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20142026
most citedA Tale of Tails: Model Collapse as a Change of Scaling Laws

39 citations · 158 across the 36 of their papers we have counts for

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Showing 2019Show all

5 papers · 1 filter

stat.ML2019★ 1 cited

On the Convergence of Approximate and Regularized Policy Iteration Schemes

Elena Smirnova, Elvis Dohmatob

Entropy regularized algorithms such as Soft Q-learning and Soft Actor-Critic, recently showed state-of-the-art performance on a number of challenging reinforcement learning (RL) ta…

cs.LG2019★ 11 cited

Adversarial Robustness via Label-Smoothing

Morgane Goibert, Elvis Dohmatob

We study Label-Smoothing as a means for improving adversarial robustness of supervised deep-learning models. After establishing a thorough and unified framework, we propose several…

stat.ML2019

Distributionally Robust Counterfactual Risk Minimization

Louis Faury, Ugo Tanielian, Flavian Vasile +2

This manuscript introduces the idea of using Distributionally Robust Optimization (DRO) for the Counterfactual Risk Minimization (CRM) problem. Tapping into a rich existing literat…

cs.LG2019★ 18 cited

Learning Nonsymmetric Determinantal Point Processes

Mike Gartrell, Victor-Emmanuel Brunel, Elvis Dohmatob +1

Determinantal point processes (DPPs) have attracted substantial attention as an elegant probabilistic model that captures the balance between quality and diversity within sets. DPP…

stat.ML2019★ 18 cited

Distributionally Robust Reinforcement Learning

Elena Smirnova, Elvis Dohmatob, Jérémie Mary

Real-world applications require RL algorithms to act safely. During learning process, it is likely that the agent executes sub-optimal actions that may lead to unsafe/poor states o…