5 citations · 5 across the 1 of their papers we have counts for
6 papers
Adversarial Rademacher Complexity of Deep Neural Networks
Jiancong Xiao, Yanbo Fan, Ruoyu Sun +1
Deep neural networks (DNNs) are highly vulnerable to adversarial attacks. Ideally, a robust model should perform well on both perturbed training data and unseen perturbed test data…
Understanding Adversarial Imitation Learning in Small Sample Regime: A Stage-coupled Analysis
Tian Xu, Ziniu Li, Yang Yu +1
Imitation learning learns a policy from expert trajectories. While the expert data is believed to be crucial for imitation quality, it was found that a kind of imitation learning a…
Knapsack RL: Unlocking Exploration of LLMs via Optimizing Budget Allocation
Ziniu Li, Congliang Chen, Tianyun Yang +5
Large Language Models (LLMs) can self-improve through reinforcement learning, where they generate trajectories to explore and discover better solutions. However, this exploration p…
Preserving Diversity in Supervised Fine-Tuning of Large Language Models
Ziniu Li, Congliang Chen, Tian Xu +4
Large Language Models (LLMs) typically rely on Supervised Fine-Tuning (SFT) to specialize in downstream tasks, with the Cross Entropy (CE) loss being the de facto choice. However,…
Adam-mini: Use Fewer Learning Rates To Gain More
Yushun Zhang, Congliang Chen, Ziniu Li +6
We propose Adam-mini, an optimizer that achieves on par or better performance than AdamW with 50% less memory footprint. Adam-mini reduces memory by cutting down the learning rate…
Exploring the Generalization Capabilities of AID-based Bi-level Optimization
Congliang Chen, Li Shen, Zhiqiang Xu +3
Bi-level optimization has achieved considerable success in contemporary machine learning applications, especially for given proper hyperparameters. However, due to the two-level op…