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Smooth Bandit Optimization: Generalization to Hölder Space


Dec 11, 2020
Yusha Liu, Yining Wang, Aarti Singh

* 11 main pages, 2 figures, 13 appendix pages 

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A Large Scale Randomized Controlled Trial on Herding in Peer-Review Discussions


Nov 30, 2020
Ivan Stelmakh, Charvi Rastogi, Nihar B. Shah, Aarti Singh, Hal Daumé III


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A Novice-Reviewer Experiment to Address Scarcity of Qualified Reviewers in Large Conferences


Nov 30, 2020
Ivan Stelmakh, Nihar B. Shah, Aarti Singh, Hal Daumé III


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Prior and Prejudice: The Novice Reviewers' Bias against Resubmissions in Conference Peer Review


Nov 30, 2020
Ivan Stelmakh, Nihar B. Shah, Aarti Singh, Hal Daumé III


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Catch Me if I Can: Detecting Strategic Behaviour in Peer Assessment


Oct 08, 2020
Ivan Stelmakh, Nihar B. Shah, Aarti Singh


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Two-Sample Testing on Ranked Preference Data and the Role of Modeling Assumptions


Jun 21, 2020
Charvi Rastogi, Sivaraman Balakrishnan, Nihar B. Shah, Aarti Singh

* 36 pages, 4 figures 

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Preference-based Reinforcement Learning with Finite-Time Guarantees


Jun 16, 2020
Yichong Xu, Ruosong Wang, Lin F. Yang, Aarti Singh, Artur Dubrawski

* 22 pages, 2 figures 

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Zeroth Order Non-convex optimization with Dueling-Choice Bandits


Nov 03, 2019
Yichong Xu, Aparna Joshi, Aarti Singh, Artur Dubrawski

* 19 pages, 3 figures 

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Active Learning for Graph Neural Networks via Node Feature Propagation


Oct 16, 2019
Yuexin Wu, Yichong Xu, Aarti Singh, Yiming Yang, Artur Dubrawski

* 16 pages, 5 figures 

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Thresholding Bandit Problem with Both Duels and Pulls


Oct 14, 2019
Yichong Xu, Xi Chen, Aarti Singh, Artur Dubrawski

* 21 pages, 8 figures 

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Efficient Load Sampling for Worst-Case Structural Analysis Under Force Location Uncertainty


Oct 25, 2018
Yining Wang, Erva Ulu, Aarti Singh, Levent Burak Kara

* Proceedings of the ASME 2018 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference (IDETC/CIE 2018) (In Print) 

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Local White Matter Architecture Defines Functional Brain Dynamics


Sep 16, 2018
Yo Joong Choe, Sivaraman Balakrishnan, Aarti Singh, Jean M. Vettel, Timothy Verstynen

* Accepted to the 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2018) 

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PeerReview4All: Fair and Accurate Reviewer Assignment in Peer Review


Jun 16, 2018
Ivan Stelmakh, Nihar B. Shah, Aarti Singh


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Gradient Descent Learns One-hidden-layer CNN: Don't be Afraid of Spurious Local Minima


Jun 15, 2018
Simon S. Du, Jason D. Lee, Yuandong Tian, Barnabas Poczos, Aarti Singh

* Accepted by ICML 2018 

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Nonparametric Regression with Comparisons: Escaping the Curse of Dimensionality with Ordinal Information


Jun 08, 2018
Yichong Xu, Hariank Muthakana, Sivaraman Balakrishnan, Aarti Singh, Artur Dubrawski

* 21 pages, 5 figures; International Conference on Machine Learning 2018 

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Robust Nonparametric Regression under Huber's $Δ$-contamination Model


May 26, 2018
Simon S. Du, Yining Wang, Sivaraman Balakrishnan, Pradeep Ravikumar, Aarti Singh


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How Many Samples are Needed to Learn a Convolutional Neural Network?


May 21, 2018
Simon S. Du, Yining Wang, Xiyu Zhai, Sivaraman Balakrishnan, Ruslan Salakhutdinov, Aarti Singh


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Optimization of Smooth Functions with Noisy Observations: Local Minimax Rates


Mar 22, 2018
Yining Wang, Sivaraman Balakrishnan, Aarti Singh

* 29 pages, 1 figure 

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Stochastic Zeroth-order Optimization in High Dimensions


Feb 26, 2018
Yining Wang, Simon Du, Sivaraman Balakrishnan, Aarti Singh

* Camera-ready version at AISTATS 2018 

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Towards Understanding the Generalization Bias of Two Layer Convolutional Linear Classifiers with Gradient Descent


Feb 13, 2018
Yifan Wu, Barnabas Poczos, Aarti Singh


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Provably Correct Algorithms for Matrix Column Subset Selection with Selectively Sampled Data


Jan 25, 2018
Yining Wang, Aarti Singh

* 42 pages. Accepted to Journal of Machine Learning Research 

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On Computationally Tractable Selection of Experiments in Measurement-Constrained Regression Models


Dec 20, 2017
Yining Wang, Adams Wei Yu, Aarti Singh

* 41 pages. Accepted for publication in Journal of Machine Learning Research 

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Near-Optimal Discrete Optimization for Experimental Design: A Regret Minimization Approach


Nov 14, 2017
Zeyuan Allen-Zhu, Yuanzhi Li, Aarti Singh, Yining Wang

* 33 pages, 4 tables. A preliminary version of this paper titled "Near-Optimal Experimental Design via Regret Minimization" with weaker results appeared in the Proceedings of the 34th International Conference on Machine Learning (ICML 2017), Sydney 

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Gradient Descent Can Take Exponential Time to Escape Saddle Points


Nov 05, 2017
Simon S. Du, Chi Jin, Jason D. Lee, Michael I. Jordan, Barnabas Poczos, Aarti Singh

* Accepted by NIPS 2017 

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Hypothesis Transfer Learning via Transformation Functions


Nov 05, 2017
Simon Shaolei Du, Jayanth Koushik, Aarti Singh, Barnabas Poczos

* Accepted by NIPS 2017 

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On the Power of Truncated SVD for General High-rank Matrix Estimation Problems


Nov 05, 2017
Simon S. Du, Yining Wang, Aarti Singh

* Accepted by NIPS 2017. Add gap-dependent bounds 

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Rate Optimal Estimation and Confidence Intervals for High-dimensional Regression with Missing Covariates


Nov 03, 2017
Yining Wang, Jialei Wang, Sivaraman Balakrishnan, Aarti Singh

* 41 pages, 1 figure, 3 tables 

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Noise-Tolerant Interactive Learning from Pairwise Comparisons


May 19, 2017
Yichong Xu, Hongyang Zhang, Aarti Singh, Kyle Miller, Artur Dubrawski

* 28 pages, 1 figure, 3 tables 

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Computationally Efficient Robust Estimation of Sparse Functionals


Feb 24, 2017
Simon S. Du, Sivaraman Balakrishnan, Aarti Singh


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Subspace Learning from Extremely Compressed Measurements


Dec 12, 2016
Akshay Krishnamurthy, Martin Azizyan, Aarti Singh


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