NOMAD: networked-observation and mobile-agent-based scene abstraction and determination. - Lin Zhang 001; Wenzhu Zhang; Xinyu Mao; Jiantao Jiao; Shijie Zheng; Linglong Li; Yujie Liu; Teng Wang; Ming Gu (SenSys, 2010)

Universal estimation of directed information via sequential probability assignments. - Jiantao Jiao; Haim H. Permuter; Lei Zhao; Young-Han Kim 001; Tsachy Weissman (ISIT, 2012)

Minimax-Optimal Bounds for Detectors Based on Estimated Prior Probabilities. - Jiantao Jiao; Lin Zhang 001; Robert D. Nowak (IEEE Trans. Inf. Theory, 2012)

Pointwise relations between information and estimation in the Poisson channel. - Jiantao Jiao; Kartik Venkat; Tsachy Weissman (ISIT, 2013)

Universal Estimation of Directed Information. - Jiantao Jiao; Haim H. Permuter; Lei Zhao; Young-Han Kim 001; Tsachy Weissman (IEEE Trans. Inf. Theory, 2013)

An extremal inequality for long Markov chains. - Thomas A. Courtade; Jiantao Jiao (Allerton, 2014)

Information divergences and the curious case of the binary alphabet. - Jiantao Jiao; Thomas A. Courtade; Albert No; Kartik Venkat; Tsachy Weissman (ISIT, 2014)

Justification of logarithmic loss via the benefit of side information. - Jiantao Jiao; Thomas A. Courtade; Kartik Venkat; Tsachy Weissman (ISIT, 2014)

Relations between information and estimation in scalar Lévy channels. - Jiantao Jiao; Kartik Venkat; Tsachy Weissman (ISIT, 2014)

Beyond Maximum Likelihood: from Theory to Practice. - Jiantao Jiao; Kartik Venkat; Yanjun Han; Tsachy Weissman (CoRR, 2014)

Order-Optimal Estimation of Functionals of Discrete Distributions. - Jiantao Jiao; Kartik Venkat; Tsachy Weissman (CoRR, 2014)

Maximum Likelihood Estimation of Functionals of Discrete Distributions. - Jiantao Jiao; Kartik Venkat; Tsachy Weissman (CoRR, 2014)

Information Measures: The Curious Case of the Binary Alphabet. - Jiantao Jiao; Thomas A. Courtade; Albert No; Kartik Venkat; Tsachy Weissman (IEEE Trans. Inf. Theory, 2014)

Does dirichlet prior smoothing solve the Shannon entropy estimation problem? - Yanjun Han; Jiantao Jiao; Tsachy Weissman (ISIT, 2015)

Adaptive estimation of Shannon entropy. - Yanjun Han; Jiantao Jiao; Tsachy Weissman (ISIT, 2015)

Minimax estimation of discrete distributions. - Yanjun Han; Jiantao Jiao; Tsachy Weissman (ISIT, 2015)

Maximum Likelihood Estimation of information measures. - Jiantao Jiao; Kartik Venkat; Yanjun Han; Tsachy Weissman (ISIT, 2015)

Minimax estimation of information measures. - Jiantao Jiao; Kartik Venkat; Yanjun Han; Tsachy Weissman (ISIT, 2015)

Minimax Estimation of Discrete Distributions Under ℓ1 Loss. - Yanjun Han; Jiantao Jiao; Tsachy Weissman (IEEE Trans. Inf. Theory, 2015)

Minimax Estimation of Functionals of Discrete Distributions. - Jiantao Jiao; Kartik Venkat; Yanjun Han; Tsachy Weissman (IEEE Trans. Inf. Theory, 2015)

Beyond maximum likelihood: Boosting the Chow-Liu algorithm for large alphabets. - Jiantao Jiao; Yanjun Han; Tsachy Weissman (ACSSC, 2016)

Minimax estimation of the L1 distance. - Jiantao Jiao; Yanjun Han; Tsachy Weissman (ISIT, 2016)

Mutual information, relative entropy and estimation error in semi-martingale channels. - Jiantao Jiao; Kartik Venkat; Tsachy Weissman (ISIT, 2016)

Minimax rate-optimal estimation of KL divergence between discrete distributions. - Yanjun Han; Jiantao Jiao; Tsachy Weissman (ISITA, 2016)

Minimax Estimation of KL Divergence between Discrete Distributions. - Yanjun Han; Jiantao Jiao; Tsachy Weissman (CoRR, 2016)

Demystifying ResNet. - Sihan Li; Jiantao Jiao; Yanjun Han; Tsachy Weissman (CoRR, 2016)

Dependence measures bounding the exploration bias for general measurements. - Jiantao Jiao; Yanjun Han; Tsachy Weissman (ISIT, 2017)

Minimax Estimation of the $L_1$ Distance. - Jiantao Jiao; Yanjun Han; Tsachy Weissman (CoRR, 2017)

Bias Correction with Jackknife, Bootstrap, and Taylor Series. - Jiantao Jiao; Yanjun Han; Tsachy Weissman (CoRR, 2017)

On Estimation of L{r}-Norms in Gaussian White Noise Models. - Yanjun Han; Jiantao Jiao; Rajarshi Mukherjee; Tsachy Weissman (CoRR, 2017)

Optimal rates of entropy estimation over Lipschitz balls. - Yanjun Han; Jiantao Jiao; Tsachy Weissman; Yihong Wu 001 (CoRR, 2017)

Maximum Likelihood Estimation of Functionals of Discrete Distributions. - Jiantao Jiao; Kartik Venkat; Yanjun Han; Tsachy Weissman (IEEE Trans. Inf. Theory, 2017)

Relations Between Information and Estimation in Discrete-Time Lévy Channels. - Jiantao Jiao; Kartik Venkat; Tsachy Weissman (IEEE Trans. Inf. Theory, 2017)

Local moment matching: A unified methodology for symmetric functional estimation and distribution estimation under Wasserstein distance. - Yanjun Han; Jiantao Jiao; Tsachy Weissman (COLT, 2018)

Minimax Redundancy for Markov Chains with Large State Space. - Kedar Tatwawadi; Jiantao Jiao; Tsachy Weissman (ISIT, 2018)

Entropy Rate Estimation for Markov Chains with Large State Space. - Yanjun Han; Jiantao Jiao; Chuan-Zheng Lee; Tsachy Weissman; Yihong Wu 001; Tiancheng Yu (NeurIPS, 2018)

The Nearest Neighbor Information Estimator is Adaptively Near Minimax Rate-Optimal. - Jiantao Jiao; Weihao Gao; Yanjun Han (NeurIPS, 2018)

Concentration Inequalities for the Empirical Distribution. - Jay Mardia; Jiantao Jiao; Ervin Tanczos; Robert D. Nowak; Tsachy Weissman (CoRR, 2018)

Stackelberg GAN: Towards Provable Minimax Equilibrium via Multi-Generator Architectures. - Hongyang Zhang 001; Susu Xu; Jiantao Jiao; Pengtao Xie; Ruslan Salakhutdinov; Eric P. Xing (CoRR, 2018)

Minimax Estimation of the L1 Distance. - Jiantao Jiao; Yanjun Han; Tsachy Weissman (IEEE Trans. Inf. Theory, 2018)

Theoretically Principled Trade-off between Robustness and Accuracy. - Hongyang Zhang 001; Yaodong Yu; Jiantao Jiao; Eric P. Xing; Laurent El Ghaoui; Michael I. Jordan (ICML, 2019)

Barracuda: The Power of ℓ-polling in Proof-of-Stake Blockchains. - Giulia Fanti; Jiantao Jiao; Ashok Vardhan Makkuva; Sewoong Oh; Ranvir Rana; Pramod Viswanath (MobiHoc, 2019)

Generalized Resilience and Robust Statistics. - Banghua Zhu; Jiantao Jiao; Jacob Steinhardt (CoRR, 2019)

Approximate Profile Maximum Likelihood. - Dmitri S. Pavlichin; Jiantao Jiao; Tsachy Weissman (J. Mach. Learn. Res., 2019)

Estimating the Fundamental Limits is Easier Than Achieving the Fundamental Limits. - Jiantao Jiao; Yanjun Han; Irena Fischer-Hwang; Tsachy Weissman (IEEE Trans. Inf. Theory, 2019)

When does the Tukey Median work? - Banghua Zhu; Jiantao Jiao; Jacob Steinhardt (ISIT, 2020)

Toward the Fundamental Limits of Imitation Learning. - Nived Rajaraman; Lin F. Yang; Jiantao Jiao; Kannan Ramchandran (NeurIPS, 2020)

SLIP: Learning to predict in unknown dynamical systems with long-term memory. - Paria Rashidinejad; Jiantao Jiao; Stuart Russell 001 (NeurIPS, 2020)

Robust estimation via generalized quasi-gradients. - Banghua Zhu; Jiantao Jiao; Jacob Steinhardt (CoRR, 2020)

Minimax Estimation of Divergences Between Discrete Distributions. - Yanjun Han; Jiantao Jiao; Tsachy Weissman (IEEE J. Sel. Areas Inf. Theory, 2020)

Bias Correction With Jackknife, Bootstrap, and Taylor Series. - Jiantao Jiao; Yanjun Han (IEEE Trans. Inf. Theory, 2020)

Deconstructing Generative Adversarial Networks. - Banghua Zhu; Jiantao Jiao; David Tse (IEEE Trans. Inf. Theory, 2020)

On the Value of Interaction and Function Approximation in Imitation Learning. - Nived Rajaraman; Yanjun Han; Lin Yang 011; Jingbo Liu; Jiantao Jiao; Kannan Ramchandran (NeurIPS, 2021)

Bridging Offline Reinforcement Learning and Imitation Learning: A Tale of Pessimism. - Paria Rashidinejad; Banghua Zhu; Cong Ma 001; Jiantao Jiao; Stuart Russell 001 (NeurIPS, 2021)

MADE: Exploration via Maximizing Deviation from Explored Regions. - Tianjun Zhang; Paria Rashidinejad; Jiantao Jiao; Yuandong Tian; Joseph E. Gonzalez; Stuart Russell 001 (NeurIPS, 2021)

Linear Representation Meta-Reinforcement Learning for Instant Adaptation. - Matt Peng; Banghua Zhu; Jiantao Jiao (CoRR, 2021)

Provably Breaking the Quadratic Error Compounding Barrier in Imitation Learning, Optimally. - Nived Rajaraman; Yanjun Han; Lin F. Yang; Kannan Ramchandran; Jiantao Jiao (CoRR, 2021)

Computational Benefits of Intermediate Rewards for Hierarchical Planning. - Yuexiang Zhai; Christina Baek; Zhengyuan Zhou; Jiantao Jiao; Yi Ma 001 (CoRR, 2021)

Nearly Optimal Policy Optimization with Stable at Any Time Guarantee. - Tianhao Wu 002; Yunchang Yang; Han Zhong 001; Liwei Wang 001; Simon S. Du; Jiantao Jiao (ICML, 2022)

Robust Estimation for Non-parametric Families via Generative Adversarial Networks. - Banghua Zhu; Jiantao Jiao; Michael I. Jordan (ISIT, 2022)

Minimax Optimal Online Imitation Learning via Replay Estimation. - Gokul Swamy 001; Nived Rajaraman; Matthew Peng; Sanjiban Choudhury; J. Andrew Bagnell; Steven Wu 001; Jiantao Jiao; Kannan Ramchandran (NeurIPS, 2022)

Beyond the Best: Distribution Functional Estimation in Infinite-Armed Bandits. - Yifei Wang 005; Tavor Z. Baharav; Yanjun Han; Jiantao Jiao; David Tse (NeurIPS, 2022)

Robust Estimation for Nonparametric Families via Generative Adversarial Networks. - Banghua Zhu; Jiantao Jiao; Michael I. Jordan (CoRR, 2022)

Byzantine-Robust Federated Learning with Optimal Statistical Rates and Privacy Guarantees. - Banghua Zhu; Lun Wang 001; Qi Pang; Shuai Wang 011; Jiantao Jiao; Dawn Song; Michael I. Jordan (CoRR, 2022)

Beyond the Best: Estimating Distribution Functionals in Infinite-Armed Bandits. - Yifei Wang 005; Tavor Z. Baharav; Yanjun Han; Jiantao Jiao; David Tse (CoRR, 2022)

Computational Benefits of Intermediate Rewards for Goal-Reaching Policy Learning. - Yuexiang Zhai; Christina Baek; Zhengyuan Zhou; Jiantao Jiao; Yi Ma 001 (J. Artif. Intell. Res., 2022)

Minimax Off-Policy Evaluation for Multi-Armed Bandits. - Cong Ma 001; Banghua Zhu; Jiantao Jiao; Martin J. Wainwright (IEEE Trans. Inf. Theory, 2022)

Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated Learning. - Jinhyun So; Ramy E. Ali; Basak Güler; Jiantao Jiao; Amir Salman Avestimehr (AAAI, 2023)

Byzantine-Robust Federated Learning with Optimal Statistical Rates. - Banghua Zhu; Lun Wang 001; Qi Pang; Shuai Wang 011; Jiantao Jiao; Dawn Song; Michael I. Jordan (AISTATS, 2023)

Optimal Conservative Offline RL with General Function Approximation via Augmented Lagrangian. - Paria Rashidinejad; Hanlin Zhu; Kunhe Yang; Stuart Russell 001; Jiantao Jiao (ICLR, 2023)

Jump-Start Reinforcement Learning. - Ikechukwu Uchendu; Ted Xiao; Yao Lu 006; Banghua Zhu; Mengyuan Yan; Joséphine Simon; Matthew Bennice; Chuyuan Fu; Cong Ma 001; Jiantao Jiao; Sergey Levine; Karol Hausman (ICML, 2023)

Online Learning in Stackelberg Games with an Omniscient Follower. - Geng Zhao; Banghua Zhu; Jiantao Jiao; Michael I. Jordan (ICML, 2023)

Principled Reinforcement Learning with Human Feedback from Pairwise or K-wise Comparisons. - Banghua Zhu; Michael I. Jordan; Jiantao Jiao (ICML, 2023)

On the Optimal Bounds for Noisy Computing. - Banghua Zhu; Ziao Wang; Nadim Ghaddar; Jiantao Jiao; Lele Wang 001 (ISIT, 2023)

Towards Optimal Caching and Model Selection for Large Model Inference. - Banghua Zhu; Ying Sheng 007; Lianmin Zheng; Clark W. Barrett; Michael I. Jordan; Jiantao Jiao (NeurIPS, 2023)

Doubly-Robust Self-Training. - Banghua Zhu; Mingyu Ding; Philip L. Jacobson; Ming Wu; Wei Zhan; Michael I. Jordan; Jiantao Jiao (NeurIPS, 2023)

Importance Weighted Actor-Critic for Optimal Conservative Offline Reinforcement Learning. - Hanlin Zhu; Paria Rashidinejad; Jiantao Jiao (NeurIPS, 2023)

The Sample Complexity of Online Contract Design. - Banghua Zhu; Stephen Bates; Zhuoran Yang; Yixin Wang 002; Jiantao Jiao; Michael I. Jordan (EC, 2023)

Beyond UCB: Statistical Complexity and Optimal Algorithms for Non-linear Ridge Bandits. - Nived Rajaraman; Yanjun Han; Jiantao Jiao; Kannan Ramchandran (CoRR, 2023)

Online Learning in a Creator Economy. - Banghua Zhu; Sai Praneeth Karimireddy; Jiantao Jiao; Michael I. Jordan (CoRR, 2023)

On Optimal Caching and Model Multiplexing for Large Model Inference. - Banghua Zhu; Ying Sheng 007; Lianmin Zheng; Clark W. Barrett; Michael I. Jordan; Jiantao Jiao (CoRR, 2023)

Fine-Tuning Language Models with Advantage-Induced Policy Alignment. - Banghua Zhu; Hiteshi Sharma; Felipe Vieira Frujeri; Shi Dong; Chenguang Zhu; Michael I. Jordan; Jiantao Jiao (CoRR, 2023)

Pairwise Proximal Policy Optimization: Harnessing Relative Feedback for LLM Alignment. - Tianhao Wu 002; Banghua Zhu; Ruoyu Zhang; Zhaojin Wen; Kannan Ramchandran; Jiantao Jiao (CoRR, 2023)

End-to-end Story Plot Generator. - Hanlin Zhu; Andrew Cohen; Danqing Wang; Kevin Yang; Xiaomeng Yang; Jiantao Jiao; Yuandong Tian (CoRR, 2023)

Towards Optimal Statistical Watermarking. - Baihe Huang; Banghua Zhu; Hanlin Zhu; Jason D. Lee; Jiantao Jiao; Michael I. Jordan (CoRR, 2023)

Iterative Data Smoothing: Mitigating Reward Overfitting and Overoptimization in RLHF. - Banghua Zhu; Michael I. Jordan; Jiantao Jiao (ICML, 2024)

Guided Online Distillation: Promoting Safe Reinforcement Learning by Offline Demonstration. - Jinning Li 002; Xinyi Liu; Banghua Zhu; Jiantao Jiao; Masayoshi Tomizuka; Chen Tang 001; Wei Zhan (ICRA, 2024)

Toxicity Detection for Free. - Zhanhao Hu; Julien Piet; Geng Zhao; Jiantao Jiao; David A. Wagner 001 (NeurIPS, 2024)

An Analysis of Tokenization: Transformers under Markov Data. - Nived Rajaraman; Jiantao Jiao; Kannan Ramchandran (NeurIPS, 2024)

Towards a Theoretical Understanding of the 'Reversal Curse' via Training Dynamics. - Hanlin Zhu; Baihe Huang; Shaolun Zhang; Michael I. Jordan; Jiantao Jiao; Yuandong Tian; Stuart J. Russell (NeurIPS, 2024)

Efficient Prompt Caching via Embedding Similarity. - Hanlin Zhu; Banghua Zhu; Jiantao Jiao (CoRR, 2024)

Generative AI Security: Challenges and Countermeasures. - Banghua Zhu; Norman Mu; Jiantao Jiao; David A. Wagner 001 (CoRR, 2024)

Toward a Theory of Tokenization in LLMs. - Nived Rajaraman; Jiantao Jiao; Kannan Ramchandran (CoRR, 2024)

Universal evaluation and design of imaging systems using information estimation. - Henry Pinkard; Leyla A. Kabuli; Eric Markley; Tiffany Chien; Jiantao Jiao; Laura Waller (CoRR, 2024)

Meta-Rewarding Language Models: Self-Improving Alignment with LLM-as-a-Meta-Judge. - Tianhao Wu 002; Weizhe Yuan; Olga Golovneva; Jing Xu 014; Yuandong Tian; Jiantao Jiao; Jason Weston; Sainbayar Sukhbaatar (CoRR, 2024)

Thinking LLMs: General Instruction Following with Thought Generation. - Tianhao Wu 002; Janice Lan; Weizhe Yuan; Jiantao Jiao; Jason Weston; Sainbayar Sukhbaatar (CoRR, 2024)

Active-Dormant Attention Heads: Mechanistically Demystifying Extreme-Token Phenomena in LLMs. - Tianyu Guo 004; Druv Pai; Yu Bai 017; Jiantao Jiao; Michael I. Jordan; Song Mei (CoRR, 2024)

Noisy Computing of the OR and MAX Functions. - Banghua Zhu; Ziao Wang; Nadim Ghaddar; Jiantao Jiao; Lele Wang 001 (IEEE J. Sel. Areas Inf. Theory, 2024)

How to Evaluate Reward Models for RLHF. - Evan Frick; Tianle Li; Connor Chen; Wei-Lin Chiang; Anastasios Nikolas Angelopoulos; Jiantao Jiao; Banghua Zhu; Joseph E. Gonzalez; Ion Stoica (ICLR, 2025)

EmbedLLM: Learning Compact Representations of Large Language Models. - Richard Zhuang; Tianhao Wu 002; Zhaojin Wen; Andrew Li; Jiantao Jiao; Kannan Ramchandran (ICLR, 2025)

Token Assorted: Mixing Latent and Text Tokens for Improved Language Model Reasoning. - DiJia Su; Hanlin Zhu; Yingchen Xu; Jiantao Jiao; Yuandong Tian; Qinqing Zheng (CoRR, 2025)

How Do LLMs Perform Two-Hop Reasoning in Context? - Tianyu Guo 004; Hanlin Zhu; Ruiqi Zhang; Jiantao Jiao; Song Mei; Michael I. Jordan; Stuart Russell 001 (CoRR, 2025)

Reasoning by Superposition: A Theoretical Perspective on Chain of Continuous Thought. - Hanlin Zhu; Shibo Hao; Zhiting Hu; Jiantao Jiao; Stuart Russell 001; Yuandong Tian (CoRR, 2025)

Sample Complexity and Representation Ability of Test-time Scaling Paradigms. - Baihe Huang; Shanda Li; Tianhao Wu 002; Yiming Yang 002; Ameet Talwalkar; Kannan Ramchandran; Michael I. Jordan; Jiantao Jiao (CoRR, 2025)

Generalization or Hallucination? Understanding Out-of-Context Reasoning in Transformers. - Yixiao Huang 004; Hanlin Zhu; Tianyu Guo 004; Jiantao Jiao; Somayeh Sojoudi; Michael I. Jordan; Stuart Russell 001; Song Mei (CoRR, 2025)

GSM-Agent: Understanding Agentic Reasoning Using Controllable Environments. - Hanlin Zhu; Tianyu Guo 004; Song Mei; Stuart Russell 001; Nikhil Ghosh; Alberto Bietti; Jiantao Jiao (CoRR, 2025)

Emergence of Superposition: Unveiling the Training Dynamics of Chain of Continuous Thought. - Hanlin Zhu; Shibo Hao; Zhiting Hu; Jiantao Jiao; Stuart Russell 001; Yuandong Tian (CoRR, 2025)