9 papers
Action-Conditioned Risk Gating for Safety-Critical Control under Partial Observability
Yushen Liu, Yin-Jen Chen, Ziyi Chen +4
Many safety-critical control problems are modeled as risk-sensitive partially observable Markov decision processes, where the controller must make decisions from incomplete observa…
RNA-FM: Flow-Matching Generative Model for Genome-wide RNA-Seq Prediction
Yaxuan Song, Jianan Fan, Tianyi Wang +4
Histopathology whole-slide images (WSIs) are routinely acquired in clinical practice and contain rich tissue morphology but lack direct molecular architecture and functional progra…
FedAttr: Towards Privacy-preserving Client-Level Attribution in Federated LLM Fine-tuning
Su Zhang, Junfeng Guo, Heng Huang
Watermark radioactivity testing type of methods can detect whether a model was trained on watermarked documents, and have become key tools for protecting data ownership in the fine…
From Dual Tracking to Clipping: Provably Faster Distributionally Robust Multi-Objective Optimization
Yufeng Yang, Fangning Zhuo, Ziyi Chen +2
Multi-objective optimization (MOO) has received growing attention in applications that require learning under multiple criteria. However, most existing MOO formulations do not expl…
Provably Efficient Algorithms for S- and Non-Rectangular Robust MDPs with General Parameterization
Anirudh Satheesh, Ziyi Chen, Furong Huang +1
We study robust Markov decision processes (RMDPs) with general policy parameterization under s-rectangular and non-rectangular uncertainty sets. Prior work is largely limited to ta…
Zeroth-Order Methods for Stochastic Nonconvex Nonsmooth Composite Optimization
Ziyi Chen, Peiran Yu, Heng Huang
This work aims to solve a stochastic nonconvex nonsmooth composite optimization problem. Previous works on composite optimization problem requires the major part to satisfy Lipschi…
Trade-off in Estimating the Number of Byzantine Clients in Federated Learning
Ziyi Chen, Su Zhang, Heng Huang
Federated learning has attracted increasing attention at recent large-scale optimization and machine learning research and applications, but is also vulnerable to Byzantine clients…
Achieve Performatively Optimal Policy for Performative Reinforcement Learning
Ziyi Chen, Heng Huang
Performative reinforcement learning is an emerging dynamical decision making framework, which extends reinforcement learning to the common applications where the agent's policy can…
Revisiting Convergence: Shuffling Complexity Beyond Lipschitz Smoothness
Qi He, Peiran Yu, Ziyi Chen +1
Shuffling-type gradient methods are favored in practice for their simplicity and rapid empirical performance. Despite extensive development of convergence guarantees under various…