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2020 – today
- 2024
[j19]Banghua Zhu
, Ziao Wang, Nadim Ghaddar, Jiantao Jiao, Lele Wang:
Noisy Computing of the OR and MAX Functions. IEEE J. Sel. Areas Inf. Theory 5: 302-313 (2024)- [c46]Banghua Zhu, Michael I. Jordan, Jiantao Jiao:
Iterative Data Smoothing: Mitigating Reward Overfitting and Overoptimization in RLHF. ICML 2024 - [c45]Jinning Li, Xinyi Liu, Banghua Zhu, Jiantao Jiao, Masayoshi Tomizuka, Chen Tang, Wei Zhan:
Guided Online Distillation: Promoting Safe Reinforcement Learning by Offline Demonstration. ICRA 2024: 7447-7454 - [i78]Banghua Zhu, Michael I. Jordan, Jiantao Jiao:
Iterative Data Smoothing: Mitigating Reward Overfitting and Overoptimization in RLHF. CoRR abs/2401.16335 (2024) - [i77]Hanlin Zhu, Banghua Zhu, Jiantao Jiao:
Efficient Prompt Caching via Embedding Similarity. CoRR abs/2402.01173 (2024) - [i76]Banghua Zhu, Norman Mu, Jiantao Jiao, David A. Wagner:
Generative AI Security: Challenges and Countermeasures. CoRR abs/2402.12617 (2024) - [i75]Nived Rajaraman, Jiantao Jiao, Kannan Ramchandran:
Toward a Theory of Tokenization in LLMs. CoRR abs/2404.08335 (2024) - [i74]Hanlin Zhu, Baihe Huang, Shaolun Zhang, Michael I. Jordan, Jiantao Jiao, Yuandong Tian, Stuart Russell:
Towards a Theoretical Understanding of the 'Reversal Curse' via Training Dynamics. CoRR abs/2405.04669 (2024) - [i73]Zhanhao Hu, Julien Piet, Geng Zhao, Jiantao Jiao, David A. Wagner:
Toxicity Detection for Free. CoRR abs/2405.18822 (2024) - [i72]Henry Pinkard, Leyla A. Kabuli, Eric Markley, Tiffany Chien, Jiantao Jiao, Laura Waller:
Universal evaluation and design of imaging systems using information estimation. CoRR abs/2405.20559 (2024) - [i71]Tianhao Wu, Weizhe Yuan, Olga Golovneva, Jing Xu, Yuandong Tian, Jiantao Jiao, Jason Weston, Sainbayar Sukhbaatar:
Meta-Rewarding Language Models: Self-Improving Alignment with LLM-as-a-Meta-Judge. CoRR abs/2407.19594 (2024) - [i70]Richard Zhuang, Tianhao Wu, Zhaojin Wen, Andrew Li, Jiantao Jiao, Kannan Ramchandran:
EmbedLLM: Learning Compact Representations of Large Language Models. CoRR abs/2410.02223 (2024) - [i69]Tianhao Wu, Janice Lan, Weizhe Yuan, Jiantao Jiao, Jason Weston, Sainbayar Sukhbaatar:
Thinking LLMs: General Instruction Following with Thought Generation. CoRR abs/2410.10630 (2024) - [i68]Tianyu Guo, Druv Pai, Yu Bai, Jiantao Jiao, Michael I. Jordan, Song Mei:
Active-Dormant Attention Heads: Mechanistically Demystifying Extreme-Token Phenomena in LLMs. CoRR abs/2410.13835 (2024) - [i67]Evan Frick, Tianle Li, Connor Chen, Wei-Lin Chiang, Anastasios N. Angelopoulos, Jiantao Jiao, Banghua Zhu, Joseph E. Gonzalez, Ion Stoica:
How to Evaluate Reward Models for RLHF. CoRR abs/2410.14872 (2024) - 2023
- [c44]Jinhyun So, Ramy E. Ali, Basak Güler, Jiantao Jiao, Amir Salman Avestimehr:
Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated Learning. AAAI 2023: 9864-9873 - [c43]Banghua Zhu, Lun Wang, Qi Pang, Shuai Wang, Jiantao Jiao, Dawn Song, Michael I. Jordan:
Byzantine-Robust Federated Learning with Optimal Statistical Rates. AISTATS 2023: 3151-3178 - [c42]Paria Rashidinejad, Hanlin Zhu, Kunhe Yang, Stuart Russell, Jiantao Jiao:
Optimal Conservative Offline RL with General Function Approximation via Augmented Lagrangian. ICLR 2023 - [c41]Ikechukwu Uchendu, Ted Xiao, Yao Lu, Banghua Zhu, Mengyuan Yan, Joséphine Simon, Matthew Bennice, Chuyuan Fu, Cong Ma, Jiantao Jiao, Sergey Levine, Karol Hausman:
Jump-Start Reinforcement Learning. ICML 2023: 34556-34583 - [c40]Geng Zhao, Banghua Zhu, Jiantao Jiao, Michael I. Jordan:
Online Learning in Stackelberg Games with an Omniscient Follower. ICML 2023: 42304-42316 - [c39]Banghua Zhu, Michael I. Jordan, Jiantao Jiao:
Principled Reinforcement Learning with Human Feedback from Pairwise or K-wise Comparisons. ICML 2023: 43037-43067 - [c38]Banghua Zhu, Ziao Wang, Nadim Ghaddar, Jiantao Jiao, Lele Wang:
On the Optimal Bounds for Noisy Computing. ISIT 2023: 1788-1793 - [c37]Banghua Zhu, Ying Sheng, Lianmin Zheng, Clark W. Barrett, Michael I. Jordan, Jiantao Jiao:
Towards Optimal Caching and Model Selection for Large Model Inference. NeurIPS 2023 - [c36]Banghua Zhu, Mingyu Ding, Philip L. Jacobson, Ming Wu, Wei Zhan, Michael I. Jordan, Jiantao Jiao:
Doubly-Robust Self-Training. NeurIPS 2023 - [c35]Hanlin Zhu, Paria Rashidinejad, Jiantao Jiao:
Importance Weighted Actor-Critic for Optimal Conservative Offline Reinforcement Learning. NeurIPS 2023 - [c34]Banghua Zhu, Stephen Bates, Zhuoran Yang, Yixin Wang, Jiantao Jiao, Michael I. Jordan:
The Sample Complexity of Online Contract Design. EC 2023: 1188 - [i66]Banghua Zhu, Jiantao Jiao, Michael I. Jordan:
Principled Reinforcement Learning with Human Feedback from Pairwise or K-wise Comparisons. CoRR abs/2301.11270 (2023) - [i65]Geng Zhao, Banghua Zhu, Jiantao Jiao, Michael I. Jordan:
Online Learning in Stackelberg Games with an Omniscient Follower. CoRR abs/2301.11518 (2023) - [i64]Hanlin Zhu, Paria Rashidinejad, Jiantao Jiao:
Importance Weighted Actor-Critic for Optimal Conservative Offline Reinforcement Learning. CoRR abs/2301.12714 (2023) - [i63]Nived Rajaraman, Yanjun Han, Jiantao Jiao, Kannan Ramchandran:
Beyond UCB: Statistical Complexity and Optimal Algorithms for Non-linear Ridge Bandits. CoRR abs/2302.06025 (2023) - [i62]Banghua Zhu, Sai Praneeth Karimireddy, Jiantao Jiao, Michael I. Jordan:
Online Learning in a Creator Economy. CoRR abs/2305.11381 (2023) - [i61]Banghua Zhu, Mingyu Ding, Philip L. Jacobson, Ming Wu, Wei Zhan, Michael I. Jordan, Jiantao Jiao:
Doubly Robust Self-Training. CoRR abs/2306.00265 (2023) - [i60]Banghua Zhu, Ying Sheng, Lianmin Zheng, Clark W. Barrett, Michael I. Jordan, Jiantao Jiao:
On Optimal Caching and Model Multiplexing for Large Model Inference. CoRR abs/2306.02003 (2023) - [i59]Banghua Zhu, Hiteshi Sharma, Felipe Vieira Frujeri, Shi Dong, Chenguang Zhu, Michael I. Jordan, Jiantao Jiao:
Fine-Tuning Language Models with Advantage-Induced Policy Alignment. CoRR abs/2306.02231 (2023) - [i58]Banghua Zhu, Ziao Wang, Nadim Ghaddar, Jiantao Jiao, Lele Wang:
On the Optimal Bounds for Noisy Computing. CoRR abs/2306.11951 (2023) - [i57]Banghua Zhu, Ziao Wang, Nadim Ghaddar, Jiantao Jiao, Lele Wang:
Noisy Computing of the OR and MAX Functions. CoRR abs/2309.03986 (2023) - [i56]Jinning Li, Xinyi Liu, Banghua Zhu, Jiantao Jiao, Masayoshi Tomizuka, Chen Tang, Wei Zhan:
Guided Online Distillation: Promoting Safe Reinforcement Learning by Offline Demonstration. CoRR abs/2309.09408 (2023) - [i55]Tianhao Wu, Banghua Zhu, Ruoyu Zhang, Zhaojin Wen, Kannan Ramchandran, Jiantao Jiao:
Pairwise Proximal Policy Optimization: Harnessing Relative Feedback for LLM Alignment. CoRR abs/2310.00212 (2023) - [i54]Hanlin Zhu, Andrew Cohen, Danqing Wang, Kevin Yang, Xiaomeng Yang, Jiantao Jiao, Yuandong Tian:
End-to-end Story Plot Generator. CoRR abs/2310.08796 (2023) - [i53]Baihe Huang, Banghua Zhu, Hanlin Zhu, Jason D. Lee, Jiantao Jiao, Michael I. Jordan:
Towards Optimal Statistical Watermarking. CoRR abs/2312.07930 (2023) - 2022
- [j18]Yuexiang Zhai, Christina Baek, Zhengyuan Zhou, Jiantao Jiao, Yi Ma:
Computational Benefits of Intermediate Rewards for Goal-Reaching Policy Learning. J. Artif. Intell. Res. 73: 847-896 (2022) - [j17]Cong Ma, Banghua Zhu, Jiantao Jiao, Martin J. Wainwright:
Minimax Off-Policy Evaluation for Multi-Armed Bandits. IEEE Trans. Inf. Theory 68(8): 5314-5339 (2022) - [j16]Paria Rashidinejad, Banghua Zhu, Cong Ma, Jiantao Jiao, Stuart Russell:
Bridging Offline Reinforcement Learning and Imitation Learning: A Tale of Pessimism. IEEE Trans. Inf. Theory 68(12): 8156-8196 (2022) - [c33]Tianhao Wu, Yunchang Yang, Han Zhong, Liwei Wang, Simon S. Du, Jiantao Jiao:
Nearly Optimal Policy Optimization with Stable at Any Time Guarantee. ICML 2022: 24243-24265 - [c32]Banghua Zhu, Jiantao Jiao, Michael I. Jordan:
Robust Estimation for Non-parametric Families via Generative Adversarial Networks. ISIT 2022: 1100-1105 - [c31]Gokul Swamy, Nived Rajaraman, Matthew Peng, Sanjiban Choudhury, J. Andrew Bagnell, Steven Wu, Jiantao Jiao, Kannan Ramchandran:
Minimax Optimal Online Imitation Learning via Replay Estimation. NeurIPS 2022 - [c30]Yifei Wang, Tavor Z. Baharav, Yanjun Han, Jiantao Jiao, David Tse:
Beyond the Best: Distribution Functional Estimation in Infinite-Armed Bandits. NeurIPS 2022 - [i52]Banghua Zhu, Jiantao Jiao, Michael I. Jordan:
Robust Estimation for Nonparametric Families via Generative Adversarial Networks. CoRR abs/2202.01269 (2022) - [i51]Ikechukwu Uchendu, Ted Xiao, Yao Lu, Banghua Zhu, Mengyuan Yan, Joséphine Simon, Matthew Bennice, Chuyuan Fu, Cong Ma, Jiantao Jiao, Sergey Levine, Karol Hausman:
Jump-Start Reinforcement Learning. CoRR abs/2204.02372 (2022) - [i50]Banghua Zhu, Lun Wang, Qi Pang, Shuai Wang, Jiantao Jiao, Dawn Song, Michael I. Jordan:
Byzantine-Robust Federated Learning with Optimal Statistical Rates and Privacy Guarantees. CoRR abs/2205.11765 (2022) - [i49]Gokul Swamy, Nived Rajaraman, Matthew Peng, Sanjiban Choudhury, J. Andrew Bagnell, Zhiwei Steven Wu, Jiantao Jiao, Kannan Ramchandran:
Minimax Optimal Online Imitation Learning via Replay Estimation. CoRR abs/2205.15397 (2022) - [i48]Paria Rashidinejad, Hanlin Zhu, Kunhe Yang, Stuart Russell, Jiantao Jiao:
Optimal Conservative Offline RL with General Function Approximation via Augmented Lagrangian. CoRR abs/2211.00716 (2022) - [i47]Yifei Wang, Tavor Z. Baharav, Yanjun Han, Jiantao Jiao, David Tse:
Beyond the Best: Estimating Distribution Functionals in Infinite-Armed Bandits. CoRR abs/2211.01743 (2022) - [i46]Banghua Zhu, Stephen Bates, Zhuoran Yang, Yixin Wang, Jiantao Jiao, Michael I. Jordan:
The Sample Complexity of Online Contract Design. CoRR abs/2211.05732 (2022) - 2021
- [c29]Nived Rajaraman, Yanjun Han, Lin Yang, Jingbo Liu, Jiantao Jiao, Kannan Ramchandran:
On the Value of Interaction and Function Approximation in Imitation Learning. NeurIPS 2021: 1325-1336 - [c28]Tianjun Zhang, Paria Rashidinejad, Jiantao Jiao, Yuandong Tian, Joseph E. Gonzalez, Stuart Russell:
MADE: Exploration via Maximizing Deviation from Explored Regions. NeurIPS 2021: 9663-9680 - [c27]Paria Rashidinejad, Banghua Zhu, Cong Ma, Jiantao Jiao, Stuart Russell:
Bridging Offline Reinforcement Learning and Imitation Learning: A Tale of Pessimism. NeurIPS 2021: 11702-11716 - [i45]Matt Peng, Banghua Zhu, Jiantao Jiao:
Linear Representation Meta-Reinforcement Learning for Instant Adaptation. CoRR abs/2101.04750 (2021) - [i44]Cong Ma, Banghua Zhu, Jiantao Jiao, Martin J. Wainwright:
Minimax Off-Policy Evaluation for Multi-Armed Bandits. CoRR abs/2101.07781 (2021) - [i43]Nived Rajaraman, Yanjun Han, Lin F. Yang, Kannan Ramchandran, Jiantao Jiao:
Provably Breaking the Quadratic Error Compounding Barrier in Imitation Learning, Optimally. CoRR abs/2102.12948 (2021) - [i42]Paria Rashidinejad, Banghua Zhu, Cong Ma, Jiantao Jiao, Stuart Russell:
Bridging Offline Reinforcement Learning and Imitation Learning: A Tale of Pessimism. CoRR abs/2103.12021 (2021) - [i41]Jinhyun So, Ramy E. Ali, Basak Guler, Jiantao Jiao, Salman Avestimehr:
Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated Learning. CoRR abs/2106.03328 (2021) - [i40]Tianjun Zhang, Paria Rashidinejad, Jiantao Jiao, Yuandong Tian, Joseph Gonzalez, Stuart Russell:
MADE: Exploration via Maximizing Deviation from Explored Regions. CoRR abs/2106.10268 (2021) - [i39]Yuexiang Zhai, Christina Baek, Zhengyuan Zhou, Jiantao Jiao, Yi Ma:
Computational Benefits of Intermediate Rewards for Hierarchical Planning. CoRR abs/2107.03961 (2021) - [i38]Tianhao Wu, Yunchang Yang, Han Zhong, Liwei Wang, Simon S. Du, Jiantao Jiao:
Nearly Optimal Policy Optimization with Stable at Any Time Guarantee. CoRR abs/2112.10935 (2021) - [i37]Jinhyun So, Ramy E. Ali, Basak Guler, Jiantao Jiao, Salman Avestimehr:
Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated Learning. IACR Cryptol. ePrint Arch. 2021: 771 (2021) - 2020
- [j15]Yanjun Han, Jiantao Jiao, Tsachy Weissman:
Minimax Estimation of Divergences Between Discrete Distributions. IEEE J. Sel. Areas Inf. Theory 1(3): 814-823 (2020) - [j14]Jiantao Jiao, Yanjun Han:
Bias Correction With Jackknife, Bootstrap, and Taylor Series. IEEE Trans. Inf. Theory 66(7): 4392-4418 (2020) - [j13]Banghua Zhu, Jiantao Jiao, David Tse:
Deconstructing Generative Adversarial Networks. IEEE Trans. Inf. Theory 66(11): 7155-7179 (2020) - [c26]Banghua Zhu, Jiantao Jiao, Jacob Steinhardt:
When does the Tukey Median work? ISIT 2020: 1201-1206 - [c25]Nived Rajaraman, Lin F. Yang, Jiantao Jiao, Kannan Ramchandran:
Toward the Fundamental Limits of Imitation Learning. NeurIPS 2020 - [c24]Paria Rashidinejad, Jiantao Jiao, Stuart Russell:
SLIP: Learning to predict in unknown dynamical systems with long-term memory. NeurIPS 2020 - [i36]Banghua Zhu, Jiantao Jiao, Jacob Steinhardt:
When does the Tukey median work? CoRR abs/2001.07805 (2020) - [i35]Banghua Zhu, Jiantao Jiao, Jacob Steinhardt:
Robust estimation via generalized quasi-gradients. CoRR abs/2005.14073 (2020) - [i34]Nived Rajaraman, Lin F. Yang, Jiantao Jiao, Kannan Ramchandran:
Toward the Fundamental Limits of Imitation Learning. CoRR abs/2009.05990 (2020) - [i33]Paria Rashidinejad, Jiantao Jiao, Stuart Russell:
SLIP: Learning to Predict in Unknown Dynamical Systems with Long-Term Memory. CoRR abs/2010.05899 (2020)
2010 – 2019
- 2019
- [j12]Dmitri S. Pavlichin, Jiantao Jiao, Tsachy Weissman:
Approximate Profile Maximum Likelihood. J. Mach. Learn. Res. 20: 122:1-122:55 (2019) - [j11]Jiantao Jiao, Yanjun Han, Irena Fischer-Hwang, Tsachy Weissman:
Estimating the Fundamental Limits is Easier Than Achieving the Fundamental Limits. IEEE Trans. Inf. Theory 65(10): 6704-6715 (2019) - [c23]Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric P. Xing, Laurent El Ghaoui, Michael I. Jordan:
Theoretically Principled Trade-off between Robustness and Accuracy. ICML 2019: 7472-7482 - [c22]Giulia Fanti, Jiantao Jiao, Ashok Vardhan Makkuva, Sewoong Oh, Ranvir Rana, Pramod Viswanath:
Barracuda: The Power of ℓ-polling in Proof-of-Stake Blockchains. MobiHoc 2019: 351-360 - [i32]Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric P. Xing, Laurent El Ghaoui, Michael I. Jordan:
Theoretically Principled Trade-off between Robustness and Accuracy. CoRR abs/1901.08573 (2019) - [i31]Banghua Zhu, Jiantao Jiao, David Tse:
Deconstructing Generative Adversarial Networks. CoRR abs/1901.09465 (2019) - [i30]Giulia Fanti, Jiantao Jiao, Ashok Vardhan Makkuva, Sewoong Oh, Ranvir Rana, Pramod Viswanath:
Barracuda: The Power of 𝓁-polling in Proof-of-Stake Blockchains. CoRR abs/1909.08719 (2019) - [i29]Banghua Zhu, Jiantao Jiao, Jacob Steinhardt:
Generalized Resilience and Robust Statistics. CoRR abs/1909.08755 (2019) - 2018
- [j10]Jiantao Jiao, Kartik Venkat, Tsachy Weissman:
Mutual Information, Relative Entropy and Estimation Error in Semi-Martingale Channels. IEEE Trans. Inf. Theory 64(10): 6662-6671 (2018) - [j9]Jiantao Jiao, Yanjun Han, Tsachy Weissman:
Minimax Estimation of the L1 Distance. IEEE Trans. Inf. Theory 64(10): 6672-6706 (2018) - [c21]Yanjun Han, Jiantao Jiao, Tsachy Weissman:
Local moment matching: A unified methodology for symmetric functional estimation and distribution estimation under Wasserstein distance. COLT 2018: 3189-3221 - [c20]Kedar Tatwawadi, Jiantao Jiao, Tsachy Weissman:
Minimax Redundancy for Markov Chains with Large State Space. ISIT 2018: 216-220 - [c19]Jiantao Jiao, Weihao Gao, Yanjun Han:
The Nearest Neighbor Information Estimator is Adaptively Near Minimax Rate-Optimal. NeurIPS 2018: 3160-3171 - [c18]Yanjun Han, Jiantao Jiao, Chuan-Zheng Lee, Tsachy Weissman, Yihong Wu, Tiancheng Yu:
Entropy Rate Estimation for Markov Chains with Large State Space. NeurIPS 2018: 9803-9814 - [i28]Yanjun Han, Jiantao Jiao, Chuan-Zheng Lee, Tsachy Weissman, Yihong Wu, Tiancheng Yu:
Entropy Rate Estimation for Markov Chains with Large State Space. CoRR abs/1802.07889 (2018) - [i27]Yanjun Han, Jiantao Jiao, Tsachy Weissman:
Local moment matching: A unified methodology for symmetric functional estimation and distribution estimation under Wasserstein distance. CoRR abs/1802.08405 (2018) - [i26]Kedar Shriram Tatwawadi, Jiantao Jiao, Tsachy Weissman:
Minimax redundancy for Markov chains with large state space. CoRR abs/1805.01355 (2018) - [i25]Jay Mardia, Jiantao Jiao, Ervin Tanczos, Robert D. Nowak, Tsachy Weissman:
Concentration Inequalities for the Empirical Distribution. CoRR abs/1809.06522 (2018) - [i24]Hongyang Zhang, Susu Xu, Jiantao Jiao, Pengtao Xie, Ruslan Salakhutdinov, Eric P. Xing:
Stackelberg GAN: Towards Provable Minimax Equilibrium via Multi-Generator Architectures. CoRR abs/1811.08010 (2018) - 2017
- [j8]Jiantao Jiao, Kartik Venkat, Tsachy Weissman:
Relations Between Information and Estimation in Discrete-Time Lévy Channels. IEEE Trans. Inf. Theory 63(6): 3579-3594 (2017) - [j7]Jiantao Jiao, Kartik Venkat, Yanjun Han, Tsachy Weissman:
Maximum Likelihood Estimation of Functionals of Discrete Distributions. IEEE Trans. Inf. Theory 63(10): 6774-6798 (2017) - [c17]Jiantao Jiao, Yanjun Han, Tsachy Weissman:
Dependence measures bounding the exploration bias for general measurements. ISIT 2017: 1475-1479 - [i23]Jiantao Jiao, Kartik Venkat, Tsachy Weissman:
Mutual Information, Relative Entropy and Estimation Error in Semi-martingale Channels. CoRR abs/1704.05199 (2017) - [i22]Jiantao Jiao, Yanjun Han, Tsachy Weissman:
Minimax Estimation of the $L_1$ Distance. CoRR abs/1705.00807 (2017) - [i21]Jiantao Jiao, Yanjun Han, Irena Fischer-Hwang, Tsachy Weissman:
Estimating the Fundamental Limits is Easier than Achieving the Fundamental Limits. CoRR abs/1707.01203 (2017) - [i20]Jiantao Jiao, Yanjun Han, Tsachy Weissman:
Bias Correction with Jackknife, Bootstrap, and Taylor Series. CoRR abs/1709.06183 (2017) - [i19]Yanjun Han, Jiantao Jiao, Rajarshi Mukherjee, Tsachy Weissman:
On Estimation of L{r}-Norms in Gaussian White Noise Models. CoRR abs/1710.03863 (2017) - [i18]Yanjun Han, Jiantao Jiao, Tsachy Weissman, Yihong Wu:
Optimal rates of entropy estimation over Lipschitz balls. CoRR abs/1711.02141 (2017) - [i17]Jiantao Jiao, Weihao Gao, Yanjun Han:
The Nearest Neighbor Information Estimator is Adaptively Near Minimax Rate-Optimal. CoRR abs/1711.08824 (2017) - [i16]Dmitri S. Pavlichin, Jiantao Jiao, Tsachy Weissman:
Approximate Profile Maximum Likelihood. CoRR abs/1712.07177 (2017) - 2016
- [c16]Jiantao Jiao, Yanjun Han, Tsachy Weissman:
Beyond maximum likelihood: Boosting the Chow-Liu algorithm for large alphabets. ACSSC 2016: 321-325 - [c15]Jiantao Jiao, Yanjun Han, Tsachy Weissman:
Minimax estimation of the L1 distance. ISIT 2016: 750-754 - [c14]Jiantao Jiao, Kartik Venkat, Tsachy Weissman:
Mutual information, relative entropy and estimation error in semi-martingale channels. ISIT 2016: 2794-2798 - [c13]Yanjun Han, Jiantao Jiao, Tsachy Weissman:
Minimax rate-optimal estimation of KL divergence between discrete distributions. ISITA 2016: 256-260 - [i15]Yanjun Han, Jiantao Jiao, Tsachy Weissman:
Minimax Estimation of KL Divergence between Discrete Distributions. CoRR abs/1605.09124 (2016) - [i14]Sihan Li, Jiantao Jiao, Yanjun Han, Tsachy Weissman:
Demystifying ResNet. CoRR abs/1611.01186 (2016) - [i13]Jiantao Jiao, Yanjun Han, Tsachy Weissman:
Dependence Measures Bounding the Exploration Bias for General Measurements. CoRR abs/1612.05845 (2016) - 2015
- [j6]Jiantao Jiao, Kartik Venkat, Yanjun Han, Tsachy Weissman:
Minimax Estimation of Functionals of Discrete Distributions. IEEE Trans. Inf. Theory 61(5): 2835-2885 (2015) - [j5]Jiantao Jiao, Thomas A. Courtade, Kartik Venkat, Tsachy Weissman:
Justification of Logarithmic Loss via the Benefit of Side Information. IEEE Trans. Inf. Theory 61(10): 5357-5365 (2015) - [j4]Yanjun Han, Jiantao Jiao, Tsachy Weissman:
Minimax Estimation of Discrete Distributions Under ℓ1 Loss. IEEE Trans. Inf. Theory 61(11): 6343-6354 (2015) - [c12]Jiantao Jiao, Kartik Venkat, Yanjun Han, Tsachy Weissman:
Maximum Likelihood Estimation of information measures. ISIT 2015: 839-843 - [c11]Yanjun Han, Jiantao Jiao, Tsachy Weissman:
Does dirichlet prior smoothing solve the Shannon entropy estimation problem? ISIT 2015: 1367-1371 - [c10]Yanjun Han, Jiantao Jiao, Tsachy Weissman:
Adaptive estimation of Shannon entropy. ISIT 2015: 1372-1376 - [c9]Yanjun Han, Jiantao Jiao, Tsachy Weissman:
Minimax estimation of discrete distributions. ISIT 2015: 2291-2295 - [c8]Jiantao Jiao, Kartik Venkat, Yanjun Han, Tsachy Weissman:
Minimax estimation of information measures. ISIT 2015: 2296-2300 - [i12]Yanjun Han, Jiantao Jiao, Tsachy Weissman:
Adaptive Estimation of Shannon Entropy. CoRR abs/1502.00326 (2015) - [i11]Yanjun Han, Jiantao Jiao, Tsachy Weissman:
Does Dirichlet Prior Smoothing Solve the Shannon Entropy Estimation Problem? CoRR abs/1502.00327 (2015) - 2014
- [j3]Jiantao Jiao, Thomas A. Courtade, Albert No, Kartik Venkat, Tsachy Weissman:
Information Measures: The Curious Case of the Binary Alphabet. IEEE Trans. Inf. Theory 60(12): 7616-7626 (2014) - [c7]Thomas A. Courtade, Jiantao Jiao:
An extremal inequality for long Markov chains. Allerton 2014: 763-770 - [c6]Jiantao Jiao, Thomas A. Courtade, Albert No, Kartik Venkat, Tsachy Weissman:
Information divergences and the curious case of the binary alphabet. ISIT 2014: 351-355 - [c5]Jiantao Jiao, Thomas A. Courtade, Kartik Venkat, Tsachy Weissman:
Justification of logarithmic loss via the benefit of side information. ISIT 2014: 946-950 - [c4]Jiantao Jiao, Kartik Venkat, Tsachy Weissman:
Relations between information and estimation in scalar Lévy channels. ISIT 2014: 2212-2216 - [i10]Jiantao Jiao, Thomas A. Courtade, Kartik Venkat, Tsachy Weissman:
Justification of Logarithmic Loss via the Benefit of Side Information. CoRR abs/1403.4679 (2014) - [i9]Jiantao Jiao, Thomas A. Courtade, Albert No, Kartik Venkat, Tsachy Weissman:
Information Measures: the Curious Case of the Binary Alphabet. CoRR abs/1404.6810 (2014) - [i8]Jiantao Jiao, Kartik Venkat, Tsachy Weissman:
Relations between Information and Estimation in Scalar Lévy Channels. CoRR abs/1404.6812 (2014) - [i7]Thomas A. Courtade, Jiantao Jiao:
An Extremal Inequality for Long Markov Chains. CoRR abs/1404.6984 (2014) - [i6]Jiantao Jiao, Kartik Venkat, Tsachy Weissman:
Order-Optimal Estimation of Functionals of Discrete Distributions. CoRR abs/1406.6956 (2014) - [i5]Jiantao Jiao, Kartik Venkat, Tsachy Weissman:
Maximum Likelihood Estimation of Functionals of Discrete Distributions. CoRR abs/1406.6959 (2014) - [i4]Jiantao Jiao, Kartik Venkat, Yanjun Han, Tsachy Weissman:
Beyond Maximum Likelihood: from Theory to Practice. CoRR abs/1409.7458 (2014) - [i3]Yanjun Han, Jiantao Jiao, Tsachy Weissman:
Minimax Estimation of Discrete Distributions under ℓ1 Loss. CoRR abs/1411.1467 (2014) - 2013
- [j2]Jiantao Jiao, Haim H. Permuter, Lei Zhao, Young-Han Kim, Tsachy Weissman:
Universal Estimation of Directed Information. IEEE Trans. Inf. Theory 59(10): 6220-6242 (2013) - [c3]Jiantao Jiao, Kartik Venkat, Tsachy Weissman:
Pointwise relations between information and estimation in the Poisson channel. ISIT 2013: 449-453 - 2012
- [j1]Jiantao Jiao, Lin Zhang, Robert D. Nowak:
Minimax-Optimal Bounds for Detectors Based on Estimated Prior Probabilities. IEEE Trans. Inf. Theory 58(9): 6101-6109 (2012) - [c2]Jiantao Jiao, Haim H. Permuter, Lei Zhao, Young-Han Kim, Tsachy Weissman:
Universal estimation of directed information via sequential probability assignments. ISIT 2012: 523-527 - [i2]Jiantao Jiao, Haim H. Permuter, Lei Zhao, Young-Han Kim, Tsachy Weissman:
Universal Estimation of Directed Information. CoRR abs/1201.2334 (2012) - 2011
- [i1]Jiantao Jiao, Lin Zhang, Robert D. Nowak:
Minimax-Optimal Bounds for Detectors Based on Estimated Prior Probabilities. CoRR abs/1107.6027 (2011) - 2010
- [c1]Lin Zhang, Wenzhu Zhang, Xinyu Mao, Jiantao Jiao, Shijie Zheng, Linglong Li, Yujie Liu, Teng Wang, Ming Gu:
NOMAD: networked-observation and mobile-agent-based scene abstraction and determination. SenSys 2010: 415-416
Coauthor Index
[j15] [j12] [j11] [j10] [j9] [c21] [c20] [c18] [i28] [i27] [i26] [i25] [j8] [j7] [c17] [i23] [i22] [i21] [i20] [i19] [i18] [i16] [c16] [c15] [c14] [c13] [i15] [i14] [i13] [j6] [j5] [j4] [c12] [c11] [c10] [c9] [c8] [i12] [i11] [j3] [c6] [c5] [c4] [i10] [i9] [i8] [i6] [i5] [i4] [i3] [j2] [c3] [c2] [i2]
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