activity
20182022
most citedDistributionally Robust Reinforcement Learning

18 citations · 19 across the 6 of their papers we have counts for

collaborators

14 papers

cs.LG2022

An Adversarial Robustness Perspective on the Topology of Neural Networks

Morgane Goibert, Thomas Ricatte, Elvis Dohmatob

In this paper, we investigate the impact of neural networks (NNs) topology on adversarial robustness. Specifically, we study the graph produced when an input traverses all the laye…

cs.IR2022

Fast online ranking with fairness of exposure

Nicolas Usunier, Virginie Do, Elvis Dohmatob

As recommender systems become increasingly central for sorting and prioritizing the content available online, they have a growing impact on the opportunities or revenue of their it…

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…

cs.LG2020

Scalable Learning and MAP Inference for Nonsymmetric Determinantal Point Processes

Mike Gartrell, Insu Han, Elvis Dohmatob +2

Determinantal point processes (DPPs) have attracted significant attention in machine learning for their ability to model subsets drawn from a large item collection. Recent work sho…