5 papers
DiffNash: Finding Nash Equilibrium in High-Dimensional Continuous Action Spaces with Diffusion Policies
Haichuan Wang, Anagha Satish, Jiale Zheng +4
Computing equilibria in high-dimensional continuous games requires learning and representing rich, multi-modal strategy distributions, something conventional neural policies strugg…
Variance-Optimal Control Variates for Learning with Black-box Feedback
Zihao Zhao, Shuhan Zhang, Kai Wang
Modern models increasingly learn through black-box oracles such as humans, optimization solvers, and external tools that provide feedback without exposing their internal mechanisms…
Diffusion-DFL: Decision-focused Diffusion Models for Stochastic Optimization
Zihao Zhao, Christopher Yeh, Lingkai Kong +1
Decision-focused learning (DFL) integrates predictive modeling and optimization by training predictors to optimize the downstream decision target rather than merely minimizing pred…
What is the Right Notion of Distance between Predict-then-Optimize Tasks?
Paula Rodriguez-Diaz, Lingkai Kong, Kai Wang +2
Comparing datasets is a fundamental task in machine learning, essential for various learning paradigms-from evaluating train and test datasets for model generalization to using dat…
Aligning Large Language Models with Representation Editing: A Control Perspective
Lingkai Kong, Haorui Wang, Wenhao Mu +7
Aligning large language models (LLMs) with human objectives is crucial for real-world applications. However, fine-tuning LLMs for alignment often suffers from unstable training and…