most citedDistributionally Robust Reinforcement Learning

18 citations · 29 across the 4 of their papers we have counts for

collaborators

5 papers

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…

stat.ML201918 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…

cs.IR20176 cited

Specializing Joint Representations for the task of Product Recommendation

Thomas Nedelec, Elena Smirnova, Flavian Vasile

We propose a unified product embedded representation that is optimized for the task of retrieval-based product recommendation. To this end, we introduce a new way to fuse modality-…

cs.IR20174 cited

Contextual Sequence Modeling for Recommendation with Recurrent Neural Networks

Elena Smirnova, Flavian Vasile

Recommendations can greatly benefit from good representations of the user state at recommendation time. Recent approaches that leverage Recurrent Neural Networks (RNNs) for session…