b'Aarti Singh'

research

∙ 04/24/2022

Complete Policy Regret Bounds for Tallying Bandits

Policy regret is a well established notion of measuring the performance ...

Dhruv Malik, et al. ∙

research

∙ 04/05/2022

Integrating Rankings into Quantized Scores in Peer Review

In peer review, reviewers are usually asked to provide scores for the pa...

Yusha Liu, et al. ∙

research

∙ 12/08/2021

Best Arm Identification under Additive Transfer Bandits

We consider a variant of the best arm identification (BAI) problem in mu...

Ojash Neopane, et al. ∙

research

∙ 12/11/2020

Smooth Bandit Optimization: Generalization to Hölder Space

We consider bandit optimization of a smooth reward function, where the g...

Yusha Liu, et al. ∙

research

∙ 11/30/2020

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

Peer review is the backbone of academia and humans constitute a cornerst...

Ivan Stelmakh, et al. ∙

research

∙ 11/30/2020

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

Conference peer review constitutes a human-computation process whose imp...

Ivan Stelmakh, et al. ∙

research

∙ 11/30/2020

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

Modern machine learning and computer science conferences are experiencin...

Ivan Stelmakh, et al. ∙

research

∙ 10/08/2020

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

We consider the issue of strategic behaviour in various peer-assessment ...

Ivan Stelmakh, et al. ∙

research

∙ 06/21/2020

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

A number of applications require two-sample testing on ranked preference...

Charvi Rastogi, et al. ∙

research

∙ 06/16/2020

Preference-based Reinforcement Learning with Finite-Time Guarantees

Preference-based Reinforcement Learning (PbRL) replaces reward values in...

Yichong Xu, et al. ∙

research

∙ 12/31/2019

On Testing for Biases in Peer Review

We consider the issue of biases in scholarly research, specifically, in ...

Ivan Stelmakh, et al. ∙

research

∙ 11/03/2019

Zeroth Order Non-convex optimization with Dueling-Choice Bandits

We consider a novel setting of zeroth order non-convex optimization, whe...

Yichong Xu, et al. ∙

research

∙ 10/16/2019

Active Learning for Graph Neural Networks via Node Feature Propagation

Graph Neural Networks (GNNs) for prediction tasks like node classificati...

Yuexin Wu, et al. ∙

research

∙ 10/14/2019

Thresholding Bandit Problem with Both Duels and Pulls

The Thresholding Bandit Problem (TBP) aims to find the set of arms with ...

Yichong Xu, et al. ∙

research

∙ 10/25/2018

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

An important task in structural design is to quantify the structural per...

Yining Wang, et al. ∙

research

∙ 10/04/2018

Gradient Descent Provably Optimizes Over-parameterized Neural Networks

One of the mystery in the success of neural networks is randomly initial...

Simon S. Du, et al. ∙

research

∙ 06/16/2018

PeerReview4All: Fair and Accurate Reviewer Assignment in Peer Review

We consider the problem of automated assignment of papers to reviewers i...

Ivan Stelmakh, et al. ∙

research

∙ 06/08/2018

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

In supervised learning, we leverage a labeled dataset to design methods ...

Yichong Xu, et al. ∙

research

∙ 05/26/2018

Robust Nonparametric Regression under Huber's ε-contamination Model

We consider the non-parametric regression problem under Huber's ϵ-contam...

Simon S. Du, et al. ∙

research

∙ 05/21/2018

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

A widespread folklore for explaining the success of convolutional neural...

Simon S. Du, et al. ∙

research

∙ 04/22/2018

Local White Matter Architecture Defines Functional Brain Dynamics

Large bundles of myelinated axons, called white matter, anatomically con...

Yo Joong Choe, et al. ∙

research

∙ 03/22/2018

Optimization of Smooth Functions with Noisy Observations: Local Minimax Rates

We consider the problem of global optimization of an unknown non-convex ...

Yining Wang, et al. ∙

research

∙ 02/13/2018

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

A major challenge in understanding the generalization of deep learning i...

Yifan Wu, et al. ∙

research

∙ 12/03/2017

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

We consider the problem of learning a one-hidden-layer neural network wi...

Simon S. Du, et al. ∙

research

∙ 11/14/2017

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

The experimental design problem concerns the selection of k points from ...

Zeyuan Allen-Zhu, et al. ∙

research

∙ 10/29/2017

Stochastic Zeroth-order Optimization in High Dimensions

We consider the problem of optimizing a high-dimensional convex function...

Yining Wang, et al. ∙

research

∙ 05/29/2017

Gradient Descent Can Take Exponential Time to Escape Saddle Points

Although gradient descent (GD) almost always escapes saddle points asymp...

Simon S. Du, et al. ∙

research

∙ 04/19/2017

Noise-Tolerant Interactive Learning from Pairwise Comparisons

We study the problem of interactively learning a binary classifier using...

Yichong Xu, et al. ∙

research

∙ 02/24/2017

Computationally Efficient Robust Estimation of Sparse Functionals

Many conventional statistical procedures are extremely sensitive to seem...

Simon S. Du, et al. ∙

research

∙ 02/22/2017

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

We show that given an estimate A that is close to a general high-rank po...

Simon S. Du, et al. ∙

research

∙ 02/09/2017

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

Although a majority of the theoretical literature in high-dimensional st...

Yining Wang, et al. ∙

research

∙ 10/24/2016

A Theoretical Analysis of Noisy Sparse Subspace Clustering on Dimensionality-Reduced Data

Subspace clustering is the problem of partitioning unlabeled data points...

Yining Wang, et al. ∙

research

∙ 02/06/2016

Classification Accuracy as a Proxy for Two Sample Testing

When data analysts train a classifier and check if its accuracy is signi...

Aaditya Ramdas, et al. ∙

research

∙ 02/01/2016

Active Learning Algorithms for Graphical Model Selection

The problem of learning the structure of a high dimensional graphical mo...

Gautam Dasarathy, et al. ∙

research

∙ 01/23/2016

Minimax Lower Bounds for Linear Independence Testing

Linear independence testing is a fundamental information-theoretic and s...

Aaditya Ramdas, et al. ∙

research

∙ 01/09/2016

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

We derive computationally tractable methods to select a small subset of ...

Yining Wang, et al. ∙

research

∙ 12/16/2015

Signal Representations on Graphs: Tools and Applications

We present a framework for representing and modeling data on graphs. Bas...

Siheng Chen, et al. ∙

research

∙ 08/04/2015

Adaptivity and Computation-Statistics Tradeoffs for Kernel and Distance based High Dimensional Two Sample Testing

Nonparametric two sample testing is a decision theoretic problem that in...

Aaditya Ramdas, et al. ∙

research

∙ 06/02/2015

Extreme Compressive Sampling for Covariance Estimation

This paper studies the problem of estimating the covariance of a collect...

Martin Azizyan, et al. ∙

research

∙ 05/17/2015

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

We consider the problem of matrix column subset selection, which selects...

Yining Wang, et al. ∙

research

∙ 05/15/2015

An Analysis of Active Learning With Uniform Feature Noise

In active learning, the user sequentially chooses values for feature X a...

Aaditya Ramdas, et al. ∙

research

∙ 05/15/2015

Algorithmic Connections Between Active Learning and Stochastic Convex Optimization

Interesting theoretical associations have been established by recent pap...

Aaditya Ramdas, et al. ∙

research

∙ 05/03/2015

Risk Bounds For Mode Clustering

Density mode clustering is a nonparametric clustering method. The cluste...

Martin Azizyan, et al. ∙

research

∙ 04/21/2015

Signal Recovery on Graphs: Random versus Experimentally Designed Sampling

We study signal recovery on graphs based on two sampling strategies: ran...

Siheng Chen, et al. ∙

research

∙ 04/04/2015

Graph Connectivity in Noisy Sparse Subspace Clustering

Subspace clustering is the problem of clustering data points into a unio...

Yining Wang, et al. ∙

research

∙ 11/23/2014

On the High-dimensional Power of Linear-time Kernel Two-Sample Testing under Mean-difference Alternatives

Nonparametric two sample testing deals with the question of consistently...

Aaditya Ramdas, et al. ∙

research

∙ 07/14/2014

On the Power of Adaptivity in Matrix Completion and Approximation

We consider the related tasks of matrix completion and matrix approximat...

Akshay Krishnamurthy, et al. ∙

research

∙ 06/20/2014

Noise-adaptive Margin-based Active Learning and Lower Bounds under Tsybakov Noise Condition

We present a simple noise-robust margin-based active learning algorithm ...

Yining Wang, et al. ∙

research

∙ 04/03/2014

Subspace Learning from Extremely Compressed Measurements

We consider learning the principal subspace of a large set of vectors fr...

Akshay Krishnamurthy, et al. ∙

research

∙ 12/11/2013

Near-optimal Anomaly Detection in Graphs using Lovasz Extended Scan Statistic

The detection of anomalous activity in graphs is a statistical problem t...

James Sharpnack, et al. ∙

Aarti Singh

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