Incremental Association Rule Mining Based on Matrix Compression for Edge Computing. - Dongdai Zhou; Meng Ouyang; Zhejun Kuang; Zhen Li 021; Jin Peng Zhou; Xiaochun Cheng (IEEE Access, 2019)

Predicting Twitter Engagement With Deep Language Models. - Maksims Volkovs; Zhaoyue Cheng; Mathieu Ravaut; Hojin Yang; Kevin Shen; Jin Peng Zhou; Anson Wong; Saba Zuberi; Ivan Zhang; Nick Frosst; Helen Ngo; Carol Chen; et al. (RecSys Challenge, 2020)

TAFA: Two-headed Attention Fused Autoencoder for Context-Aware Recommendations. - Jin Peng Zhou; Zhaoyue Cheng; Felipe Pérez; Maksims Volkovs (RecSys, 2020)

Noise Contrastive Estimation for Autoencoding-based One-Class Collaborative Filtering. - Jin Peng Zhou; Ga Wu; Zheda Mai; Scott Sanner (CoRR, 2020)

Not My Deepfake: Towards Plausible Deniability for Machine-Generated Media. - Baiwu Zhang; Jin Peng Zhou; Ilia Shumailov; Nicolas Papernot (CoRR, 2020)

Bayesian Preference Elicitation with Keyphrase-Item Coembeddings for Interactive Recommendation. - Hojin Yang; Scott Sanner; Ga Wu; Jin Peng Zhou (UMAP, 2021)

Learned Systems Security. - Roei Schuster; Jin Peng Zhou; Thorsten Eisenhofer; Paul Grubbs; Nicolas Papernot (CoRR, 2022)

Does Label Differential Privacy Prevent Label Inference Attacks? - Ruihan Wu; Jin Peng Zhou; Kilian Q. Weinberger; Chuan Guo 001 (AISTATS, 2023)

Draft, Sketch, and Prove: Guiding Formal Theorem Provers with Informal Proofs. - Albert Qiaochu Jiang; Sean Welleck; Jin Peng Zhou; Timothée Lacroix; Jiacheng Liu 010; Wenda Li; Mateja Jamnik; Guillaume Lample; Yuhuai Wu (ICLR, 2023)

Unsupervised Out-of-Distribution Detection with Diffusion Inpainting. - Zhenzhen Liu; Jin Peng Zhou; Yufan Wang; Kilian Q. Weinberger (ICML, 2023)

Magnushammer: A Transformer-based Approach to Premise Selection. - Maciej Mikula; Szymon Antoniak; Szymon Tworkowski; Albert Qiaochu Jiang; Jin Peng Zhou; Christian Szegedy; Lukasz Kucinski; Piotr Milos; Yuhuai Wu (CoRR, 2023)

Correction with Backtracking Reduces Hallucination in Summarization. - Zhenzhen Liu; Chao Wan; Varsha Kishore; Jin Peng Zhou; Minmin Chen; Kilian Q. Weinberger (CoRR, 2023)

Magnushammer: A Transformer-Based Approach to Premise Selection. - Maciej Mikula; Szymon Tworkowski; Szymon Antoniak; Bartosz Piotrowski; Albert Q. Jiang; Jin Peng Zhou; Christian Szegedy; Lukasz Kucinski; Piotr Milos; Yuhuai Wu (ICLR, 2024)

Don't Trust: Verify - Grounding LLM Quantitative Reasoning with Autoformalization. - Jin Peng Zhou; Charles Staats; Wenda Li; Christian Szegedy; Kilian Q. Weinberger; Yuhuai Wu (ICLR, 2024)

REFACTOR: Learning to Extract Theorems from Proofs. - Jin Peng Zhou; Yuhuai Wu; Qiyang Li; Roger Baker Grosse (ICLR, 2024)

Zero-shot Object-Level OOD Detection with Context-Aware Inpainting. - Quang-Huy Nguyen; Jin Peng Zhou; Zhenzhen Liu; Khanh-Huyen Bui; Kilian Q. Weinberger; Dung D. Le (CoRR, 2024)

Code Repair with LLMs gives an Exploration-Exploitation Tradeoff. - Hao Tang 008; Keya Hu; Jin Peng Zhou; Sicheng Zhong; Wei-Long Zheng; Xujie Si; Kevin Ellis (CoRR, 2024)

Orchestrating LLMs with Different Personalizations. - Jin Peng Zhou; Katie Z. Luo; Jingwen Gu; Jason Yuan; Kilian Q. Weinberger; Wen Sun 002 (CoRR, 2024)

Gemma 2: Improving Open Language Models at a Practical Size. - Morgane Rivière; Shreya Pathak; Pier Giuseppe Sessa; Cassidy Hardin; Surya Bhupatiraju; Léonard Hussenot; Thomas Mesnard; Bobak Shahriari; Alexandre Ramé; Johan Ferret; Peter Liu; Pouya Tafti; et al. (CoRR, 2024)

Towards More Robust Retrieval-Augmented Generation: Evaluating RAG Under Adversarial Poisoning Attacks. - Jinyan Su; Jin Peng Zhou; Zhengxin Zhang; Preslav Nakov; Claire Cardie (CoRR, 2024)

Enhancing Cognitive Diagnosis by Modeling Learner Cognitive Structure State. - Zhifu Chen; Hengnian Gu; Jin Peng Zhou; Dongdai Zhou (CoRR, 2024)

Leveraging diffusion models for unsupervised out-of-distribution detection on image manifold. - Zhenzhen Liu; Jin Peng Zhou; Kilian Q. Weinberger (Frontiers Artif. Intell., 2024)

Rethinking LLM Unlearning Objectives: A Gradient Perspective and Go Beyond. - Qizhou Wang; Jin Peng Zhou; Zhanke Zhou; Saebyeol Shin; Bo Han 003; Kilian Q. Weinberger (ICLR, 2025)

On Speeding Up Language Model Evaluation. - Jin Peng Zhou; Christian K. Belardi; Ruihan Wu; Travis Zhang; Carla P. Gomes; Wen Sun 002; Kilian Q. Weinberger (ICLR, 2025)

Learned-Database Systems Security. - Roei Schuster; Jin Peng Zhou; Thorsten Eisenhofer; Paul Grubbs; Nicolas Papernot (Trans. Mach. Learn. Res., 2025)

Graders should cheat: privileged information enables expert-level automated evaluations. - Jin Peng Zhou; Sébastien M. R. Arnold; Nan Ding 002; Kilian Q. Weinberger; Nan Hua; Fei Sha (CoRR, 2025)

Q♯: Provably Optimal Distributional RL for LLM Post-Training. - Jin Peng Zhou; Kaiwen Wang; Jonathan D. Chang; Zhaolin Gao; Nathan Kallus; Kilian Q. Weinberger; Kianté Brantley; Wen Sun 002 (CoRR, 2025)

INPROVF: Leveraging Large Language Models to Repair High-level Robot Controllers from Assumption Violations. - Qian Meng; Jin Peng Zhou; Kilian Q. Weinberger; Hadas Kress-Gazit (CoRR, 2025)

Pre-training Large Memory Language Models with Internal and External Knowledge. - Linxi Zhao; Sofian Zalouk; Christian K. Belardi; Justin Lovelace; Jin Peng Zhou; Kilian Q. Weinberger; Yoav Artzi; Jennifer J. Sun (CoRR, 2025)

Value-Guided Search for Efficient Chain-of-Thought Reasoning. - Kaiwen Wang; Jin Peng Zhou; Jonathan D. Chang; Zhaolin Gao; Nathan Kallus; Kianté Brantley; Wen Sun 002 (CoRR, 2025)

Efficient Controllable Diffusion via Optimal Classifier Guidance. - Owen Oertell; Shikun Sun; Yiding Chen; Jin Peng Zhou; Zhiyong Wang; Wen Sun (CoRR, 2025)