ICML
Collection
Accepted papers for ICML (International Conference on Machine Learning), one dataset per year. • 13 items • Updated
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values | abstract large_stringlengths 289 1.91k | keywords listlengths 0 0 | TL;DR large_stringclasses 0
values | submission_number int64 1 1.08k | arxiv_id stringlengths 10 10 ⌀ | arxiv_id_source stringclasses 2
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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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