activity
20192022
most citedA Theoretical Analysis of Contrastive Unsupervised Representation Learning

204 citations · 213 across the 4 of their papers we have counts for

collaborators

5 papers

cs.DS2022

On the cut-query complexity of approximating max-cut

Orestis Plevrakis, Seyoon Ragavan, S. Matthew Weinberg

We consider the problem of query-efficient global max-cut on a weighted undirected graph in the value oracle model examined by [RSW18]. Graph algorithms in this cut query model and…

cs.LG2021★ 1 cited

Learning from Censored and Dependent Data: The case of Linear Dynamics

Orestis Plevrakis

Observations from dynamical systems often exhibit irregularities, such as censoring, where values are recorded only if they fall within a certain range. Censoring is ubiquitous in…

cs.LG2020★ 1 cited

Geometric Exploration for Online Control

Orestis Plevrakis, Elad Hazan

We study the control of an \emph{unknown} linear dynamical system under general convex costs. The objective is minimizing regret vs. the class of disturbance-feedback-controllers,…

cs.LG2020★ 7 cited

Over-parameterized Adversarial Training: An Analysis Overcoming the Curse of Dimensionality

Yi Zhang, Orestis Plevrakis, Simon S. Du +3

Adversarial training is a popular method to give neural nets robustness against adversarial perturbations. In practice adversarial training leads to low robust training loss. Howev…

cs.LG2019★ 204 cited

A Theoretical Analysis of Contrastive Unsupervised Representation Learning

Sanjeev Arora, Hrishikesh Khandeparkar, Mikhail Khodak +2

Recent empirical works have successfully used unlabeled data to learn feature representations that are broadly useful in downstream classification tasks. Several of these methods a…