30 citations · 133 across the 29 of their papers we have counts for
29 papers
AeroSketch: Near-Optimal Time Matrix Sketch Framework for Persistent, Sliding Window, and Distributed Streams
Hanyan Yin, Dongxie Wen, Jiajun Li +4
Many real-world matrix datasets arrive as high-throughput vector streams, making it impractical to store or process them in their entirety. To enable real-time analytics under limi…
Revisiting Weighted Strategy for Non-stationary Parametric Bandits and MDPs
Jing Wang, Peng Zhao, Zhi-Hua Zhou
Non-stationary parametric bandits have attracted much attention recently. There are three principled ways to deal with non-stationarity, including sliding-window, weighted, and res…
TreeLoRA: Efficient Continual Learning via Layer-Wise LoRAs Guided by a Hierarchical Gradient-Similarity Tree
Yu-Yang Qian, Yuan-Ze Xu, Zhen-Yu Zhang +2
Many real-world applications collect data in a streaming environment, where learning tasks are encountered sequentially. This necessitates continual learning (CL) to update models…
Provably Efficient Online RLHF with One-Pass Reward Modeling
Long-Fei Li, Yu-Yang Qian, Peng Zhao +1
Reinforcement Learning from Human Feedback (RLHF) has shown remarkable success in aligning Large Language Models (LLMs) with human preferences. Traditional RLHF methods rely on a f…
Near-Optimal Dynamic Regret for Adversarial Linear Mixture MDPs
Long-Fei Li, Peng Zhao, Zhi-Hua Zhou
We study episodic linear mixture MDPs with the unknown transition and adversarial rewards under full-information feedback, employing dynamic regret as the performance measure. We s…
Gradient-Variation Online Learning under Generalized Smoothness
Yan-Feng Xie, Peng Zhao, Zhi-Hua Zhou
Gradient-variation online learning aims to achieve regret guarantees that scale with variations in the gradients of online functions, which has been shown to be crucial for attaini…
Provably Efficient Reinforcement Learning with Multinomial Logit Function Approximation
Long-Fei Li, Yu-Jie Zhang, Peng Zhao +1
We study a new class of MDPs that employs multinomial logit (MNL) function approximation to ensure valid probability distributions over the state space. Despite its significant ben…
Improved Algorithm for Adversarial Linear Mixture MDPs with Bandit Feedback and Unknown Transition
Long-Fei Li, Peng Zhao, Zhi-Hua Zhou
We study reinforcement learning with linear function approximation, unknown transition, and adversarial losses in the bandit feedback setting. Specifically, we focus on linear mixt…
Efficient Methods for Non-stationary Online Learning
Peng Zhao, Yan-Feng Xie, Lijun Zhang +1
Non-stationary online learning has drawn much attention in recent years. In particular, dynamic regret and adaptive regret are proposed as two principled performance measures for o…
Universal Online Learning with Gradient Variations: A Multi-layer Online Ensemble Approach
Yu-Hu Yan, Peng Zhao, Zhi-Hua Zhou
In this paper, we propose an online convex optimization approach with two different levels of adaptivity. On a higher level, our approach is agnostic to the unknown types and curva…
Revisiting Weighted Strategy for Non-stationary Parametric Bandits
Jing Wang, Peng Zhao, Zhi-Hua Zhou
Non-stationary parametric bandits have attracted much attention recently. There are three principled ways to deal with non-stationarity, including sliding-window, weighted, and res…
Optimistic Online Mirror Descent for Bridging Stochastic and Adversarial Online Convex Optimization
Sijia Chen, Yu-Jie Zhang, Wei-Wei Tu +2
Stochastically Extended Adversarial (SEA) model is introduced by Sachs et al. [2022] as an interpolation between stochastic and adversarial online convex optimization. Under the sm…
Adapting to Continuous Covariate Shift via Online Density Ratio Estimation
Yu-Jie Zhang, Zhen-Yu Zhang, Peng Zhao +1
Dealing with distribution shifts is one of the central challenges for modern machine learning. One fundamental situation is the covariate shift, where the input distributions of da…
Dynamic Regret of Online Markov Decision Processes
Peng Zhao, Long-Fei Li, Zhi-Hua Zhou
We investigate online Markov Decision Processes (MDPs) with adversarially changing loss functions and known transitions. We choose dynamic regret as the performance measure, define…
Adapting to Online Label Shift with Provable Guarantees
Yong Bai, Yu-Jie Zhang, Peng Zhao +2
The standard supervised learning paradigm works effectively when training data shares the same distribution as the upcoming testing samples. However, this stationary assumption is…
Corralling a Larger Band of Bandits: A Case Study on Switching Regret for Linear Bandits
Haipeng Luo, Mengxiao Zhang, Peng Zhao +1
We consider the problem of combining and learning over a set of adversarial bandit algorithms with the goal of adaptively tracking the best one on the fly. The CORRAL algorithm of…
No-Regret Learning in Time-Varying Zero-Sum Games
Mengxiao Zhang, Peng Zhao, Haipeng Luo +1
Learning from repeated play in a fixed two-player zero-sum game is a classic problem in game theory and online learning. We consider a variant of this problem where the game payoff…
Adaptivity and Non-stationarity: Problem-dependent Dynamic Regret for Online Convex Optimization
Peng Zhao, Yu-Jie Zhang, Lijun Zhang +1
We investigate online convex optimization in non-stationary environments and choose dynamic regret as the performance measure, defined as the difference between cumulative loss inc…
Optimal Rates of (Locally) Differentially Private Heavy-tailed Multi-Armed Bandits
Youming Tao, Yulian Wu, Peng Zhao +1
In this paper we investigate the problem of stochastic multi-armed bandits (MAB) in the (local) differential privacy (DP/LDP) model. Unlike previous results that assume bounded/sub…
A Simple Approach for Non-stationary Linear Bandits
Peng Zhao, Lijun Zhang, Yuan Jiang +1
This paper investigates the problem of non-stationary linear bandits, where the unknown regression parameter is evolving over time. Existing studies develop various algorithms and…
Non-stationary Online Learning with Memory and Non-stochastic Control
Peng Zhao, Yu-Hu Yan, Yu-Xiang Wang +1
We study the problem of Online Convex Optimization (OCO) with memory, which allows loss functions to depend on past decisions and thus captures temporal effects of learning problem…
Storage Fit Learning with Feature Evolvable Streams
Bo-Jian Hou, Yu-Hu Yan, Peng Zhao +1
Feature evolvable learning has been widely studied in recent years where old features will vanish and new features will emerge when learning with streams. Conventional methods usua…
Dynamic Regret of Convex and Smooth Functions
Peng Zhao, Yu-Jie Zhang, Lijun Zhang +1
We investigate online convex optimization in non-stationary environments and choose the dynamic regret as the performance measure, defined as the difference between cumulative loss…
Improved Analysis for Dynamic Regret of Strongly Convex and Smooth Functions
Peng Zhao, Lijun Zhang
In this paper, we present an improved analysis for dynamic regret of strongly convex and smooth functions. Specifically, we investigate the Online Multiple Gradient Descent (OMGD)…