activity
20192025
most citedMomentum-Based Policy Gradient Methods

3 citations · 5 across the 2 of their papers we have counts for

collaborators

5 papers

cs.LG2025

Cost-Aware Contrastive Routing for LLMs

Reza Shirkavand, Shangqian Gao, Peiran Yu +1

We study cost-aware routing for large language models across diverse and dynamic pools of models. Existing approaches often overlook prompt-specific context, rely on expensive mode…

cs.LG2024

Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models

Reza Shirkavand, Peiran Yu, Shangqian Gao +3

Recent advances in diffusion generative models have yielded remarkable progress. While the quality of generated content continues to improve, these models have grown considerably i…

cs.CV20222 cited

Interpretations Steered Network Pruning via Amortized Inferred Saliency Maps

Alireza Ganjdanesh, Shangqian Gao, Heng Huang

Convolutional Neural Networks (CNNs) compression is crucial to deploying these models in edge devices with limited resources. Existing channel pruning algorithms for CNNs have achi…

cs.LG20203 cited

Momentum-Based Policy Gradient Methods

Feihu Huang, Shangqian Gao, Jian Pei +1

In the paper, we propose a class of efficient momentum-based policy gradient methods for the model-free reinforcement learning, which use adaptive learning rates and do not require…

math.OC2019

Zeroth-Order Stochastic Alternating Direction Method of Multipliers for Nonconvex Nonsmooth Optimization

Feihu Huang, Shangqian Gao, Songcan Chen +1

Alternating direction method of multipliers (ADMM) is a popular optimization tool for the composite and constrained problems in machine learning. However, in many machine learning…