
Catch Me if I Can: Detecting Strategic Behaviour in Peer Assessment
We consider the issue of strategic behaviour in various peerassessment ...
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TwoSample Testing on Ranked Preference Data and the Role of Modeling Assumptions
A number of applications require twosample testing on ranked preference...
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Preferencebased Reinforcement Learning with FiniteTime Guarantees
Preferencebased Reinforcement Learning (PbRL) replaces reward values in...
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On Testing for Biases in Peer Review
We consider the issue of biases in scholarly research, specifically, in ...
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Zeroth Order Nonconvex optimization with DuelingChoice Bandits
We consider a novel setting of zeroth order nonconvex optimization, whe...
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Active Learning for Graph Neural Networks via Node Feature Propagation
Graph Neural Networks (GNNs) for prediction tasks like node classificati...
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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 ...
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Efficient Load Sampling for WorstCase Structural Analysis Under Force Location Uncertainty
An important task in structural design is to quantify the structural per...
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Gradient Descent Provably Optimizes Overparameterized Neural Networks
One of the mystery in the success of neural networks is randomly initial...
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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...
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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 ...
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Robust Nonparametric Regression under Huber's εcontamination Model
We consider the nonparametric regression problem under Huber's ϵcontam...
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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...
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Local White Matter Architecture Defines Functional Brain Dynamics
Large bundles of myelinated axons, called white matter, anatomically con...
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Optimization of Smooth Functions with Noisy Observations: Local Minimax Rates
We consider the problem of global optimization of an unknown nonconvex ...
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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...
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Gradient Descent Learns Onehiddenlayer CNN: Don't be Afraid of Spurious Local Minima
We consider the problem of learning a onehiddenlayer neural network wi...
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NearOptimal Discrete Optimization for Experimental Design: A Regret Minimization Approach
The experimental design problem concerns the selection of k points from ...
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Stochastic Zerothorder Optimization in High Dimensions
We consider the problem of optimizing a highdimensional convex function...
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Gradient Descent Can Take Exponential Time to Escape Saddle Points
Although gradient descent (GD) almost always escapes saddle points asymp...
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NoiseTolerant Interactive Learning from Pairwise Comparisons
We study the problem of interactively learning a binary classifier using...
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Computationally Efficient Robust Estimation of Sparse Functionals
Many conventional statistical procedures are extremely sensitive to seem...
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On the Power of Truncated SVD for General Highrank Matrix Estimation Problems
We show that given an estimate A that is close to a general highrank po...
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Rate Optimal Estimation and Confidence Intervals for Highdimensional Regression with Missing Covariates
Although a majority of the theoretical literature in highdimensional st...
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A Theoretical Analysis of Noisy Sparse Subspace Clustering on DimensionalityReduced Data
Subspace clustering is the problem of partitioning unlabeled data points...
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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...
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Active Learning Algorithms for Graphical Model Selection
The problem of learning the structure of a high dimensional graphical mo...
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Minimax Lower Bounds for Linear Independence Testing
Linear independence testing is a fundamental informationtheoretic and s...
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On Computationally Tractable Selection of Experiments in MeasurementConstrained Regression Models
We derive computationally tractable methods to select a small subset of ...
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Signal Representations on Graphs: Tools and Applications
We present a framework for representing and modeling data on graphs. Bas...
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Adaptivity and ComputationStatistics Tradeoffs for Kernel and Distance based High Dimensional Two Sample Testing
Nonparametric two sample testing is a decision theoretic problem that in...
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Extreme Compressive Sampling for Covariance Estimation
This paper studies the problem of estimating the covariance of a collect...
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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...
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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...
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Algorithmic Connections Between Active Learning and Stochastic Convex Optimization
Interesting theoretical associations have been established by recent pap...
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Risk Bounds For Mode Clustering
Density mode clustering is a nonparametric clustering method. The cluste...
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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...
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Graph Connectivity in Noisy Sparse Subspace Clustering
Subspace clustering is the problem of clustering data points into a unio...
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On the Highdimensional Power of Lineartime Kernel TwoSample Testing under Meandifference Alternatives
Nonparametric two sample testing deals with the question of consistently...
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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...
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Noiseadaptive Marginbased Active Learning and Lower Bounds under Tsybakov Noise Condition
We present a simple noiserobust marginbased active learning algorithm ...
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Subspace Learning from Extremely Compressed Measurements
We consider learning the principal subspace of a large set of vectors fr...
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Nearoptimal Anomaly Detection in Graphs using Lovasz Extended Scan Statistic
The detection of anomalous activity in graphs is a statistical problem t...
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Tight Lower Bounds for Homology Inference
The homology groups of a manifold are important topological invariants t...
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Cluster Trees on Manifolds
In this paper we investigate the problem of estimating the cluster tree ...
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Minimax Theory for Highdimensional Gaussian Mixtures with Sparse Mean Separation
While several papers have investigated computationally and statistically...
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Recovering GraphStructured Activations using Adaptive Compressive Measurements
We study the localization of a cluster of activated vertices in a graph,...
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LowRank Matrix and Tensor Completion via Adaptive Sampling
We study low rank matrix and tensor completion and propose novel algorit...
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DistributionFree Distribution Regression
`Distribution regression' refers to the situation where a response Y dep...
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Recovering Blockstructured Activations Using Compressive Measurements
We consider the problems of detection and localization of a contiguous b...
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Aarti Singh
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Associate Professor, A. Nico Habermann Faculty Chair 20132016 Machine Learning Department at Carnegie Mellon University