9 papers
Safeguarded Accelerated Adaptive Search under Unreliable Gradient Oracles
Shunzhi Zhang, Shichen Liao, Congying Han +1
From the probabilistic-model viewpoint, stochastic adaptive search can require any fraction of the predicted model reduction for acceptance. Classical backtracking with…
Relating Checkpoint Update Probabilities to Momentum Parameters in Single-Loop Variance Reduction Methods
Hai Liu, Tiande Guo, Congying Han
We propose a single-loop variance-reduced acceleration framework, which relates checkpoint update probabilities to momentum parameters, for solving the composite general convex pro…
A Fast Anti-Jamming Cognitive Radar Deployment Algorithm Based on Reinforcement Learning
Wencheng Cai, Xuchao Gao, Congying Han +2
The fast deployment of cognitive radar to counter jamming remains a critical challenge in modern warfare, where more efficient deployment leads to quicker detection of targets. Exi…
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…
A Mutil-conditional Diffusion Transformer for Versatile Seismic Wave Generation
Longfei Duan, Zicheng Zhang, Lianqing Zhou +4
Seismic wave generation creates labeled waveform datasets for source parameter inversion, subsurface analysis, and, notably, training artificial intelligence seismology models. Tra…
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…
DR-BFR: Degradation Representation with Diffusion Models for Blind Face Restoration
Xinmin Qiu, Bonan Li, Zicheng Zhang +2
Blind face restoration (BFR) is fundamentally challenged by the extensive range of degradation types and degrees that impact model generalization. Recent advancements in diffusion…