Aarti Singh

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Carnegie Mellon University

Associate Professor, A. Nico Habermann Faculty Chair 2013-2016 Machine Learning Department at Carnegie Mellon University

Smooth Bandit Optimization: Generalization to Hölder Space

We consider bandit optimization of a smooth reward function, where the g...
12/11/2020 ∙ by Yusha Liu, et al. ∙ 0 ∙ share

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

Peer review is the backbone of academia and humans constitute a cornerst...
11/30/2020 ∙ by Ivan Stelmakh, et al. ∙ 0 ∙ share

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

Conference peer review constitutes a human-computation process whose imp...
11/30/2020 ∙ by Ivan Stelmakh, et al. ∙ 0 ∙ share

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

Modern machine learning and computer science conferences are experiencin...
11/30/2020 ∙ by Ivan Stelmakh, et al. ∙ 0 ∙ share

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

We consider the issue of strategic behaviour in various peer-assessment ...
10/08/2020 ∙ by Ivan Stelmakh, et al. ∙ 0 ∙ share

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

A number of applications require two-sample testing on ranked preference...
06/21/2020 ∙ by Charvi Rastogi, et al. ∙ 0 ∙ share

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Preference-based Reinforcement Learning with Finite-Time Guarantees

Preference-based Reinforcement Learning (PbRL) replaces reward values in...
06/16/2020 ∙ by Yichong Xu, et al. ∙ 0 ∙ share

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On Testing for Biases in Peer Review

We consider the issue of biases in scholarly research, specifically, in ...
12/31/2019 ∙ by Ivan Stelmakh, et al. ∙ 0 ∙ share

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Zeroth Order Non-convex optimization with Dueling-Choice Bandits

We consider a novel setting of zeroth order non-convex optimization, whe...
11/03/2019 ∙ by Yichong Xu, et al. ∙ 0 ∙ share

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Active Learning for Graph Neural Networks via Node Feature Propagation

Graph Neural Networks (GNNs) for prediction tasks like node classificati...
10/16/2019 ∙ by Yuexin Wu, et al. ∙ 0 ∙ share

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Thresholding Bandit Problem with Both Duels and Pulls

The Thresholding Bandit Problem (TBP) aims to find the set of arms with ...
10/14/2019 ∙ by Yichong Xu, et al. ∙ 0 ∙ share

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

An important task in structural design is to quantify the structural per...
10/25/2018 ∙ by Yining Wang, et al. ∙ 0 ∙ share

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Gradient Descent Provably Optimizes Over-parameterized Neural Networks

One of the mystery in the success of neural networks is randomly initial...
10/04/2018 ∙ by Simon S. Du, et al. ∙ 30 ∙ share

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PeerReview4All: Fair and Accurate Reviewer Assignment in Peer Review

We consider the problem of automated assignment of papers to reviewers i...
06/16/2018 ∙ by Ivan Stelmakh, et al. ∙ 0 ∙ share

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

In supervised learning, we leverage a labeled dataset to design methods ...
06/08/2018 ∙ by Yichong Xu, et al. ∙ 0 ∙ share

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Robust Nonparametric Regression under Huber's ε-contamination Model

We consider the non-parametric regression problem under Huber's ϵ-contam...
05/26/2018 ∙ by Simon S. Du, et al. ∙ 0 ∙ share

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

A widespread folklore for explaining the success of convolutional neural...
05/21/2018 ∙ by Simon S. Du, et al. ∙ 0 ∙ share

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Local White Matter Architecture Defines Functional Brain Dynamics

Large bundles of myelinated axons, called white matter, anatomically con...
04/22/2018 ∙ by Yo Joong Choe, et al. ∙ 0 ∙ share

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Optimization of Smooth Functions with Noisy Observations: Local Minimax Rates

We consider the problem of global optimization of an unknown non-convex ...
03/22/2018 ∙ by Yining Wang, et al. ∙ 0 ∙ share

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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...
02/13/2018 ∙ by Yifan Wu, et al. ∙ 0 ∙ share

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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...
12/03/2017 ∙ by Simon S. Du, et al. ∙ 0 ∙ share

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

The experimental design problem concerns the selection of k points from ...
11/14/2017 ∙ by Zeyuan Allen-Zhu, et al. ∙ 0 ∙ share

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Stochastic Zeroth-order Optimization in High Dimensions

We consider the problem of optimizing a high-dimensional convex function...
10/29/2017 ∙ by Yining Wang, et al. ∙ 0 ∙ share

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Gradient Descent Can Take Exponential Time to Escape Saddle Points

Although gradient descent (GD) almost always escapes saddle points asymp...
05/29/2017 ∙ by Simon S. Du, et al. ∙ 0 ∙ share

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Noise-Tolerant Interactive Learning from Pairwise Comparisons

We study the problem of interactively learning a binary classifier using...
04/19/2017 ∙ by Yichong Xu, et al. ∙ 0 ∙ share

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Computationally Efficient Robust Estimation of Sparse Functionals

Many conventional statistical procedures are extremely sensitive to seem...
02/24/2017 ∙ by Simon S. Du, et al. ∙ 0 ∙ share

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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...
02/22/2017 ∙ by Simon S. Du, et al. ∙ 0 ∙ share

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

Although a majority of the theoretical literature in high-dimensional st...
02/09/2017 ∙ by Yining Wang, et al. ∙ 0 ∙ share

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

Subspace clustering is the problem of partitioning unlabeled data points...
10/24/2016 ∙ by Yining Wang, et al. ∙ 0 ∙ share

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Classification Accuracy as a Proxy for Two Sample Testing

When data analysts train a classifier and check if its accuracy is signi...
02/06/2016 ∙ by Aaditya Ramdas, et al. ∙ 0 ∙ share

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Active Learning Algorithms for Graphical Model Selection

The problem of learning the structure of a high dimensional graphical mo...
02/01/2016 ∙ by Gautam Dasarathy, et al. ∙ 0 ∙ share

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Minimax Lower Bounds for Linear Independence Testing

Linear independence testing is a fundamental information-theoretic and s...
01/23/2016 ∙ by Aaditya Ramdas, et al. ∙ 0 ∙ share

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

We derive computationally tractable methods to select a small subset of ...
01/09/2016 ∙ by Yining Wang, et al. ∙ 0 ∙ share

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Signal Representations on Graphs: Tools and Applications

We present a framework for representing and modeling data on graphs. Bas...
12/16/2015 ∙ by Siheng Chen, et al. ∙ 0 ∙ share

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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...
08/04/2015 ∙ by Aaditya Ramdas, et al. ∙ 0 ∙ share

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Extreme Compressive Sampling for Covariance Estimation

This paper studies the problem of estimating the covariance of a collect...
06/02/2015 ∙ by Martin Azizyan, et al. ∙ 0 ∙ share

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

We consider the problem of matrix column subset selection, which selects...
05/17/2015 ∙ by Yining Wang, et al. ∙ 0 ∙ share

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An Analysis of Active Learning With Uniform Feature Noise

In active learning, the user sequentially chooses values for feature X a...
05/15/2015 ∙ by Aaditya Ramdas, et al. ∙ 0 ∙ share

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Algorithmic Connections Between Active Learning and Stochastic Convex Optimization

Interesting theoretical associations have been established by recent pap...
05/15/2015 ∙ by Aaditya Ramdas, et al. ∙ 0 ∙ share

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Risk Bounds For Mode Clustering

Density mode clustering is a nonparametric clustering method. The cluste...
05/03/2015 ∙ by Martin Azizyan, et al. ∙ 0 ∙ share

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Signal Recovery on Graphs: Random versus Experimentally Designed Sampling

We study signal recovery on graphs based on two sampling strategies: ran...
04/21/2015 ∙ by Siheng Chen, et al. ∙ 0 ∙ share

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Graph Connectivity in Noisy Sparse Subspace Clustering

Subspace clustering is the problem of clustering data points into a unio...
04/04/2015 ∙ by Yining Wang, et al. ∙ 0 ∙ share

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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...
11/23/2014 ∙ by Aaditya Ramdas, et al. ∙ 0 ∙ share

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On the Power of Adaptivity in Matrix Completion and Approximation

We consider the related tasks of matrix completion and matrix approximat...
07/14/2014 ∙ by Akshay Krishnamurthy, et al. ∙ 0 ∙ share

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

We present a simple noise-robust margin-based active learning algorithm ...
06/20/2014 ∙ by Yining Wang, et al. ∙ 0 ∙ share

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Subspace Learning from Extremely Compressed Measurements

We consider learning the principal subspace of a large set of vectors fr...
04/03/2014 ∙ by Akshay Krishnamurthy, et al. ∙ 0 ∙ share

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

The detection of anomalous activity in graphs is a statistical problem t...
12/11/2013 ∙ by James Sharpnack, et al. ∙ 0 ∙ share

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Tight Lower Bounds for Homology Inference

The homology groups of a manifold are important topological invariants t...
07/29/2013 ∙ by Sivaraman Balakrishnan, et al. ∙ 0 ∙ share

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Cluster Trees on Manifolds

In this paper we investigate the problem of estimating the cluster tree ...
07/24/2013 ∙ by Sivaraman Balakrishnan, et al. ∙ 0 ∙ share

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Minimax Theory for High-dimensional Gaussian Mixtures with Sparse Mean Separation

While several papers have investigated computationally and statistically...
06/09/2013 ∙ by Martin Azizyan, et al. ∙ 0 ∙ share

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Aarti Singh

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