11 papers · 1 filter
FedSteer: Taming Extreme Gradient Staleness in Federated Learning with Corrective Projections and Caching
Haoran Zhang, Cainã Figueiredo Pereira, Marie Siew +3
Federated learning (FL) is often subject to aggregation variance if clients do not consistently participate in training rounds. While reusing stale model updates from inactive clie…
Unlearning Offline Stochastic Multi-Armed Bandits
Zichun Ye, Runqi Wang, Xuchuang Wang +3
Machine unlearning aims to unlearn data points from a learned model, offering a principled way to process data-deletion requests and mitigate privacy risks without full retraining.…
Scaling Federated Linear Contextual Bandits via Sketching
Hantao Yang, Hong Xie, Xutong Liu +1
In federated contextual linear bandits, high data dimensionality incurs prohibitive computation and communication costs: local agents perform -time determinant computation…
Continuous Semantic Caching for Low-Cost LLM Serving
Baran Atalar, Xutong Liu, Jinhang Zuo +3
As Large Language Models (LLMs) become increasingly popular, caching responses so that they can be reused by users with semantically similar queries has become a vital strategy for…
Steering Frozen LLMs: Adaptive Social Alignment via Online Prompt Routing
Zeyu Zhang, Xiangxiang Dai, Ziyi Han +2
Large language models (LLMs) are typically governed by post-training alignment (e.g., RLHF or DPO), which yields a largely static policy during deployment and inference. However, r…
Online Learning to Rank under Corruption: A Robust Cascading Bandits Approach
Fatemeh Ghaffari, Siddarth Sitaraman, Xutong Liu +2
Online learning to rank (OLTR) studies how to recommend a short ranked list of items from a large pool and improves future rankings based on user clicks. This setting is commonly m…
Offline Clustering of Preference Learning with Active-data Augmentation
Jingyuan Liu, Fatemeh Ghaffari, Xuchuang Wang +3
Preference learning from pairwise feedback is a widely adopted framework in applications such as reinforcement learning with human feedback and recommendations. In many practical s…
Near-Optimal Regret for Efficient Stochastic Combinatorial Semi-Bandits
Zichun Ye, Runqi Wang, Xutong Liu +1
The combinatorial multi-armed bandit (CMAB) is a cornerstone of sequential decision-making framework, dominated by two algorithmic families: UCB-based and adversarial methods such…
Offline Clustering of Linear Bandits: The Power of Clusters under Limited Data
Jingyuan Liu, Zeyu Zhang, Xuchuang Wang +4
Contextual multi-armed bandit is a fundamental learning framework for making a sequence of decisions, e.g., advertising recommendations for a sequence of arriving users. Recent wor…
Fusing Reward and Dueling Feedback in Stochastic Bandits
Xuchuang Wang, Qirun Zeng, Jinhang Zuo +4
This paper investigates the fusion of absolute (reward) and relative (dueling) feedback in stochastic bandits, where both feedback types are gathered in each decision round. We der…
Heterogeneous Multi-agent Multi-armed Bandits on Stochastic Block Models
Mengfan Xu, Liren Shan, Fatemeh Ghaffari +3
We study a novel heterogeneous multi-agent multi-armed bandit problem with a cluster structure induced by stochastic block models, influencing not only graph topology, but also rew…