Timezone: »
Author Information
Tsui-Wei Weng (MIT)
Huan Zhang (UC Davis)
Hongge Chen (MIT)
Zhao Song (UT-Austin)
Cho-Jui Hsieh (University of California, Davis)
Luca Daniel (MIT)
Duane Boning (MIT)
Inderjit Dhillon (UT Austin & Amazon)
Inderjit Dhillon is the Gottesman Family Centennial Professor of Computer Science and Mathematics at UT Austin, where he is also the Director of the ICES Center for Big Data Analytics. His main research interests are in big data, machine learning, network analysis, linear algebra and optimization. He received his B.Tech. degree from IIT Bombay, and Ph.D. from UC Berkeley. Inderjit has received several awards, including the ICES Distinguished Research Award, the SIAM Outstanding Paper Prize, the Moncrief Grand Challenge Award, the SIAM Linear Algebra Prize, the University Research Excellence Award, and the NSF Career Award. He has published over 160 journal and conference papers, and has served on the Editorial Board of the Journal of Machine Learning Research, the IEEE Transactions of Pattern Analysis and Machine Intelligence, Foundations and Trends in Machine Learning and the SIAM Journal for Matrix Analysis and Applications. Inderjit is an ACM Fellow, an IEEE Fellow, a SIAM Fellow and an AAAS Fellow.
Related Events (a corresponding poster, oral, or spotlight)
-
2018 Poster: Towards Fast Computation of Certified Robustness for ReLU Networks »
Thu. Jul 12th 04:15 -- 07:00 PM Room Hall B #147
More from the Same Authors
-
2022 : Fast Convergence for Unstable Reinforcement Learning Problems by Logarithmic Mapping »
Wang Zhang · Lam Nguyen · Subhro Das · Alexandre Megretsky · Luca Daniel · Tsui-Wei Weng -
2022 : Positive Unlabeled Contrastive Representation Learning »
Anish Acharya · Sujay Sanghavi · Li Jing · Bhargav Bhushanam · Michael Rabbat · Dhruv Choudhary · Inderjit Dhillon -
2023 : UCB Provably Learns From Inconsistent Human Feedback »
Shuo Yang · Tongzheng Ren · Inderjit Dhillon · Sujay Sanghavi -
2022 Poster: Linear Bandit Algorithms with Sublinear Time Complexity »
Shuo Yang · Tongzheng Ren · Sanjay Shakkottai · Eric Price · Inderjit Dhillon · Sujay Sanghavi -
2022 Spotlight: Linear Bandit Algorithms with Sublinear Time Complexity »
Shuo Yang · Tongzheng Ren · Sanjay Shakkottai · Eric Price · Inderjit Dhillon · Sujay Sanghavi -
2021 Poster: Top-k eXtreme Contextual Bandits with Arm Hierarchy »
Rajat Sen · Alexander Rakhlin · Lexing Ying · Rahul Kidambi · Dean Foster · Daniel Hill · Inderjit Dhillon -
2021 Spotlight: Top-k eXtreme Contextual Bandits with Arm Hierarchy »
Rajat Sen · Alexander Rakhlin · Lexing Ying · Rahul Kidambi · Dean Foster · Daniel Hill · Inderjit Dhillon -
2020 : Discussion Panel »
Krzysztof Dembczynski · Prateek Jain · Alina Beygelzimer · Inderjit Dhillon · Anna Choromanska · Maryam Majzoubi · Yashoteja Prabhu · John Langford -
2020 : Invited Talk 5 Q&A - Inderjit Dhillon »
Inderjit Dhillon -
2020 : Invited Talk 5 - Multi-Output Prediction: Theory and Practice - Inderjit Dhillon »
Inderjit Dhillon -
2020 Poster: Neural Network Control Policy Verification With Persistent Adversarial Perturbation »
Yuh-Shyang Wang · Tsui-Wei Weng · Luca Daniel -
2020 Poster: On Lp-norm Robustness of Ensemble Decision Stumps and Trees »
Yihan Wang · Huan Zhang · Hongge Chen · Duane Boning · Cho-Jui Hsieh -
2020 Poster: Learning to Encode Position for Transformer with Continuous Dynamical Model »
Xuanqing Liu · Hsiang-Fu Yu · Inderjit Dhillon · Cho-Jui Hsieh -
2020 Poster: Extreme Multi-label Classification from Aggregated Labels »
Yanyao Shen · Hsiang-Fu Yu · Sujay Sanghavi · Inderjit Dhillon -
2019 Poster: A Convergence Theory for Deep Learning via Over-Parameterization »
Zeyuan Allen-Zhu · Yuanzhi Li · Zhao Song -
2019 Oral: A Convergence Theory for Deep Learning via Over-Parameterization »
Zeyuan Allen-Zhu · Yuanzhi Li · Zhao Song -
2019 Poster: POPQORN: Quantifying Robustness of Recurrent Neural Networks »
CHING-YUN KO · Zhaoyang Lyu · Tsui-Wei Weng · Luca Daniel · Ngai Wong · Dahua Lin -
2019 Poster: PROVEN: Verifying Robustness of Neural Networks with a Probabilistic Approach »
Tsui-Wei Weng · Pin-Yu Chen · Lam Nguyen · Mark Squillante · Akhilan Boopathy · Ivan Oseledets · Luca Daniel -
2019 Poster: Robust Decision Trees Against Adversarial Examples »
Hongge Chen · Huan Zhang · Duane Boning · Cho-Jui Hsieh -
2019 Oral: Robust Decision Trees Against Adversarial Examples »
Hongge Chen · Huan Zhang · Duane Boning · Cho-Jui Hsieh -
2019 Oral: PROVEN: Verifying Robustness of Neural Networks with a Probabilistic Approach »
Tsui-Wei Weng · Pin-Yu Chen · Lam Nguyen · Mark Squillante · Akhilan Boopathy · Ivan Oseledets · Luca Daniel -
2019 Oral: POPQORN: Quantifying Robustness of Recurrent Neural Networks »
CHING-YUN KO · Zhaoyang Lyu · Tsui-Wei Weng · Luca Daniel · Ngai Wong · Dahua Lin -
2018 Poster: Learning long term dependencies via Fourier recurrent units »
Jiong Zhang · Yibo Lin · Zhao Song · Inderjit Dhillon -
2018 Poster: SQL-Rank: A Listwise Approach to Collaborative Ranking »
LIWEI WU · Cho-Jui Hsieh · University of California James Sharpnack -
2018 Poster: Extreme Learning to Rank via Low Rank Assumption »
Minhao Cheng · Ian Davidson · Cho-Jui Hsieh -
2018 Oral: Extreme Learning to Rank via Low Rank Assumption »
Minhao Cheng · Ian Davidson · Cho-Jui Hsieh -
2018 Oral: SQL-Rank: A Listwise Approach to Collaborative Ranking »
LIWEI WU · Cho-Jui Hsieh · University of California James Sharpnack -
2018 Oral: Learning long term dependencies via Fourier recurrent units »
Jiong Zhang · Yibo Lin · Zhao Song · Inderjit Dhillon -
2018 Poster: Stabilizing Gradients for Deep Neural Networks via Efficient SVD Parameterization »
Jiong Zhang · Qi Lei · Inderjit Dhillon -
2018 Poster: Fast Variance Reduction Method with Stochastic Batch Size »
University of California Xuanqing Liu · Cho-Jui Hsieh -
2018 Oral: Stabilizing Gradients for Deep Neural Networks via Efficient SVD Parameterization »
Jiong Zhang · Qi Lei · Inderjit Dhillon -
2018 Oral: Fast Variance Reduction Method with Stochastic Batch Size »
University of California Xuanqing Liu · Cho-Jui Hsieh -
2017 Poster: Gradient Boosted Decision Trees for High Dimensional Sparse Output »
Si Si · Huan Zhang · Sathiya Keerthi · Dhruv Mahajan · Inderjit Dhillon · Cho-Jui Hsieh -
2017 Talk: Gradient Boosted Decision Trees for High Dimensional Sparse Output »
Si Si · Huan Zhang · Sathiya Keerthi · Dhruv Mahajan · Inderjit Dhillon · Cho-Jui Hsieh -
2017 Poster: Recovery Guarantees for One-hidden-layer Neural Networks »
Kai Zhong · Zhao Song · Prateek Jain · Peter Bartlett · Inderjit Dhillon -
2017 Poster: Doubly Greedy Primal-Dual Coordinate Descent for Sparse Empirical Risk Minimization »
Qi Lei · En-Hsu Yen · Chao-Yuan Wu · Inderjit Dhillon · Pradeep Ravikumar -
2017 Talk: Doubly Greedy Primal-Dual Coordinate Descent for Sparse Empirical Risk Minimization »
Qi Lei · En-Hsu Yen · Chao-Yuan Wu · Inderjit Dhillon · Pradeep Ravikumar -
2017 Talk: Recovery Guarantees for One-hidden-layer Neural Networks »
Kai Zhong · Zhao Song · Prateek Jain · Peter Bartlett · Inderjit Dhillon