9 citations · 24 across the 13 of their papers we have counts for
17 papers
Condition-Contrastive Extrapolation for On-Policy Distillation of Language Models
Wenxuan Luo, Chenxi Li, Haoyue Liu +2
On-policy distillation (OPD) improves a student language model by training on its own generated trajectories with supervision from a stronger teacher. However, when conditioned on…
Select Smarter, Not More: Prompt-Aware Evaluation Scheduling with Submodular Guarantees
Xiaoyu Ma, Yiwen Li, Haoyue Liu +4
Automatic prompt optimization (APO) hinges on the quality of its evaluation signal, yet scoring every prompt candidate on the full training set is prohibitively expensive. Existing…
Adaptive Prompt Structure Factorization: A Framework for Self-Discovering and Optimizing Compositional Prompt Programs
Haoyue Liu, Zhichao Wang, Yongxin Guo +2
Automated prompt optimization is crucial for eliciting reliable reasoning from large language models (LLMs), yet most API-only prompt optimizers iteratively edit monolithic prompts…
PIDP-Attack: Combining Prompt Injection with Database Poisoning Attacks on Retrieval-Augmented Generation Systems
Haozhen Wang, Haoyue Liu, Jionghao Zhu +3
Large Language Models (LLMs) have demonstrated remarkable performance across a wide range of applications. However, their practical deployment is often hindered by issues such as o…
Multi-Physics: A Comprehensive Benchmark for Multimodal LLMs Reasoning on Chinese Multi-Subject Physics Problems
Zhongze Luo, Zhenshuai Yin, Yongxin Guo +3
While multimodal LLMs (MLLMs) demonstrate remarkable reasoning progress, their application in specialized scientific domains like physics reveals significant gaps in current evalua…
GRPO-A: Guided Group Relative Policy Optimization with Adaptive Guidance
Yongxin Guo, Wenbo Deng, Zhenglin Cheng +1
Reinforcement Learning with Verifiable Rewards (RLVR) has markedly enhanced the reasoning abilities of large language models (LLMs). Its success, however, largely depends on strong…
FedMABA: Towards Fair Federated Learning through Multi-Armed Bandits Allocation
Zhichao Wang, Lin Wang, Yongxin Guo +2
The increasing concern for data privacy has driven the rapid development of federated learning (FL), a privacy-preserving collaborative paradigm. However, the statistical heterogen…
Smart Sampling: Helping from Friendly Neighbors for Decentralized Federated Learning
Lin Wang, Yang Chen, Yongxin Guo +1
Federated Learning (FL) is gaining widespread interest for its ability to share knowledge while preserving privacy and reducing communication costs. Unlike Centralized FL, Decentra…
Client2Vec: Improving Federated Learning by Distribution Shifts Aware Client Indexing
Yongxin Guo, Lin Wang, Xiaoying Tang +1
Federated Learning (FL) is a privacy-preserving distributed machine learning paradigm. Nonetheless, the substantial distribution shifts among clients pose a considerable challenge…
Dynamic Mixture of Experts: An Auto-Tuning Approach for Efficient Transformer Models
Yongxin Guo, Zhenglin Cheng, Xiaoying Tang +2
The Sparse Mixture of Experts (SMoE) has been widely employed to enhance the efficiency of training and inference for Transformer-based foundational models, yielding promising resu…
VTG-LLM: Integrating Timestamp Knowledge into Video LLMs for Enhanced Video Temporal Grounding
Yongxin Guo, Jingyu Liu, Mingda Li +6
Video Temporal Grounding (VTG) strives to accurately pinpoint event timestamps in a specific video using linguistic queries, significantly impacting downstream tasks like video bro…
Enhancing Clustered Federated Learning: Integration of Strategies and Improved Methodologies
Yongxin Guo, Xiaoying Tang, Tao Lin
Federated Learning (FL) is an evolving distributed machine learning approach that safeguards client privacy by keeping data on edge devices. However, the variation in data among cl…
FedRC: Tackling Diverse Distribution Shifts Challenge in Federated Learning by Robust Clustering
Yongxin Guo, Xiaoying Tang, Tao Lin
Federated Learning (FL) is a machine learning paradigm that safeguards privacy by retaining client data on edge devices. However, optimizing FL in practice can be challenging due t…
InvNorm: Domain Generalization for Object Detection in Gastrointestinal Endoscopy
Weichen Fan, Yuanbo Yang, Kunpeng Qiu +2
Domain Generalization is a challenging topic in computer vision, especially in Gastrointestinal Endoscopy image analysis. Due to several device limitations and ethical reasons, cur…
DELTA: Diverse Client Sampling for Fasting Federated Learning
Lin Wang, YongXin Guo, Tao Lin +1
Partial client participation has been widely adopted in Federated Learning (FL) to reduce the communication burden efficiently. However, an inadequate client sampling scheme can le…
FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias Reduction
Yongxin Guo, Xiaoying Tang, Tao Lin
Federated Learning (FL) is a way for machines to learn from data that is kept locally, in order to protect the privacy of clients. This is typically done using local SGD, which hel…
Towards Federated Learning on Time-Evolving Heterogeneous Data
Yongxin Guo, Tao Lin, Xiaoying Tang
Federated Learning (FL) is a learning paradigm that protects privacy by keeping client data on edge devices. However, optimizing FL in practice can be difficult due to the diversit…