12 citations · 31 across the 14 of their papers we have counts for
14 papers
Adaptive Estimation and Learning under Temporal Distribution Shift
Dheeraj Baby, Yifei Tang, Hieu Duy Nguyen +2
In this paper, we study the problem of estimation and learning under temporal distribution shift. Consider an observation sequence of length , which is a noisy realization of a…
Adapting to Online Distribution Shifts in Deep Learning: A Black-Box Approach
Dheeraj Baby, Boran Han, Shuai Zhang +3
We study the well-motivated problem of online distribution shift in which the data arrive in batches and the distribution of each batch can change arbitrarily over time. Since the…
Online Matrix Completion: A Collaborative Approach with Hott Items
Dheeraj Baby, Soumyabrata Pal
We investigate the low rank matrix completion problem in an online setting with users, items, rounds, and an unknown rank- reward matrix ${R}\in \mathbb{R}^{{M…
Online Feature Updates Improve Online (Generalized) Label Shift Adaptation
Ruihan Wu, Siddhartha Datta, Yi Su +3
This paper addresses the prevalent issue of label shift in an online setting with missing labels, where data distributions change over time and obtaining timely labels is challengi…
Near Optimal Heteroscedastic Regression with Symbiotic Learning
Dheeraj Baby, Aniket Das, Dheeraj Nagaraj +1
We consider the problem of heteroscedastic linear regression, where, given samples from $y_i = \langle \mathbf{w}^{*}, \mathbf{x}_i \rangle + ε_i \cdot \l…
Online Label Shift: Optimal Dynamic Regret meets Practical Algorithms
Dheeraj Baby, Saurabh Garg, Tzu-Ching Yen +3
This paper focuses on supervised and unsupervised online label shift, where the class marginals varies but the class-conditionals remain invariant. In the unsupervi…
Optimal Dynamic Regret in LQR Control
Dheeraj Baby, Yu-Xiang Wang
We consider the problem of nonstochastic control with a sequence of quadratic losses, i.e., LQR control. We provide an efficient online algorithm that achieves an optimal dynamic (…
Second Order Path Variationals in Non-Stationary Online Learning
Dheeraj Baby, Yu-Xiang Wang
We consider the problem of universal dynamic regret minimization under exp-concave and smooth losses. We show that appropriately designed Strongly Adaptive algorithms achieve a dyn…
Optimal Dynamic Regret in Proper Online Learning with Strongly Convex Losses and Beyond
Dheeraj Baby, Yu-Xiang Wang
We study the framework of universal dynamic regret minimization with strongly convex losses. We answer an open problem in Baby and Wang 2021 by showing that in a proper learning se…
Dynamic Regret for Strongly Adaptive Methods and Optimality of Online KRR
Dheeraj Baby, Hilaf Hasson, Yuyang Wang
We consider the framework of non-stationary Online Convex Optimization where a learner seeks to control its dynamic regret against an arbitrary sequence of comparators. When the lo…
Optimal Dynamic Regret in Exp-Concave Online Learning
Dheeraj Baby, Yu-Xiang Wang
We consider the problem of the Zinkevich (2003)-style dynamic regret minimization in online learning with exp-concave losses. We show that whenever improper learning is allowed, a…
An Optimal Reduction of TV-Denoising to Adaptive Online Learning
Dheeraj Baby, Xuandong Zhao, Yu-Xiang Wang
We consider the problem of estimating a function from noisy samples whose discrete Total Variation (TV) is bounded by . We reveal a deep connection to the seemingly dispar…
Adaptive Online Estimation of Piecewise Polynomial Trends
Dheeraj Baby, Yu-Xiang Wang
We consider the framework of non-stationary stochastic optimization [Besbes et al, 2015] with squared error losses and noisy gradient feedback where the dynamic regret of an online…
Online Forecasting of Total-Variation-bounded Sequences
Dheeraj Baby, Yu-Xiang Wang
We consider the problem of online forecasting of sequences of length with total-variation at most using observations contaminated by independent -subgaussian noise. We…