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20182025
most citedDistributionally Robust Reinforcement Learning

18 citations · 19 across the 7 of their papers we have counts for

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9 papers · 1 filter

stat.ML2021

Fundamental tradeoffs between memorization and robustness in random features and neural tangent regimes

Elvis Dohmatob

This work studies the (non)robustness of two-layer neural networks in various high-dimensional linearized regimes. We establish fundamental trade-offs between memorization and robu…

stat.ML2020

Implicit bias of any algorithm: bounding bias via margin

Elvis Dohmatob

Consider points in finite-dimensional euclidean space, each having one of two colors. Suppose there exists a separating hyperplane (identified with its unit no…

stat.ML2020

Classifier-independent Lower-Bounds for Adversarial Robustness

Elvis Dohmatob

We theoretically analyse the limits of robustness to test-time adversarial and noisy examples in classification. Our work focuses on deriving bounds which uniformly apply to all cl…

stat.ML2020

Learning disconnected manifolds: a no GANs land

Ugo Tanielian, Thibaut Issenhuth, Elvis Dohmatob +1

Typical architectures of Generative AdversarialNetworks make use of a unimodal latent distribution transformed by a continuous generator. Consequently, the modeled distribution alw…

stat.ML20191 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…

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…