activity
20232026
most citedOptimization Landscape of Policy Gradient Methods for Discrete-time Static Output Feedback

10 citations · 18 across the 22 of their papers we have counts for

collaborators

38 papers

cs.LG2026

Momentum as Residual-Driven Multiplier Correction for Deep Learning Optimization

Zhixin Ren, Yao Lyu, Congrong Li +2

Momentum-based optimizers are widely used in modern deep learning, yet the relations among momentum recursion, update geometry, and acceleration remain only partially understood. W…

quant-ph2026

Optimal copy complexity of quantum state cloning

Sangwoo Jeon, Vaughn Sohn, Changhun Oh

Quantum state cloning is the task of approximately producing additional copies of an unknown quantum state from a finite number of input copies. The optimal cloning fidelity is kno…

cs.LG2026

On the Identifiability of Controlled World Models

Xiangteng Zhang, Yang Guan, Bo Zhang +3

World model serves as a promising tool to infer environment dynamics under high-dimensional observations and candidate actions. Recently, LeCun's JEPA provides a compelling framewo…

cs.LG2026

Distributional Soft Bellman Operator under the Cramér Geometry

Keru Wang, Yixin Deng, Yao Lyu +2

Distributional soft policy iteration (DSPI) provides an important framework for combining distributional reinforcement learning (DRL) with maximum-entropy control, in which the pol…

cs.LG2026

FAST: A Framework for Aligned Sampling and Training in Parallel Reinforcement Learning for Autonomous Driving

Bonan Wang, Letian Tao, Bin Shuai +7

Deep reinforcement learning is pivotal for closed-loop autonomous driving yet remains constrained by severe bottlenecks in sampling efficiency. Standard parallel sampling mitigates…

cs.RO2026

Factor-Aware Mixture-of-Experts with Pretrained Encoder for Combinatorial Generalization

Feihong Zhang, Guojian Zhan, Zeyu He +8

The integration of pretrained encoders with diffusion policies has become a dominant paradigm for visual robotic manipulation. However, it still struggles to generalize across comp…