3 citations · 5 across the 10 of their papers we have counts for
10 papers
The Hidden Power of Scaling Factor in LoRA Optimization
Zicheng Zhang, Haoran Li, Jiaxing Wang +10
In Low-Rank Adaptation (LoRA), the scaling factor is often treated as a mere complement to the learning rate, yet its role in optimization remains poorly understood. In this pa…
On the Tension Between Optimality and Adversarial Robustness in Policy Optimization
Haoran Li, Jiayu Lv, Congying Han +5
Achieving optimality and adversarial robustness in deep reinforcement learning has long been regarded as conflicting goals. Nonetheless, recent theoretical insights presented in CA…
Purity Law for Generalizable Neural TSP Solvers
Wenzhao Liu, Haoran Li, Congying Han +3
Achieving generalization in neural approaches across different scales and distributions remains a significant challenge for the Traveling Salesman Problem~(TSP). A key obstacle is…
Towards Optimal Adversarial Robust Reinforcement Learning with Infinity Measurement Error
Haoran Li, Zicheng Zhang, Wang Luo +4
Ensuring the robustness of deep reinforcement learning (DRL) agents against adversarial attacks is critical for their trustworthy deployment. Recent research highlights the challen…
Dual Alignment Maximin Optimization for Offline Model-based RL
Chi Zhou, Wang Luo, Haoran Li +3
Offline reinforcement learning agents face significant deployment challenges due to the synthetic-to-real distribution mismatch. While most prior research has focused on improving…
Mitigating Distribution Shift in Model-based Offline RL via Shifts-aware Reward Learning
Wang Luo, Haoran Li, Zicheng Zhang +4
Model-based offline reinforcement learning trains policies using pre-collected datasets and learned environment models, eliminating the need for direct real-world environment inter…
Towards Optimal Adversarial Robust Q-learning with Bellman Infinity-error
Haoran Li, Zicheng Zhang, Wang Luo +4
Establishing robust policies is essential to counter attacks or disturbances affecting deep reinforcement learning (DRL) agents. Recent studies explore state-adversarial robustness…
General Method for Solving Four Types of SAT Problems
Anqi Li, Congying Han, Tiande Guo +2
Existing methods provide varying algorithms for different types of Boolean satisfiability problems (SAT), lacking a general solution framework. Accordingly, this study proposes a u…
A-PSRO: A Unified Strategy Learning Method with Advantage Function for Normal-form Games
Yudong Hu, Haoran Li, Congying Han +3
Solving Nash equilibrium is the key challenge in normal-form games with large strategy spaces, where open-ended learning frameworks offer an efficient approach. In this work, we pr…
On the optimal pivot path of simplex method for linear programming based on reinforcement learning
Anqi Li, Tiande Guo, Congying Han +2
Based on the existing pivot rules, the simplex method for linear programming is not polynomial in the worst case. Therefore the optimal pivot of the simplex method is crucial. This…