Aarti Singh

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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* 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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Aarti Singh

Smooth Bandit Optimization: Generalization to Hölder Space

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

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

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

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

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

Preference-based Reinforcement Learning with Finite-Time Guarantees

Zeroth Order Non-convex optimization with Dueling-Choice Bandits

Active Learning for Graph Neural Networks via Node Feature Propagation

Thresholding Bandit Problem with Both Duels and Pulls

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

Local White Matter Architecture Defines Functional Brain Dynamics

PeerReview4All: Fair and Accurate Reviewer Assignment in Peer Review

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

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

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

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

Optimization of Smooth Functions with Noisy Observations: Local Minimax Rates

Stochastic Zeroth-order Optimization in High Dimensions

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

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

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

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

Gradient Descent Can Take Exponential Time to Escape Saddle Points

Hypothesis Transfer Learning via Transformation Functions

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

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

Noise-Tolerant Interactive Learning from Pairwise Comparisons

Computationally Efficient Robust Estimation of Sparse Functionals

Subspace Learning from Extremely Compressed Measurements