Jiantao Jiao

Orcid: 0000-0003-3766-8031

According to our database1, Jiantao Jiao authored at least 99 papers between 2010 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Noisy Computing of the OR and MAX Functions.
IEEE J. Sel. Areas Inf. Theory, 2024

How to Evaluate Reward Models for RLHF.
CoRR, 2024

Active-Dormant Attention Heads: Mechanistically Demystifying Extreme-Token Phenomena in LLMs.
CoRR, 2024

Thinking LLMs: General Instruction Following with Thought Generation.
CoRR, 2024

EmbedLLM: Learning Compact Representations of Large Language Models.
CoRR, 2024

Meta-Rewarding Language Models: Self-Improving Alignment with LLM-as-a-Meta-Judge.
CoRR, 2024

Universal evaluation and design of imaging systems using information estimation.
CoRR, 2024

Toxicity Detection for Free.
CoRR, 2024

Towards a Theoretical Understanding of the 'Reversal Curse' via Training Dynamics.
CoRR, 2024

Toward a Theory of Tokenization in LLMs.
CoRR, 2024

Generative AI Security: Challenges and Countermeasures.
CoRR, 2024

Efficient Prompt Caching via Embedding Similarity.
CoRR, 2024

Guided Online Distillation: Promoting Safe Reinforcement Learning by Offline Demonstration.
Proceedings of the IEEE International Conference on Robotics and Automation, 2024

Iterative Data Smoothing: Mitigating Reward Overfitting and Overoptimization in RLHF.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023
Towards Optimal Statistical Watermarking.
CoRR, 2023

End-to-end Story Plot Generator.
CoRR, 2023

Pairwise Proximal Policy Optimization: Harnessing Relative Feedback for LLM Alignment.
CoRR, 2023

Fine-Tuning Language Models with Advantage-Induced Policy Alignment.
CoRR, 2023

On Optimal Caching and Model Multiplexing for Large Model Inference.
CoRR, 2023

Online Learning in a Creator Economy.
CoRR, 2023

Beyond UCB: Statistical Complexity and Optimal Algorithms for Non-linear Ridge Bandits.
CoRR, 2023

The Sample Complexity of Online Contract Design.
Proceedings of the 24th ACM Conference on Economics and Computation, 2023

Importance Weighted Actor-Critic for Optimal Conservative Offline Reinforcement Learning.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Doubly-Robust Self-Training.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Towards Optimal Caching and Model Selection for Large Model Inference.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

On the Optimal Bounds for Noisy Computing.
Proceedings of the IEEE International Symposium on Information Theory, 2023

Principled Reinforcement Learning with Human Feedback from Pairwise or K-wise Comparisons.
Proceedings of the International Conference on Machine Learning, 2023

Online Learning in Stackelberg Games with an Omniscient Follower.
Proceedings of the International Conference on Machine Learning, 2023

Jump-Start Reinforcement Learning.
Proceedings of the International Conference on Machine Learning, 2023

Optimal Conservative Offline RL with General Function Approximation via Augmented Lagrangian.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Byzantine-Robust Federated Learning with Optimal Statistical Rates.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022
Bridging Offline Reinforcement Learning and Imitation Learning: A Tale of Pessimism.
IEEE Trans. Inf. Theory, 2022

Minimax Off-Policy Evaluation for Multi-Armed Bandits.
IEEE Trans. Inf. Theory, 2022

Computational Benefits of Intermediate Rewards for Goal-Reaching Policy Learning.
J. Artif. Intell. Res., 2022

Beyond the Best: Estimating Distribution Functionals in Infinite-Armed Bandits.
CoRR, 2022

Byzantine-Robust Federated Learning with Optimal Statistical Rates and Privacy Guarantees.
CoRR, 2022

Robust Estimation for Nonparametric Families via Generative Adversarial Networks.
CoRR, 2022

Beyond the Best: Distribution Functional Estimation in Infinite-Armed Bandits.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Minimax Optimal Online Imitation Learning via Replay Estimation.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Robust Estimation for Non-parametric Families via Generative Adversarial Networks.
Proceedings of the IEEE International Symposium on Information Theory, 2022

Nearly Optimal Policy Optimization with Stable at Any Time Guarantee.
Proceedings of the International Conference on Machine Learning, 2022

2021
Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated Learning.
IACR Cryptol. ePrint Arch., 2021

Computational Benefits of Intermediate Rewards for Hierarchical Planning.
CoRR, 2021

Provably Breaking the Quadratic Error Compounding Barrier in Imitation Learning, Optimally.
CoRR, 2021

Linear Representation Meta-Reinforcement Learning for Instant Adaptation.
CoRR, 2021

MADE: Exploration via Maximizing Deviation from Explored Regions.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

On the Value of Interaction and Function Approximation in Imitation Learning.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020
Deconstructing Generative Adversarial Networks.
IEEE Trans. Inf. Theory, 2020

Bias Correction With Jackknife, Bootstrap, and Taylor Series.
IEEE Trans. Inf. Theory, 2020

Minimax Estimation of Divergences Between Discrete Distributions.
IEEE J. Sel. Areas Inf. Theory, 2020

Robust estimation via generalized quasi-gradients.
CoRR, 2020

SLIP: Learning to predict in unknown dynamical systems with long-term memory.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Toward the Fundamental Limits of Imitation Learning.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

When does the Tukey Median work?
Proceedings of the IEEE International Symposium on Information Theory, 2020

2019
Estimating the Fundamental Limits is Easier Than Achieving the Fundamental Limits.
IEEE Trans. Inf. Theory, 2019

Approximate Profile Maximum Likelihood.
J. Mach. Learn. Res., 2019

Generalized Resilience and Robust Statistics.
CoRR, 2019

Barracuda: The Power of ℓ-polling in Proof-of-Stake Blockchains.
Proceedings of the Twentieth ACM International Symposium on Mobile Ad Hoc Networking and Computing, 2019

Theoretically Principled Trade-off between Robustness and Accuracy.
Proceedings of the 36th International Conference on Machine Learning, 2019

2018
Mutual Information, Relative Entropy and Estimation Error in Semi-Martingale Channels.
IEEE Trans. Inf. Theory, 2018

Minimax Estimation of the L<sub>1</sub> Distance.
IEEE Trans. Inf. Theory, 2018

Stackelberg GAN: Towards Provable Minimax Equilibrium via Multi-Generator Architectures.
CoRR, 2018

Concentration Inequalities for the Empirical Distribution.
CoRR, 2018

The Nearest Neighbor Information Estimator is Adaptively Near Minimax Rate-Optimal.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Entropy Rate Estimation for Markov Chains with Large State Space.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Minimax Redundancy for Markov Chains with Large State Space.
Proceedings of the 2018 IEEE International Symposium on Information Theory, 2018

Local moment matching: A unified methodology for symmetric functional estimation and distribution estimation under Wasserstein distance.
Proceedings of the Conference On Learning Theory, 2018

2017
Relations Between Information and Estimation in Discrete-Time Lévy Channels.
IEEE Trans. Inf. Theory, 2017

Maximum Likelihood Estimation of Functionals of Discrete Distributions.
IEEE Trans. Inf. Theory, 2017

Optimal rates of entropy estimation over Lipschitz balls.
CoRR, 2017

On Estimation of L<sub>{r}</sub>-Norms in Gaussian White Noise Models.
CoRR, 2017

Bias Correction with Jackknife, Bootstrap, and Taylor Series.
CoRR, 2017

Minimax Estimation of the $L_1$ Distance.
CoRR, 2017

Dependence measures bounding the exploration bias for general measurements.
Proceedings of the 2017 IEEE International Symposium on Information Theory, 2017

2016
Demystifying ResNet.
CoRR, 2016

Minimax Estimation of KL Divergence between Discrete Distributions.
CoRR, 2016

Minimax rate-optimal estimation of KL divergence between discrete distributions.
Proceedings of the 2016 International Symposium on Information Theory and Its Applications, 2016

Minimax estimation of the L1 distance.
Proceedings of the IEEE International Symposium on Information Theory, 2016

Beyond maximum likelihood: Boosting the Chow-Liu algorithm for large alphabets.
Proceedings of the 50th Asilomar Conference on Signals, Systems and Computers, 2016

2015
Minimax Estimation of Functionals of Discrete Distributions.
IEEE Trans. Inf. Theory, 2015

Justification of Logarithmic Loss via the Benefit of Side Information.
IEEE Trans. Inf. Theory, 2015

Minimax Estimation of Discrete Distributions Under ℓ<sub>1</sub> Loss.
IEEE Trans. Inf. Theory, 2015

Minimax estimation of information measures.
Proceedings of the IEEE International Symposium on Information Theory, 2015

Maximum Likelihood Estimation of information measures.
Proceedings of the IEEE International Symposium on Information Theory, 2015

Minimax estimation of discrete distributions.
Proceedings of the IEEE International Symposium on Information Theory, 2015

Adaptive estimation of Shannon entropy.
Proceedings of the IEEE International Symposium on Information Theory, 2015

Does dirichlet prior smoothing solve the Shannon entropy estimation problem?
Proceedings of the IEEE International Symposium on Information Theory, 2015

2014
Information Measures: The Curious Case of the Binary Alphabet.
IEEE Trans. Inf. Theory, 2014

Maximum Likelihood Estimation of Functionals of Discrete Distributions.
CoRR, 2014

Order-Optimal Estimation of Functionals of Discrete Distributions.
CoRR, 2014

Beyond Maximum Likelihood: from Theory to Practice.
CoRR, 2014

Relations between information and estimation in scalar Lévy channels.
Proceedings of the 2014 IEEE International Symposium on Information Theory, Honolulu, HI, USA, June 29, 2014

Information divergences and the curious case of the binary alphabet.
Proceedings of the 2014 IEEE International Symposium on Information Theory, Honolulu, HI, USA, June 29, 2014

An extremal inequality for long Markov chains.
Proceedings of the 52nd Annual Allerton Conference on Communication, 2014

2013
Universal Estimation of Directed Information.
IEEE Trans. Inf. Theory, 2013

Pointwise relations between information and estimation in the Poisson channel.
Proceedings of the 2013 IEEE International Symposium on Information Theory, 2013

2012
Minimax-Optimal Bounds for Detectors Based on Estimated Prior Probabilities.
IEEE Trans. Inf. Theory, 2012

Universal estimation of directed information via sequential probability assignments.
Proceedings of the 2012 IEEE International Symposium on Information Theory, 2012

2010
NOMAD: networked-observation and mobile-agent-based scene abstraction and determination.
Proceedings of the 8th International Conference on Embedded Networked Sensor Systems, 2010


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