6 citations · 34 across the 24 of their papers we have counts for
10 papers · 1 filter
CTRLS: Chain-of-Thought Reasoning via Latent State-Transition
Junda Wu, Yuxin Xiong, Xintong Li +7
Chain-of-thought (CoT) reasoning enables large language models (LLMs) to break down complex problems into interpretable intermediate steps, significantly enhancing model transparen…
From Selection to Generation: A Survey of LLM-based Active Learning
Yu Xia, Subhojyoti Mukherjee, Zhouhang Xie +31
Active Learning (AL) has been a powerful paradigm for improving model efficiency and performance by selecting the most informative data points for labeling and training. In recent…
Fast and scalable Wasserstein-1 neural optimal transport solver for single-cell perturbation prediction
Yanshuo Chen, Zhengmian Hu, Wei Chen +1
\textbf{Motivation:} Predicting single-cell perturbation responses requires mapping between two unpaired single-cell data distributions. Optimal transport (OT) theory provides a pr…
Solving a Class of Non-Convex Minimax Optimization in Federated Learning
Xidong Wu, Jianhui Sun, Zhengmian Hu +2
The minimax problems arise throughout machine learning applications, ranging from adversarial training and policy evaluation in reinforcement learning to AUROC maximization. To add…
Federated Conditional Stochastic Optimization
Xidong Wu, Jianhui Sun, Zhengmian Hu +3
Conditional stochastic optimization has found applications in a wide range of machine learning tasks, such as invariant learning, AUPRC maximization, and meta-learning. As the dema…
Serverless Federated AUPRC Optimization for Multi-Party Collaborative Imbalanced Data Mining
Xidong Wu, Zhengmian Hu, Jian Pei +1
Multi-party collaborative training, such as distributed learning and federated learning, is used to address the big data challenges. However, traditional multi-party collaborative…
Decentralized Riemannian Algorithm for Nonconvex Minimax Problems
Xidong Wu, Zhengmian Hu, Heng Huang
The minimax optimization over Riemannian manifolds (possibly nonconvex constraints) has been actively applied to solve many problems, such as robust dimensionality reduction and de…
Faster Adaptive Federated Learning
Xidong Wu, Feihu Huang, Zhengmian Hu +1
Federated learning has attracted increasing attention with the emergence of distributed data. While extensive federated learning algorithms have been proposed for the non-convex di…
Fast and Scalable Adversarial Training of Kernel SVM via Doubly Stochastic Gradients
Huimin Wu, Zhengmian Hu, Bin Gu
Adversarial attacks by generating examples which are almost indistinguishable from natural examples, pose a serious threat to learning models. Defending against adversarial attacks…
A New Framework for Variance-Reduced Hamiltonian Monte Carlo
Zhengmian Hu, Feihu Huang, Heng Huang
We propose a new framework of variance-reduced Hamiltonian Monte Carlo (HMC) methods for sampling from an -smooth and -strongly log-concave distribution, based on a unified f…