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Neural Collapse Terminus: A Unified Solution for Class Incremental Learning and Its Variants
Yibo Yang, Haobo Yuan, Xiangtai Li +6
How to enable learnability for new classes while keeping the capability well on old classes has been a crucial challenge for class incremental learning. Beyond the normal case, lon…
Zeroth-order Optimization with Weak Dimension Dependency
Pengyun Yue, Long Yang, Cong Fang +1
Zeroth-order optimization is a fundamental research topic that has been a focus of various learning tasks, such as black-box adversarial attacks, bandits, and reinforcement learnin…
ShadowNet for Data-Centric Quantum System Learning
Yuxuan Du, Yibo Yang, Tongliang Liu +3
Understanding the dynamics of large quantum systems is hindered by the curse of dimensionality. Statistical learning offers new possibilities in this regime through neural network…
Task-Robust Pre-Training for Worst-Case Downstream Adaptation
Jianghui Wang, Yang Chen, Xingyu Xie +2
Pre-training has achieved remarkable success when transferred to downstream tasks. In machine learning, we care about not only the good performance of a model but also its behavior…
Policy Representation via Diffusion Probability Model for Reinforcement Learning
Long Yang, Zhixiong Huang, Fenghao Lei +6
Popular reinforcement learning (RL) algorithms tend to produce a unimodal policy distribution, which weakens the expressiveness of complicated policy and decays the ability of expl…
Code Prompting: a Neural Symbolic Method for Complex Reasoning in Large Language Models
Yi Hu, Haotong Yang, Zhouchen Lin +1
Large language models (LLMs) have scaled up to unlock a wide range of complex reasoning tasks with the aid of various prompting methods. However, current prompting methods generate…