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20212024
most citedDelayed Propagation Transformer: A Universal Computation Engine towards Practical Control in Cyber-Physical Systems

8 citations · 32 across the 11 of their papers we have counts for

collaborators

12 papers

cs.LG2024★ 7 cited

Revisiting Zeroth-Order Optimization for Memory-Efficient LLM Fine-Tuning: A Benchmark

Yihua Zhang, Pingzhi Li, Junyuan Hong +10

In the evolving landscape of natural language processing (NLP), fine-tuning pre-trained Large Language Models (LLMs) with first-order (FO) optimizers like SGD and Adam has become s…

cs.PL2023★ 1 cited

Outline, Then Details: Syntactically Guided Coarse-To-Fine Code Generation

Wenqing Zheng, S P Sharan, Ajay Kumar Jaiswal +4

For a complicated algorithm, its implementation by a human programmer usually starts with outlining a rough control flow followed by iterative enrichments, eventually yielding care…

cs.LG2023

You Only Transfer What You Share: Intersection-Induced Graph Transfer Learning for Link Prediction

Wenqing Zheng, Edward W Huang, Nikhil Rao +2

Link prediction is central to many real-world applications, but its performance may be hampered when the graph of interest is sparse. To alleviate issues caused by sparsity, we inv…

cs.LG2022

Symbolic Visual Reinforcement Learning: A Scalable Framework with Object-Level Abstraction and Differentiable Expression Search

Wenqing Zheng, S P Sharan, Zhiwen Fan +3

Learning efficient and interpretable policies has been a challenging task in reinforcement learning (RL), particularly in the visual RL setting with complex scenes. While neural ne…

cs.IR2022★ 1 cited

Search Behavior Prediction: A Hypergraph Perspective

Yan Han, Edward W Huang, Wenqing Zheng +3

Although the bipartite shopping graphs are straightforward to model search behavior, they suffer from two challenges: 1) The majority of items are sporadically searched and hence h…

cs.LG2022★ 3 cited

Symbolic Distillation for Learned TCP Congestion Control

S P Sharan, Wenqing Zheng, Kuo-Feng Hsu +3

Recent advances in TCP congestion control (CC) have achieved tremendous success with deep reinforcement learning (RL) approaches, which use feedforward neural networks (NN) to lear…