1 citations · 1 across the 5 of their papers we have counts for
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On the optimal regret of collaborative personalized linear bandits
Bruce Huang, Ruida Zhou, Lin F. Yang +1
Stochastic linear bandits are a fundamental model for sequential decision making, where an agent selects a vector-valued action and receives a noisy reward with expected value give…
SPIRE: Conditional Personalization for Federated Diffusion Generative Models
Kaan Ozkara, Ruida Zhou, Suhas Diggavi
Recent advances in diffusion models have revolutionized generative AI, but their sheer size makes on device personalization, and thus effective federated learning (FL), infeasible.…
Reframing Data Value for Large Language Models Through the Lens of Plausibility
Mohamad Rida Rammal, Ruida Zhou, Suhas Diggavi
Data valuation seeks to answer the important question, "How much is this data worth?" Existing data valuation methods have largely focused on discriminative models, primarily exami…
An Information-Theoretic Approach to Understanding Transformers' In-Context Learning of Variable-Order Markov Chains
Ruida Zhou, Chao Tian, Suhas Diggavi
We study transformers' in-context learning of variable-length Markov chains (VOMCs), focusing on the finite-sample accuracy as the number of in-context examples increases. Compared…
ADEPT: Hierarchical Bayes Approach to Personalized Federated Unsupervised Learning
Kaan Ozkara, Bruce Huang, Ruida Zhou +1
Statistical heterogeneity of clients' local data is an important characteristic in federated learning, motivating personalized algorithms tailored to the local data statistics. Tho…