7 citations · 10 across the 8 of their papers we have counts for
8 papers
SubZero+: Memory-Efficient Adaptive Zeroth-Order LLM Fine-Tuning in Random Subspaces
Ziming Yu, Shuyao Xiao, Xingyu Zhao +6
Zeroth-order (ZO) optimization with SGD in random subspaces enables memory-efficient fine-tuning of large language models without backpropagation. However, high gradient estimation…
AR1-ZO: Topology-Aware Rank-1 Zeroth-Order Queries for High-Rank LoRA Fine-Tuning
Ziye Chen, Hongbin Lin, Chenyu Zhang +3
Zeroth-order (ZO) optimization enables large-language-model fine-tuning without storing backpropagation activations, while LoRA supplies compact trainable adapters. Combining them…
Zeroth-Order Optimization is Secretly Single-Step Policy Optimization
Junbin Qiu, Zhengpeng Xie, Xiangda Yan +2
Zeroth-Order Optimization (ZOO) provides powerful tools for optimizing functions where explicit gradients are unavailable or expensive to compute. However, the underlying mechanism…
Approximating N-Player Nash Equilibrium through Gradient Descent
Dongge Wang, Xiang Yan, Zehao Dou +3
Decoding how rational agents should behave in shared systems remains a critical challenge within theoretical computer science, artificial intelligence and economics studies. Centra…
Coordinated Dynamic Bidding in Repeated Second-Price Auctions with Budgets
Yurong Chen, Qian Wang, Zhijian Duan +4
In online ad markets, a rising number of advertisers are employing bidding agencies to participate in ad auctions. These agencies are specialized in designing online algorithms and…
On the Convergence of Fictitious Play: A Decomposition Approach
Yurong Chen, Xiaotie Deng, Chenchen Li +4
Fictitious play (FP) is one of the most fundamental game-theoretical learning frameworks for computing Nash equilibrium in -player games, which builds the foundation for modern…
A Context-Integrated Transformer-Based Neural Network for Auction Design
Zhijian Duan, Jingwu Tang, Yutong Yin +4
One of the central problems in auction design is developing an incentive-compatible mechanism that maximizes the auctioneer's expected revenue. While theoretical approaches have en…
A Game-Theoretic Analysis of the Empirical Revenue Maximization Algorithm with Endogenous Sampling
Xiaotie Deng, Ron Lavi, Tao Lin +3
The Empirical Revenue Maximization (ERM) is one of the most important price learning algorithms in auction design: as the literature shows it can learn approximately optimal reserv…