Data Market Design through Deep Learning. - Sai Srivatsa Ravindranath; Yanchen Jiang; David C. Parkes (NeurIPS, 2023)

GemNet: Menu-Based, Strategy-Proof Multi-Bidder Auctions Through Deep Learning. - Tonghan Wang 001; Yanchen Jiang; David C. Parkes (EC, 2024)

Charting the Shapes of Stories with Game Theory. - Constantinos Daskalakis; Ian Gemp; Yanchen Jiang; Renato Paes Leme; Christos H. Papadimitriou; Georgios Piliouras (CoRR, 2024)

On Diffusion Models for Multi-Agent Partial Observability: Shared Attractors, Error Bounds, and Composite Flow. - Tonghan Wang 001; Heng Dong 001; Yanchen Jiang; David C. Parkes; Milind Tambe (AAMAS, 2025)

Multi-stage stochastic programming for thermal power plant coal procurement considering long-term contract under uncertainties. - Jun Li; Yanchen Jiang; Qiansheng Li; Yongfu Wang (Comput. Chem. Eng., 2025)

Real-time traffic object detection algorithm with deep stochastic configuration networks. - Yongfu Wang; Yang Liu 388; Ran Yi; Yanchen Jiang (Inf. Sci., 2025)

LLM-Powered Preference Elicitation in Combinatorial Assignment. - Ermis Soumalias; Yanchen Jiang; Kehang Zhu; Michael J. Curry; Sven Seuken; David C. Parkes (CoRR, 2025)

BundleFlow: Deep Menus for Combinatorial Auctions by Diffusion-Based Optimization. - Tonghan Wang 001; Yanchen Jiang; David C. Parkes (CoRR, 2025)

Learning from Synthetic Labs: Language Models as Auction Participants. - Anand Shah; Kehang Zhu; Yanchen Jiang; Jeffrey G. Wang; Arif Kerem Dayi; John J. Horton; David C. Parkes (CoRR, 2025)