html | [
"Ben Adlam",
"Jeffrey Pennington"
] | null | null | Modern deep learning models employ considerably more parameters than required to fit the training data. Whereas conventional statistical wisdom suggests such models should drastically overfit, in practice these models generalize remarkably well. An emerging paradigm for describing this unexpected behavior is in terms o... | [] | null | 8 | 2008.06786 | title_snapshot | [
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Rank Aggregation from Pairwise Comparisons in the Presence of Adversarial Corruptions | https://proceedings.mlr.press/v119/agarwal20a.html | [
"Arpit Agarwal",
"Shivani Agarwal",
"Sanjeev Khanna",
"Prathamesh Patil"
] | null | null | Rank aggregation from pairwise preferences has widespread applications in recommendation systems and information retrieval. Given the enormous economic and societal impact of these applications, and the consequent incentives for malicious players to manipulate ranking outcomes in their favor, an important challenge is ... | [] | null | 9 | null | null | [
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Boosting for Control of Dynamical Systems | https://proceedings.mlr.press/v119/agarwal20b.html | [
"Naman Agarwal",
"Nataly Brukhim",
"Elad Hazan",
"Zhou Lu"
] | null | null | We study the question of how to aggregate controllers for dynamical systems in order to improve their performance. To this end, we propose a framework of boosting for online control. Our main result is an efficient boosting algorithm that combines weak controllers into a provably more accurate one. Empirical evaluation... | [] | null | 10 | 1906.08720 | title_snapshot | [
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An Optimistic Perspective on Offline Reinforcement Learning | https://proceedings.mlr.press/v119/agarwal20c.html | [
"Rishabh Agarwal",
"Dale Schuurmans",
"Mohammad Norouzi"
] | null | null | Off-policy reinforcement learning (RL) using a fixed offline dataset of logged interactions is an important consideration in real world applications. This paper studies offline RL using the DQN replay dataset comprising the entire replay experience of a DQN agent on 60 Atari 2600 games. We demonstrate that recent off-p... | [] | null | 11 | 1907.04543 | title_snapshot | [
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Optimal Bounds between f-Divergences and Integral Probability Metrics | https://proceedings.mlr.press/v119/agrawal20a.html | [
"Rohit Agrawal",
"Thibaut Horel"
] | null | null | The families of f-divergences (e.g. the Kullback-Leibler divergence) and Integral Probability Metrics (e.g. total variation distance or maximum mean discrepancies) are commonly used in optimization and estimation. In this work, we systematically study the relationship between these two families from the perspective of ... | [] | null | 12 | 2006.05973 | title_snapshot | [
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LazyIter: A Fast Algorithm for Counting Markov Equivalent DAGs and Designing Experiments | https://proceedings.mlr.press/v119/ahmaditeshnizi20a.html | [
"Ali Ahmaditeshnizi",
"Saber Salehkaleybar",
"Negar Kiyavash"
] | null | null | The causal relationships among a set of random variables are commonly represented by a Directed Acyclic Graph (DAG), where there is a directed edge from variable $X$ to variable $Y$ if $X$ is a direct cause of $Y$. From the purely observational data, the true causal graph can be identified up to a Markov Equivalence Cl... | [] | null | 13 | 2006.09670 | title_snapshot | [
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Learning What to Defer for Maximum Independent Sets | https://proceedings.mlr.press/v119/ahn20a.html | [
"Sungsoo Ahn",
"Younggyo Seo",
"Jinwoo Shin"
] | null | null | Designing efficient algorithms for combinatorial optimization appears ubiquitously in various scientific fields. Recently, deep reinforcement learning (DRL) frameworks have gained considerable attention as a new approach: they can automate the design of a solver while relying less on sophisticated domain knowledge of t... | [] | null | 14 | 2006.09607 | title_snapshot | [
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Invariant Risk Minimization Games | https://proceedings.mlr.press/v119/ahuja20a.html | [
"Kartik Ahuja",
"Karthikeyan Shanmugam",
"Kush Varshney",
"Amit Dhurandhar"
] | null | null | The standard risk minimization paradigm of machine learning is brittle when operating in environments whose test distributions are different from the training distribution due to spurious correlations. Training on data from many environments and finding invariant predictors reduces the effect of spurious features by co... | [] | null | 15 | 2002.04692 | title_snapshot | [
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Why bigger is not always better: on finite and infinite neural networks | https://proceedings.mlr.press/v119/aitchison20a.html | [
"Laurence Aitchison"
] | null | null | Recent work has argued that neural networks can be understood theoretically by taking the number of channels to infinity, at which point the outputs become Gaussian process (GP) distributed. However, we note that infinite Bayesian neural networks lack a key facet of the behaviour of real neural networks: the fixed kern... | [] | null | 16 | 1910.08013 | title_snapshot | [
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Discriminative Jackknife: Quantifying Uncertainty in Deep Learning via Higher-Order Influence Functions | https://proceedings.mlr.press/v119/alaa20a.html | [
"Ahmed Alaa",
"Mihaela Van Der Schaar"
] | null | null | Deep learning models achieve high predictive accuracy across a broad spectrum of tasks, but rigorously quantifying their predictive uncertainty remains challenging. Usable estimates of predictive uncertainty should (1) cover the true prediction targets with high probability, and (2) discriminate between high- and low c... | [] | null | 17 | 2007.13481 | title_snapshot | [
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Frequentist Uncertainty in Recurrent Neural Networks via Blockwise Influence Functions | https://proceedings.mlr.press/v119/alaa20b.html | [
"Ahmed Alaa",
"Mihaela Van Der Schaar"
] | null | null | Recurrent neural networks (RNNs) are instrumental in modelling sequential and time-series data. Yet, when using RNNs to inform decision-making, predictions by themselves are not sufficient {—} we also need estimates of predictive uncertainty. Existing approaches for uncertainty quantification in RNNs are based predomin... | [] | null | 18 | 2006.13707 | title_snapshot | [
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Random extrapolation for primal-dual coordinate descent | https://proceedings.mlr.press/v119/alacaoglu20a.html | [
"Ahmet Alacaoglu",
"Olivier Fercoq",
"Volkan Cevher"
] | null | null | We introduce a randomly extrapolated primal-dual coordinate descent method that adapts to sparsity of the data matrix and the favorable structures of the objective function. Our method updates only a subset of primal and dual variables with sparse data, and it uses large step sizes with dense data, retaining the benefi... | [] | null | 19 | 2007.06528 | title_snapshot | [
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A new regret analysis for Adam-type algorithms | https://proceedings.mlr.press/v119/alacaoglu20b.html | [
"Ahmet Alacaoglu",
"Yura Malitsky",
"Panayotis Mertikopoulos",
"Volkan Cevher"
] | null | null | In this paper, we focus on a theory-practice gap for Adam and its variants (AMSGrad, AdamNC, etc.). In practice, these algorithms are used with a constant first-order moment parameter $\beta_{1}$ (typically between $0.9$ and $0.99$). In theory, regret guarantees for online convex optimization require a rapidly decaying... | [] | null | 20 | 2003.09729 | title_snapshot | [
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Restarted Bayesian Online Change-point Detector achieves Optimal Detection Delay | https://proceedings.mlr.press/v119/alami20a.html | [
"Reda Alami",
"Odalric Maillard",
"Raphael Feraud"
] | null | null | we consider the problem of sequential change-point detection where both the change-points and the distributions before and after the change are assumed to be unknown. For this problem of primary importance in statistical and sequential learning theory, we derive a variant of the Bayesian Online Change Point Detector p... | [] | null | 21 | null | null | [
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Maximum Likelihood with Bias-Corrected Calibration is Hard-To-Beat at Label Shift Adaptation | https://proceedings.mlr.press/v119/alexandari20a.html | [
"Amr Alexandari",
"Anshul Kundaje",
"Avanti Shrikumar"
] | null | null | Label shift refers to the phenomenon where the prior class probability p(y) changes between the training and test distributions, while the conditional probability p(x|y) stays fixed. Label shift arises in settings like medical diagnosis, where a classifier trained to predict disease given symptoms must be adapted to sc... | [] | null | 22 | 1901.06852 | title_snapshot | [
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The Implicit Regularization of Stochastic Gradient Flow for Least Squares | https://proceedings.mlr.press/v119/ali20a.html | [
"Alnur Ali",
"Edgar Dobriban",
"Ryan Tibshirani"
] | null | null | We study the implicit regularization of mini-batch stochastic gradient descent, when applied to the fundamental problem of least squares regression. We leverage a continuous-time stochastic differential equation having the same moments as stochastic gradient descent, which we call stochastic gradient flow. We give a bo... | [] | null | 23 | 2003.07802 | title_snapshot | [
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Structural Language Models of Code | https://proceedings.mlr.press/v119/alon20a.html | [
"Uri Alon",
"Roy Sadaka",
"Omer Levy",
"Eran Yahav"
] | null | null | We address the problem of any-code completion - generating a missing piece of source code in a given program without any restriction on the vocabulary or structure. We introduce a new approach to any-code completion that leverages the strict syntax of programming languages to model a code snippet as a tree - structural... | [] | null | 24 | 1910.00577 | title_snapshot | [
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LowFER: Low-rank Bilinear Pooling for Link Prediction | https://proceedings.mlr.press/v119/amin20a.html | [
"Saadullah Amin",
"Stalin Varanasi",
"Katherine Ann Dunfield",
"Günter Neumann"
] | null | null | Knowledge graphs are incomplete by nature, with only a limited number of observed facts from the world knowledge being represented as structured relations between entities. To partly address this issue, an important task in statistical relational learning is that of link prediction or knowledge graph completion. Both l... | [] | null | 25 | 2008.10858 | title_snapshot | [
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Discount Factor as a Regularizer in Reinforcement Learning | https://proceedings.mlr.press/v119/amit20a.html | [
"Ron Amit",
"Ron Meir",
"Kamil Ciosek"
] | null | null | Specifying a Reinforcement Learning (RL) task involves choosing a suitable planning horizon, which is typically modeled by a discount factor. It is known that applying RL algorithms with a lower discount factor can act as a regularizer, improving performance in the limited data regime. Yet the exact nature of this regu... | [] | null | 26 | 2007.02040 | title_snapshot | [
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Neuro-Symbolic Visual Reasoning: Disentangling "Visual" from "Reasoning" | https://proceedings.mlr.press/v119/amizadeh20a.html | [
"Saeed Amizadeh",
"Hamid Palangi",
"Alex Polozov",
"Yichen Huang",
"Kazuhito Koishida"
] | null | null | Visual reasoning tasks such as visual question answering (VQA) require an interplay of visual perception with reasoning about the question semantics grounded in perception. However, recent advances in this area are still primarily driven by perception improvements (e.g. scene graph generation) rather than reasoning. Ne... | [] | null | 27 | 2006.11524 | title_snapshot | [
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The Differentiable Cross-Entropy Method | https://proceedings.mlr.press/v119/amos20a.html | [
"Brandon Amos",
"Denis Yarats"
] | null | null | We study the Cross-Entropy Method (CEM) for the non-convex optimization of a continuous and parameterized objective function and introduce a differentiable variant that enables us to differentiate the output of CEM with respect to the objective function’s parameters. In the machine learning setting this brings CEM insi... | [] | null | 28 | 1909.12830 | title_snapshot | [
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Customizing ML Predictions for Online Algorithms | https://proceedings.mlr.press/v119/anand20a.html | [
"Keerti Anand",
"Rong Ge",
"Debmalya Panigrahi"
] | null | null | A popular line of recent research incorporates ML advice in the design of online algorithms to improve their performance in typical instances. These papers treat the ML algorithm as a black-box, and redesign online algorithms to take advantage of ML predictions. In this paper, we ask the complementary question: can we ... | [] | null | 29 | 2205.08715 | title_snapshot | [
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Fairwashing explanations with off-manifold detergent | https://proceedings.mlr.press/v119/anders20a.html | [
"Christopher Anders",
"Plamen Pasliev",
"Ann-Kathrin Dombrowski",
"Klaus-Robert Müller",
"Pan Kessel"
] | null | null | Explanation methods promise to make black-box classifiers more transparent. As a result, it is hoped that they can act as proof for a sensible, fair and trustworthy decision-making process of the algorithm and thereby increase its acceptance by the end-users. In this paper, we show both theoretically and experimentally... | [] | null | 30 | 2007.09969 | title_snapshot | [
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Population-Based Black-Box Optimization for Biological Sequence Design | https://proceedings.mlr.press/v119/angermueller20a.html | [
"Christof Angermueller",
"David Belanger",
"Andreea Gane",
"Zelda Mariet",
"David Dohan",
"Kevin Murphy",
"Lucy Colwell",
"D Sculley"
] | null | null | The use of black-box optimization for the design of new biological sequences is an emerging research area with potentially revolutionary impact. The cost and latency of wet-lab experiments requires methods that find good sequences in few experimental rounds of large batches of sequences — a setting that off-the-shelf b... | [] | null | 31 | 2006.03227 | title_snapshot | [
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Low-loss connection of weight vectors: distribution-based approaches | https://proceedings.mlr.press/v119/anokhin20a.html | [
"Ivan Anokhin",
"Dmitry Yarotsky"
] | null | null | Recent research shows that sublevel sets of the loss surfaces of overparameterized networks are connected, exactly or approximately. We describe and compare experimentally a panel of methods used to connect two low-loss points by a low-loss curve on this surface. Our methods vary in accuracy and complexity. Most of our... | [] | null | 32 | 2008.00741 | title_snapshot | [
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Online metric algorithms with untrusted predictions | https://proceedings.mlr.press/v119/antoniadis20a.html | [
"Antonios Antoniadis",
"Christian Coester",
"Marek Elias",
"Adam Polak",
"Bertrand Simon"
] | null | null | Machine-learned predictors, although achieving very good results for inputs resembling training data, cannot possibly provide perfect predictions in all situations. Still, decision-making systems that are based on such predictors need not only to benefit from good predictions but also to achieve a decent performance wh... | [] | null | 33 | 2003.02144 | title_snapshot | [
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NADS: Neural Architecture Distribution Search for Uncertainty Awareness | https://proceedings.mlr.press/v119/ardywibowo20a.html | [
"Randy Ardywibowo",
"Shahin Boluki",
"Xinyu Gong",
"Zhangyang Wang",
"Xiaoning Qian"
] | null | null | Machine learning (ML) systems often encounter Out-of-Distribution (OoD) errors when dealing with testing data coming from a distribution different from training data. It becomes important for ML systems in critical applications to accurately quantify its predictive uncertainty and screen out these anomalous inputs. How... | [] | null | 34 | 2006.06646 | title_snapshot | [
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Provable Representation Learning for Imitation Learning via Bi-level Optimization | https://proceedings.mlr.press/v119/arora20a.html | [
"Sanjeev Arora",
"Simon Du",
"Sham Kakade",
"Yuping Luo",
"Nikunj Saunshi"
] | null | null | A common strategy in modern learning systems is to learn a representation that is useful for many tasks, a.k.a. representation learning. We study this strategy in the imitation learning setting for Markov decision processes (MDPs) where multiple experts’ trajectories are available. We formulate representation learning ... | [] | null | 35 | 2002.10544 | title_snapshot | [
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Quantum Boosting | https://proceedings.mlr.press/v119/arunachalam20a.html | [
"Srinivasan Arunachalam",
"Reevu Maity"
] | null | null | Boosting is a technique that boosts a weak and inaccurate machine learning algorithm into a strong accurate learning algorithm. The AdaBoost algorithm by Freund and Schapire (for which they were awarded the G{ö}del prize in 2003) is one of the widely used boosting algorithms, with many applications in theory and practi... | [] | null | 36 | 2002.05056 | title_snapshot | [
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Black-box Certification and Learning under Adversarial Perturbations | https://proceedings.mlr.press/v119/ashtiani20a.html | [
"Hassan Ashtiani",
"Vinayak Pathak",
"Ruth Urner"
] | null | null | We formally study the problem of classification under adversarial perturbations from a learner’s perspective as well as a third-party who aims at certifying the robustness of a given black-box classifier. We analyze a PAC-type framework of semi-supervised learning and identify possibility and impossibility results for ... | [] | null | 37 | 2006.16520 | title_snapshot | [
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Invertible generative models for inverse problems: mitigating representation error and dataset bias | https://proceedings.mlr.press/v119/asim20a.html | [
"Muhammad Asim",
"Mara Daniels",
"Oscar Leong",
"Ali Ahmed",
"Paul Hand"
] | null | null | Trained generative models have shown remarkable performance as priors for inverse problems in imaging – for example, Generative Adversarial Network priors permit recovery of test images from 5-10x fewer measurements than sparsity priors. Unfortunately, these models may be unable to represent any particular image becaus... | [] | null | 38 | 1905.11672 | title_snapshot | [
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On the Convergence of Nesterov’s Accelerated Gradient Method in Stochastic Settings | https://proceedings.mlr.press/v119/assran20a.html | [
"Mahmoud Assran",
"Mike Rabbat"
] | null | null | We study Nesterov’s accelerated gradient method with constant step-size and momentum parameters in the stochastic approximation setting (unbiased gradients with bounded variance) and the finite-sum setting (where randomness is due to sampling mini-batches). To build better insight into the behavior of Nesterov’s method... | [] | null | 39 | 2002.12414 | title_snapshot | [
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Safe screening rules for L0-regression from Perspective Relaxations | https://proceedings.mlr.press/v119/atamturk20a.html | [
"Alper Atamturk",
"Andres Gomez"
] | null | null | We give safe screening rules to eliminate variables from regression with $\ell_0$ regularization or cardinality constraint. These rules are based on guarantees that a feature may or may not be selected in an optimal solution. The screening rules can be computed from a convex relaxation solution in linear time, without ... | [] | null | 40 | 2004.08773 | title_judge | [
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Adversarial Learning Guarantees for Linear Hypotheses and Neural Networks | https://proceedings.mlr.press/v119/awasthi20a.html | [
"Pranjal Awasthi",
"Natalie Frank",
"Mehryar Mohri"
] | null | null | Adversarial or test time robustness measures the susceptibility of a classifier to perturbations to the test input. While there has been a flurry of recent work on designing defenses against such perturbations, the theory of adversarial robustness is not well understood. In order to make progress on this, we focus on t... | [] | null | 41 | 2004.13617 | title_snapshot | [
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Sample Amplification: Increasing Dataset Size even when Learning is Impossible | https://proceedings.mlr.press/v119/axelrod20a.html | [
"Brian Axelrod",
"Shivam Garg",
"Vatsal Sharan",
"Gregory Valiant"
] | null | null | Given data drawn from an unknown distribution, D, to what extent is it possible to “amplify” this dataset and faithfully output an even larger set of samples that appear to have been drawn from D? We formalize this question as follows: an (n,m) amplification procedure takes as input n independent draws from an unknown ... | [] | null | 42 | 1904.12053 | title_snapshot | [
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Sparse Convex Optimization via Adaptively Regularized Hard Thresholding | https://proceedings.mlr.press/v119/axiotis20a.html | [
"Kyriakos Axiotis",
"Maxim Sviridenko"
] | null | null | The goal of Sparse Convex Optimization is to optimize a convex function $f$ under a sparsity constraint $s\leq s^*\gamma$, where $s^*$ is the target number of non-zero entries in a feasible solution (sparsity) and $\gamma\geq 1$ is an approximation factor. There has been a lot of work to analyze the sparsity guarantees... | [] | null | 43 | 2006.14571 | title_snapshot | [
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Model-Based Reinforcement Learning with Value-Targeted Regression | https://proceedings.mlr.press/v119/ayoub20a.html | [
"Alex Ayoub",
"Zeyu Jia",
"Csaba Szepesvari",
"Mengdi Wang",
"Lin Yang"
] | null | null | This paper studies model-based reinforcement learning (RL) for regret minimization. We focus on finite-horizon episodic RL where the transition model $P$ belongs to a known family of models $\mathcal{P}$, a special case of which is when models in $\mathcal{P}$ take the form of linear mixtures: $P_{\theta} = \sum_{i=1}^... | [] | null | 44 | 2006.01107 | title_snapshot | [
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Forecasting Sequential Data Using Consistent Koopman Autoencoders | https://proceedings.mlr.press/v119/azencot20a.html | [
"Omri Azencot",
"N. Benjamin Erichson",
"Vanessa Lin",
"Michael Mahoney"
] | null | null | Recurrent neural networks are widely used on time series data, yet such models often ignore the underlying physical structures in such sequences. A new class of physics-based methods related to Koopman theory has been introduced, offering an alternative for processing nonlinear dynamical systems. In this work, we propo... | [] | null | 45 | 2003.02236 | title_snapshot | [
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Constant Curvature Graph Convolutional Networks | https://proceedings.mlr.press/v119/bachmann20a.html | [
"Gregor Bachmann",
"Gary Becigneul",
"Octavian Ganea"
] | null | null | Interest has been rising lately towards methods representing data in non-Euclidean spaces, e.g. hyperbolic or spherical that provide specific inductive biases useful for certain real-world data properties, e.g. scale-free, hierarchical or cyclical. However, the popular graph neural networks are currently limited in mod... | [] | null | 46 | 1911.05076 | title_snapshot | [
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Scalable Nearest Neighbor Search for Optimal Transport | https://proceedings.mlr.press/v119/backurs20a.html | [
"Arturs Backurs",
"Yihe Dong",
"Piotr Indyk",
"Ilya Razenshteyn",
"Tal Wagner"
] | null | null | The Optimal Transport (a.k.a. Wasserstein) distance is an increasingly popular similarity measure for rich data domains, such as images or text documents. This raises the necessity for fast nearest neighbor search algorithms according to this distance, which poses a substantial computational bottleneck on massive datas... | [] | null | 47 | 1910.04126 | title_snapshot | [
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Agent57: Outperforming the Atari Human Benchmark | https://proceedings.mlr.press/v119/badia20a.html | [
"Adrià Puigdomènech Badia",
"Bilal Piot",
"Steven Kapturowski",
"Pablo Sprechmann",
"Alex Vitvitskyi",
"Zhaohan Daniel Guo",
"Charles Blundell"
] | null | null | Atari games have been a long-standing benchmark in the reinforcement learning (RL) community for the past decade. This benchmark was proposed to test general competency of RL algorithms. Previous work has achieved good average performance by doing outstandingly well on many games of the set, but very poorly in several ... | [] | null | 48 | 2003.13350 | title_snapshot | [
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Fiduciary Bandits | https://proceedings.mlr.press/v119/bahar20a.html | [
"Gal Bahar",
"Omer Ben-Porat",
"Kevin Leyton-Brown",
"Moshe Tennenholtz"
] | null | null | Recommendation systems often face exploration-exploitation tradeoffs: the system can only learn about the desirability of new options by recommending them to some user. Such systems can thus be modeled as multi-armed bandit settings; however, users are self-interested and cannot be made to follow recommendations. We as... | [] | null | 49 | 1905.07043 | title_snapshot | [
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Learning De-biased Representations with Biased Representations | https://proceedings.mlr.press/v119/bahng20a.html | [
"Hyojin Bahng",
"Sanghyuk Chun",
"Sangdoo Yun",
"Jaegul Choo",
"Seong Joon Oh"
] | null | null | Many machine learning algorithms are trained and evaluated by splitting data from a single source into training and test sets. While such focus on in-distribution learning scenarios has led to interesting advancement, it has not been able to tell if models are relying on dataset biases as shortcuts for successful predi... | [] | null | 50 | 1910.02806 | title_snapshot | [
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Deep k-NN for Noisy Labels | https://proceedings.mlr.press/v119/bahri20a.html | [
"Dara Bahri",
"Heinrich Jiang",
"Maya Gupta"
] | null | null | Modern machine learning models are often trained on examples with noisy labels that hurt performance and are hard to identify. In this paper, we provide an empirical study showing that a simple $k$-nearest neighbor-based filtering approach on the logit layer of a preliminary model can remove mislabeled training data an... | [] | null | 51 | 2004.12289 | title_snapshot | [
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Provable Self-Play Algorithms for Competitive Reinforcement Learning | https://proceedings.mlr.press/v119/bai20a.html | [
"Yu Bai",
"Chi Jin"
] | null | null | Self-play, where the algorithm learns by playing against itself without requiring any direct supervision, has become the new weapon in modern Reinforcement Learning (RL) for achieving superhuman performance in practice. However, the majority of exisiting theory in reinforcement learning only applies to the setting wher... | [] | null | 52 | 2002.04017 | title_snapshot | [
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Sparse Subspace Clustering with Entropy-Norm | https://proceedings.mlr.press/v119/bai20b.html | [
"Liang Bai",
"Jiye Liang"
] | null | null | In this paper, we provide an explicit theoretical connection between Sparse subspace clustering (SSC) and spectral clustering (SC) from the perspective of learning a data similarity matrix. We show that spectral clustering with Gaussian kernel can be viewed as sparse subspace clustering with entropy-norm (SSC+E). Compa... | [] | null | 53 | null | null | [
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Coresets for Clustering in Graphs of Bounded Treewidth | https://proceedings.mlr.press/v119/baker20a.html | [
"Daniel Baker",
"Vladimir Braverman",
"Lingxiao Huang",
"Shaofeng H.-C. Jiang",
"Robert Krauthgamer",
"Xuan Wu"
] | null | null | We initiate the study of coresets for clustering in graph metrics, i.e., the shortest-path metric of edge-weighted graphs. Such clustering problems are essential to data analysis and used for example in road networks and data visualization. A coreset is a compact summary of the data that approximately preserves the clu... | [] | null | 54 | 1907.04733 | title_snapshot | [
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Refined bounds for algorithm configuration: The knife-edge of dual class approximability | https://proceedings.mlr.press/v119/balcan20a.html | [
"Maria-Florina Balcan",
"Tuomas Sandholm",
"Ellen Vitercik"
] | null | null | Automating algorithm configuration is growing increasingly necessary as algorithms come with more and more tunable parameters. It is common to tune parameters using machine learning, optimizing algorithmic performance (runtime or solution quality, for example) using a training set of problem instances from the specific... | [] | null | 55 | 2006.11827 | title_snapshot | [
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Ready Policy One: World Building Through Active Learning | https://proceedings.mlr.press/v119/ball20a.html | [
"Philip Ball",
"Jack Parker-Holder",
"Aldo Pacchiano",
"Krzysztof Choromanski",
"Stephen Roberts"
] | null | null | Model-Based Reinforcement Learning (MBRL) offers a promising direction for sample efficient learning, often achieving state of the art results for continuous control tasks. However many existing MBRL methods rely on combining greedy policies with exploration heuristics, and even those which utilize principled explorati... | [] | null | 56 | 2002.02693 | title_snapshot | [
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Stochastic Optimization for Regularized Wasserstein Estimators | https://proceedings.mlr.press/v119/ballu20a.html | [
"Marin Ballu",
"Quentin Berthet",
"Francis Bach"
] | null | null | Optimal transport is a foundational problem in optimization, that allows to compare probability distributions while taking into account geometric aspects. Its optimal objective value, the Wasserstein distance, provides an important loss between distributions that has been used in many applications throughout machine le... | [] | null | 57 | 2002.08695 | title_snapshot | [
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Dual Mirror Descent for Online Allocation Problems | https://proceedings.mlr.press/v119/balseiro20a.html | [
"Santiago Balseiro",
"Haihao Lu",
"Vahab Mirrokni"
] | null | null | We consider online allocation problems with concave revenue functions and resource constraints, which are central problems in revenue management and online advertising. In these settings, requests arrive sequentially during a finite horizon and, for each request, a decision maker needs to choose an action that consumes... | [] | null | 58 | 2002.10421 | title_snapshot | [
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Inductive-bias-driven Reinforcement Learning For Efficient Schedules in Heterogeneous Clusters | https://proceedings.mlr.press/v119/banerjee20a.html | [
"Subho Banerjee",
"Saurabh Jha",
"Zbigniew Kalbarczyk",
"Ravishankar Iyer"
] | null | null | The problem of scheduling of workloads onto heterogeneous processors (e.g., CPUs, GPUs, FPGAs) is of fundamental importance in modern data centers. Current system schedulers rely on application/system-specific heuristics that have to be built on a case-by-case basis. Recent work has demonstrated ML techniques for autom... | [] | null | 59 | 1909.02119 | title_snapshot | [
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UniLMv2: Pseudo-Masked Language Models for Unified Language Model Pre-Training | https://proceedings.mlr.press/v119/bao20a.html | [
"Hangbo Bao",
"Li Dong",
"Furu Wei",
"Wenhui Wang",
"Nan Yang",
"Xiaodong Liu",
"Yu Wang",
"Jianfeng Gao",
"Songhao Piao",
"Ming Zhou",
"Hsiao-Wuen Hon"
] | null | null | We propose to pre-train a unified language model for both autoencoding and partially autoregressive language modeling tasks using a novel training procedure, referred to as a pseudo-masked language model (PMLM). Given an input text with masked tokens, we rely on conventional masks to learn inter-relations between corru... | [] | null | 60 | 2002.12804 | title_snapshot | [
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Fast OSCAR and OWL Regression via Safe Screening Rules | https://proceedings.mlr.press/v119/bao20b.html | [
"Runxue Bao",
"Bin Gu",
"Heng Huang"
] | null | null | Ordered Weighted $L_{1}$ (OWL) regularized regression is a new regression analysis for high-dimensional sparse learning. Proximal gradient methods are used as standard approaches to solve OWL regression. However, it is still a burning issue to solve OWL regression due to considerable computational cost and memory usage... | [] | null | 61 | 2006.16433 | title_snapshot | [
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Option Discovery in the Absence of Rewards with Manifold Analysis | https://proceedings.mlr.press/v119/bar20a.html | [
"Amitay Bar",
"Ronen Talmon",
"Ron Meir"
] | null | null | Options have been shown to be an effective tool in reinforcement learning, facilitating improved exploration and learning. In this paper, we present an approach based on spectral graph theory and derive an algorithm that systematically discovers options without access to a specific reward or task assignment. As opposed... | [] | null | 62 | 2003.05878 | title_snapshot | [
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Learning the piece-wise constant graph structure of a varying Ising model | https://proceedings.mlr.press/v119/bars20a.html | [
"Batiste Le Bars",
"Pierre Humbert",
"Argyris Kalogeratos",
"Nicolas Vayatis"
] | null | null | This work focuses on the estimation of multiple change-points in a time-varying Ising model that evolves piece-wise constantly. The aim is to identify both the moments at which significant changes occur in the Ising model, as well as the underlying graph structures. For this purpose, we propose to estimate the neighbor... | [] | null | 63 | 1910.08512 | title_snapshot | [
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Frequency Bias in Neural Networks for Input of Non-Uniform Density | https://proceedings.mlr.press/v119/basri20a.html | [
"Ronen Basri",
"Meirav Galun",
"Amnon Geifman",
"David Jacobs",
"Yoni Kasten",
"Shira Kritchman"
] | null | null | Recent works have partly attributed the generalization ability of over-parameterized neural networks to frequency bias – networks trained with gradient descent on data drawn from a uniform distribution find a low frequency fit before high frequency ones. As realistic training sets are not drawn from a uniform distribut... | [] | null | 64 | 2003.04560 | title_snapshot | [
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Private Query Release Assisted by Public Data | https://proceedings.mlr.press/v119/bassily20a.html | [
"Raef Bassily",
"Albert Cheu",
"Shay Moran",
"Aleksandar Nikolov",
"Jonathan Ullman",
"Steven Wu"
] | null | null | We study the problem of differentially private query release assisted by access to public data. In this problem, the goal is to answer a large class $\mathcal{H}$ of statistical queries with error no more than $\alpha$ using a combination of public and private samples. The algorithm is required to satisfy differential ... | [] | null | 65 | 2004.10941 | title_snapshot | [
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ECLIPSE: An Extreme-Scale Linear Program Solver for Web-Applications | https://proceedings.mlr.press/v119/basu20a.html | [
"Kinjal Basu",
"Amol Ghoting",
"Rahul Mazumder",
"Yao Pan"
] | null | null | Key problems arising in web applications (with millions of users and thousands of items) can be formulated as linear programs involving billions to trillions of decision variables and constraints. Despite the appeal of linear program (LP) formulations, solving problems at these scales appear to be well beyond the capab... | [] | null | 66 | null | null | [
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On Second-Order Group Influence Functions for Black-Box Predictions | https://proceedings.mlr.press/v119/basu20b.html | [
"Samyadeep Basu",
"Xuchen You",
"Soheil Feizi"
] | null | null | With the rapid adoption of machine learning systems in sensitive applications, there is an increasing need to make black-box models explainable. Often we want to identify an influential group of training samples in a particular test prediction for a given machine learning model. Existing influence functions tackle this... | [] | null | 67 | 1911.00418 | title_snapshot | [
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Kernel interpolation with continuous volume sampling | https://proceedings.mlr.press/v119/belhadji20a.html | [
"Ayoub Belhadji",
"Rémi Bardenet",
"Pierre Chainais"
] | null | null | A fundamental task in kernel methods is to pick nodes and weights, so as to approximate a given function from an RKHS by the weighted sum of kernel translates located at the nodes. This is the crux of kernel density estimation, kernel quadrature, or interpolation from discrete samples. Furthermore, RKHSs offer a conven... | [] | null | 68 | 2002.09677 | title_snapshot | [
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Decoupled Greedy Learning of CNNs | https://proceedings.mlr.press/v119/belilovsky20a.html | [
"Eugene Belilovsky",
"Michael Eickenberg",
"Edouard Oyallon"
] | null | null | A commonly cited inefficiency of neural network training by back-propagation is the update locking problem: each layer must wait for the signal to propagate through the network before updating. In recent years multiple authors have considered alternatives that can alleviate this issue. In this context, we consider a si... | [] | null | 69 | 1901.08164 | title_snapshot | [
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The Cost-free Nature of Optimally Tuning Tikhonov Regularizers and Other Ordered Smoothers | https://proceedings.mlr.press/v119/bellec20a.html | [
"Pierre Bellec",
"Dana Yang"
] | null | null | We consider the problem of selecting the best estimator among a family of Tikhonov regularized estimators, or, alternatively, to select a linear combination of these regularizers that is as good as the best regularizer in the family. Our theory reveals that if the Tikhonov regularizers share the same penalty matrix wit... | [] | null | 70 | 1905.12517 | title_snapshot | [
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Defense Through Diverse Directions | https://proceedings.mlr.press/v119/bender20a.html | [
"Christopher Bender",
"Yang Li",
"Yifeng Shi",
"Michael K. Reiter",
"Junier Oliva"
] | null | null | In this work we develop a novel Bayesian neural network methodology to achieve strong adversarial robustness without the need for online adversarial training. Unlike previous efforts in this direction, we do not rely solely on the stochasticity of network weights by minimizing the divergence between the learned paramet... | [] | null | 71 | 2003.10602 | title_snapshot | [
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Interference and Generalization in Temporal Difference Learning | https://proceedings.mlr.press/v119/bengio20a.html | [
"Emmanuel Bengio",
"Joelle Pineau",
"Doina Precup"
] | null | null | We study the link between generalization and interference in temporal-difference (TD) learning. Interference is defined as the inner product of two different gradients, representing their alignment; this quantity emerges as being of interest from a variety of observations about neural networks, parameter sharing and th... | [] | null | 72 | 2003.06350 | title_snapshot | [
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Preselection Bandits | https://proceedings.mlr.press/v119/bengs20a.html | [
"Viktor Bengs",
"Eyke Hüllermeier"
] | null | null | In this paper, we introduce the Preselection Bandit problem, in which the learner preselects a subset of arms (choice alternatives) for a user, which then chooses the final arm from this subset. The learner is not aware of the user’s preferences, but can learn them from observed choices. In our concrete setting, we all... | [] | null | 73 | 1907.06123 | title_snapshot | [
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Efficient Policy Learning from Surrogate-Loss Classification Reductions | https://proceedings.mlr.press/v119/bennett20a.html | [
"Andrew Bennett",
"Nathan Kallus"
] | null | null | Recent work on policy learning from observational data has highlighted the importance of efficient policy evaluation and has proposed reductions to weighted (cost-sensitive) classification. But, efficient policy evaluation need not yield efficient estimation of policy parameters. We consider the estimation problem give... | [] | null | 74 | 2002.05153 | title_snapshot | [
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Training Neural Networks for and by Interpolation | https://proceedings.mlr.press/v119/berrada20a.html | [
"Leonard Berrada",
"Andrew Zisserman",
"M. Pawan Kumar"
] | null | null | In modern supervised learning, many deep neural networks are able to interpolate the data: the empirical loss can be driven to near zero on all samples simultaneously. In this work, we explicitly exploit this interpolation property for the design of a new optimization algorithm for deep learning, which we term Adaptive... | [] | null | 75 | 1906.05661 | title_snapshot | [
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Implicit differentiation of Lasso-type models for hyperparameter optimization | https://proceedings.mlr.press/v119/bertrand20a.html | [
"Quentin Bertrand",
"Quentin Klopfenstein",
"Mathieu Blondel",
"Samuel Vaiter",
"Alexandre Gramfort",
"Joseph Salmon"
] | null | null | Setting regularization parameters for Lasso-type estimators is notoriously difficult, though crucial for obtaining the best accuracy. The most popular hyperparameter optimization approach is grid-search on a held-out dataset. However, grid-search requires to choose a predefined grid of parameters and scales exponential... | [] | null | 76 | 2002.08943 | title_snapshot | [
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Online Learning with Imperfect Hints | https://proceedings.mlr.press/v119/bhaskara20a.html | [
"Aditya Bhaskara",
"Ashok Cutkosky",
"Ravi Kumar",
"Manish Purohit"
] | null | null | We consider a variant of the classical online linear optimization problem in which at every step, the online player receives a “hint” vector before choosing the action for that round. Rather surprisingly, it was shown that if the hint vector is guaranteed to have a positive correlation with the cost vector, then the on... | [] | null | 77 | 2002.04726 | title_snapshot | [
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When are Non-Parametric Methods Robust? | https://proceedings.mlr.press/v119/bhattacharjee20a.html | [
"Robi Bhattacharjee",
"Kamalika Chaudhuri"
] | null | null | A growing body of research has shown that many classifiers are susceptible to adversarial examples – small strategic modifications to test inputs that lead to misclassification. In this work, we study general non-parametric methods, with a view towards understanding when they are robust to these modifications. We estab... | [] | null | 78 | 2003.06121 | title_snapshot | [
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Learning and Sampling of Atomic Interventions from Observations | https://proceedings.mlr.press/v119/bhattacharyya20a.html | [
"Arnab Bhattacharyya",
"Sutanu Gayen",
"Saravanan Kandasamy",
"Ashwin Maran",
"Vinodchandran N. Variyam"
] | null | null | We study the problem of efficiently estimating the effect of an intervention on a single variable using observational samples. Our goal is to give algorithms with polynomial time and sample complexity in a non-parametric setting. Tian and Pearl (AAAI ’02) have exactly characterized the class of causal graphs for which ... | [] | null | 79 | 2002.04232 | title_snapshot | [
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Near-optimal sample complexity bounds for learning Latent $k-$polytopes and applications to Ad-Mixtures | https://proceedings.mlr.press/v119/bhattacharyya20b.html | [
"Chiranjib Bhattacharyya",
"Ravindran Kannan"
] | null | null | Deriving Optimal bounds on Sample Complexity of Latent Variable models is an active area of research. Recently such bounds were obtained for Mixture of Gaussians \cite{HSNCAY18}, no such results are known for Ad-mixtures, a generalization of Mixture distributions. In this paper we show that $O^*(dk/m)$ samples are suff... | [] | null | 80 | null | null | [
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Low-Rank Bottleneck in Multi-head Attention Models | https://proceedings.mlr.press/v119/bhojanapalli20a.html | [
"Srinadh Bhojanapalli",
"Chulhee Yun",
"Ankit Singh Rawat",
"Sashank Reddi",
"Sanjiv Kumar"
] | null | null | Attention based Transformer architecture has enabled significant advances in the field of natural language processing. In addition to new pre-training techniques, recent improvements crucially rely on working with a relatively larger embedding dimension for tokens. Unfortunately, this leads to models that are prohibiti... | [] | null | 81 | 2002.07028 | title_snapshot | [
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Spectral Clustering with Graph Neural Networks for Graph Pooling | https://proceedings.mlr.press/v119/bianchi20a.html | [
"Filippo Maria Bianchi",
"Daniele Grattarola",
"Cesare Alippi"
] | null | null | Spectral clustering (SC) is a popular clustering technique to find strongly connected communities on a graph. SC can be used in Graph Neural Networks (GNNs) to implement pooling operations that aggregate nodes belonging to the same cluster. However, the eigendecomposition of the Laplacian is expensive and, since cluste... | [] | null | 82 | 1907.00481 | title_snapshot | [
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Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders | https://proceedings.mlr.press/v119/bica20a.html | [
"Ioana Bica",
"Ahmed Alaa",
"Mihaela Van Der Schaar"
] | null | null | The estimation of treatment effects is a pervasive problem in medicine. Existing methods for estimating treatment effects from longitudinal observational data assume that there are no hidden confounders, an assumption that is not testable in practice and, if it does not hold, leads to biased estimates. In this paper, w... | [] | null | 83 | 1902.00450 | title_snapshot | [
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Adversarial Robustness for Code | https://proceedings.mlr.press/v119/bielik20a.html | [
"Pavol Bielik",
"Martin Vechev"
] | null | null | Machine learning and deep learning in particular has been recently used to successfully address many tasks in the domain of code such as finding and fixing bugs, code completion, decompilation, type inference and many others. However, the issue of adversarial robustness of models for code has gone largely unnoticed. In... | [] | null | 84 | 2002.04694 | title_snapshot | [
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The Boomerang Sampler | https://proceedings.mlr.press/v119/bierkens20a.html | [
"Joris Bierkens",
"Sebastiano Grazzi",
"Kengo Kamatani",
"Gareth Roberts"
] | null | null | This paper introduces the boomerang sampler as a novel class of continuous-time non-reversible Markov chain Monte Carlo algorithms. The methodology begins by representing the target density as a density, $e^{-U}$, with respect to a prescribed (usually) Gaussian measure and constructs a continuous trajectory consisting ... | [] | null | 85 | 2006.13777 | title_snapshot | [
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Tight Bounds on Minimax Regret under Logarithmic Loss via Self-Concordance | https://proceedings.mlr.press/v119/bilodeau20a.html | [
"Blair Bilodeau",
"Dylan Foster",
"Daniel Roy"
] | null | null | We consider the classical problem of sequential probability assignment under logarithmic loss while competing against an arbitrary, potentially nonparametric class of experts. We obtain tight bounds on the minimax regret via a new approach that exploits the self-concordance property of the logarithmic loss. We show tha... | [] | null | 86 | 2007.01160 | title_snapshot | [
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My Fair Bandit: Distributed Learning of Max-Min Fairness with Multi-player Bandits | https://proceedings.mlr.press/v119/bistritz20a.html | [
"Ilai Bistritz",
"Tavor Baharav",
"Amir Leshem",
"Nicholas Bambos"
] | null | null | Consider N cooperative but non-communicating players where each plays one out of M arms for T turns. Players have different utilities for each arm, representable as an NxM matrix. These utilities are unknown to the players. In each turn players receive noisy observations of their utility for their selected arm. However... | [] | null | 87 | 2002.09808 | title_snapshot | [
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Provable guarantees for decision tree induction: the agnostic setting | https://proceedings.mlr.press/v119/blanc20a.html | [
"Guy Blanc",
"Jane Lange",
"Li-Yang Tan"
] | null | null | We give strengthened provable guarantees on the performance of widely employed and empirically successful {\sl top-down decision tree learning heuristics}. While prior works have focused on the realizable setting, we consider the more realistic and challenging {\sl agnostic} setting. We show that for all monotone funct... | [] | null | 88 | 2006.00743 | title_snapshot | [
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Fast Differentiable Sorting and Ranking | https://proceedings.mlr.press/v119/blondel20a.html | [
"Mathieu Blondel",
"Olivier Teboul",
"Quentin Berthet",
"Josip Djolonga"
] | null | null | The sorting operation is one of the most commonly used building blocks in computer programming. In machine learning, it is often used for robust statistics. However, seen as a function, it is piecewise linear and as a result includes many kinks where it is non-differentiable. More problematic is the related ranking ope... | [] | null | 89 | 2002.08871 | title_snapshot | [
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Beyond Signal Propagation: Is Feature Diversity Necessary in Deep Neural Network Initialization? | https://proceedings.mlr.press/v119/blumenfeld20a.html | [
"Yaniv Blumenfeld",
"Dar Gilboa",
"Daniel Soudry"
] | null | null | Deep neural networks are typically initialized with random weights, with variances chosen to facilitate signal propagation and stable gradients. It is also believed that diversity of features is an important property of these initializations. We construct a deep convolutional network with identical features by initiali... | [] | null | 90 | 2007.01038 | title_snapshot | [
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Modulating Surrogates for Bayesian Optimization | https://proceedings.mlr.press/v119/bodin20a.html | [
"Erik Bodin",
"Markus Kaiser",
"Ieva Kazlauskaite",
"Zhenwen Dai",
"Neill Campbell",
"Carl Henrik Ek"
] | null | null | Bayesian optimization (BO) methods often rely on the assumption that the objective function is well-behaved, but in practice, this is seldom true for real-world objectives even if noise-free observations can be collected. Common approaches, which try to model the objective as precisely as possible, often fail to make p... | [] | null | 91 | 1906.11152 | title_snapshot | [
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Deep Coordination Graphs | https://proceedings.mlr.press/v119/boehmer20a.html | [
"Wendelin Boehmer",
"Vitaly Kurin",
"Shimon Whiteson"
] | null | null | This paper introduces the deep coordination graph (DCG) for collaborative multi-agent reinforcement learning. DCG strikes a flexible trade-off between representational capacity and generalization by factoring the joint value function of all agents according to a coordination graph into payoffs between pairs of agents. ... | [] | null | 92 | 1910.00091 | title_snapshot | [
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Lorentz Group Equivariant Neural Network for Particle Physics | https://proceedings.mlr.press/v119/bogatskiy20a.html | [
"Alexander Bogatskiy",
"Brandon Anderson",
"Jan Offermann",
"Marwah Roussi",
"David Miller",
"Risi Kondor"
] | null | null | We present a neural network architecture that is fully equivariant with respect to transformations under the Lorentz group, a fundamental symmetry of space and time in physics. The architecture is based on the theory of the finite-dimensional representations of the Lorentz group and the equivariant nonlinearity involve... | [] | null | 93 | 2006.04780 | title_snapshot | [
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Efficient Robustness Certificates for Discrete Data: Sparsity-Aware Randomized Smoothing for Graphs, Images and More | https://proceedings.mlr.press/v119/bojchevski20a.html | [
"Aleksandar Bojchevski",
"Johannes Gasteiger",
"Stephan Günnemann"
] | null | null | Existing techniques for certifying the robustness of models for discrete data either work only for a small class of models or are general at the expense of efficiency or tightness. Moreover, they do not account for sparsity in the input which, as our findings show, is often essential for obtaining non-trivial guarantee... | [] | null | 94 | 2008.12952 | title_snapshot | [
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Proper Network Interpretability Helps Adversarial Robustness in Classification | https://proceedings.mlr.press/v119/boopathy20a.html | [
"Akhilan Boopathy",
"Sijia Liu",
"Gaoyuan Zhang",
"Cynthia Liu",
"Pin-Yu Chen",
"Shiyu Chang",
"Luca Daniel"
] | null | null | Recent works have empirically shown that there exist adversarial examples that can be hidden from neural network interpretability (namely, making network interpretation maps visually similar), or interpretability is itself susceptible to adversarial attacks. In this paper, we theoretically show that with a proper measu... | [] | null | 95 | 2006.14748 | title_snapshot | [
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Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural Networks | https://proceedings.mlr.press/v119/bordelon20a.html | [
"Blake Bordelon",
"Abdulkadir Canatar",
"Cengiz Pehlevan"
] | null | null | We derive analytical expressions for the generalization performance of kernel regression as a function of the number of training samples using theoretical methods from Gaussian processes and statistical physics. Our expressions apply to wide neural networks due to an equivalence between training them and kernel regress... | [] | null | 96 | 2002.02561 | title_snapshot | [
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Small Data, Big Decisions: Model Selection in the Small-Data Regime | https://proceedings.mlr.press/v119/bornschein20a.html | [
"Jorg Bornschein",
"Francesco Visin",
"Simon Osindero"
] | null | null | Highly overparametrized neural networks can display curiously strong generalization performance – a phenomenon that has recently garnered a wealth of theoretical and empirical research in order to better understand it. In contrast to most previous work, which typically considers the performance as a function of the mod... | [] | null | 97 | 2009.12583 | title_snapshot | [
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Latent Variable Modelling with Hyperbolic Normalizing Flows | https://proceedings.mlr.press/v119/bose20a.html | [
"Joey Bose",
"Ariella Smofsky",
"Renjie Liao",
"Prakash Panangaden",
"Will Hamilton"
] | null | null | The choice of approximate posterior distributions plays a central role in stochastic variational inference (SVI). One effective solution is the use of normalizing flows \cut{defined on Euclidean spaces} to construct flexible posterior distributions. However, one key limitation of existing normalizing flows is that they... | [] | null | 98 | 2002.06336 | title_snapshot | [
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Tightening Exploration in Upper Confidence Reinforcement Learning | https://proceedings.mlr.press/v119/bourel20a.html | [
"Hippolyte Bourel",
"Odalric Maillard",
"Mohammad Sadegh Talebi"
] | null | null | The upper confidence reinforcement learning (UCRL2) algorithm introduced in \citep{jaksch2010near} is a popular method to perform regret minimization in unknown discrete Markov Decision Processes under the average-reward criterion. Despite its nice and generic theoretical regret guarantees, this algorithm and its varia... | [] | null | 99 | 2004.09656 | title_snapshot | [
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Preference Modeling with Context-Dependent Salient Features | https://proceedings.mlr.press/v119/bower20a.html | [
"Amanda Bower",
"Laura Balzano"
] | null | null | We consider the problem of estimating a ranking on a set of items from noisy pairwise comparisons given item features. We address the fact that pairwise comparison data often reflects irrational choice, e.g. intransitivity. Our key observation is that two items compared in isolation from other items may be compared bas... | [] | null | 100 | 2002.09615 | title_snapshot | [
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