7 papers · 1 filter
A Simple, Optimal and Efficient Algorithm for Online Exp-Concave Optimization
Yi-Han Wang, Peng Zhao, Zhi-Hua Zhou
Online eXp-concave Optimization (OXO) is a fundamental problem in online learning, where the goal is to minimize regret when loss functions are exponentially concave. The standard…
Adaptivity and Universality: Problem-dependent Universal Regret for Online Convex Optimization
Peng Zhao, Yu-Hu Yan, Hang Yu +1
Universal online learning aims to achieve optimal regret guarantees without requiring prior knowledge of the curvature of online functions. Existing methods have established minima…
Optimistic Online-to-Batch Conversions for Accelerated Convergence and Universality
Yu-Hu Yan, Peng Zhao, Zhi-Hua Zhou
In this work, we study offline convex optimization with smooth objectives, where the classical Nesterov's Accelerated Gradient (NAG) method achieves the optimal accelerated converg…
Dual Adaptivity: Universal Algorithms for Minimizing the Adaptive Regret of Convex Functions
Lijun Zhang, Wenhao Yang, Guanghui Wang +2
To deal with changing environments, a new performance measure -- adaptive regret, defined as the maximum static regret over any interval, was proposed in online learning. Under the…
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…
Heavy-Tailed Linear Bandits: Huber Regression with One-Pass Update
Jing Wang, Yu-Jie Zhang, Peng Zhao +1
We study the stochastic linear bandits with heavy-tailed noise. Two principled strategies for handling heavy-tailed noise, truncation and median-of-means, have been introduced to h…
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…