688 citations · 1.9k across the 57 of their papers we have counts for
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Towards Statistical and Computational Complexities of Polyak Step Size Gradient Descent
Tongzheng Ren, Fuheng Cui, Alexia Atsidakou +2
We study the statistical and computational complexities of the Polyak step size gradient descent algorithm under generalized smoothness and Lojasiewicz conditions of the population…
DISCO : efficient unsupervised decoding for discrete natural language problems via convex relaxation
Anish Acharya, Rudrajit Das
In this paper we study test time decoding; an ubiquitous step in almost all sequential text generation task spanning across a wide array of natural language processing (NLP) proble…
Robust Training in High Dimensions via Block Coordinate Geometric Median Descent
Anish Acharya, Abolfazl Hashemi, Prateek Jain +3
Geometric median (\textsc{Gm}) is a classical method in statistics for achieving a robust estimation of the uncorrupted data; under gross corruption, it achieves the optimal breakd…
On the Convergence of Differentially Private Federated Learning on Non-Lipschitz Objectives, and with Normalized Client Updates
Rudrajit Das, Abolfazl Hashemi, Sujay Sanghavi +1
There is a dearth of convergence results for differentially private federated learning (FL) with non-Lipschitz objective functions (i.e., when gradient norms are not bounded). The…
Enabling Efficiency-Precision Trade-offs for Label Trees in Extreme Classification
Tavor Z. Baharav, Daniel L. Jiang, Kedarnath Kolluri +2
Extreme multi-label classification (XMC) aims to learn a model that can tag data points with a subset of relevant labels from an extremely large label set. Real world e-commerce ap…
Nearly Horizon-Free Offline Reinforcement Learning
Tongzheng Ren, Jialian Li, Bo Dai +2
We revisit offline reinforcement learning on episodic time-homogeneous Markov Decision Processes (MDP). For tabular MDP with states and actions, or linear MDP with anchor p…