Search Results for author: Aarti Singh

Found 77 papers, 3 papers with code

Complete Policy Regret Bounds for Tallying Bandits

no code implementations24 Apr 2022 Dhruv Malik, Yuanzhi Li, Aarti Singh

Policy regret is a well established notion of measuring the performance of an online learning algorithm against an adaptive adversary.

online learning

Integrating Rankings into Quantized Scores in Peer Review

1 code implementation5 Apr 2022 Yusha Liu, Yichong Xu, Nihar B. Shah, Aarti Singh

Our approach addresses the two aforementioned challenges by: (i) ensuring that rankings are incorporated into the updates scores in the same manner for all papers, thereby mitigating arbitrariness, and (ii) allowing to seamlessly use existing interfaces and workflows designed for scores.

Decision Making

Best Arm Identification under Additive Transfer Bandits

no code implementations8 Dec 2021 Ojash Neopane, Aaditya Ramdas, Aarti Singh

We consider a variant of the best arm identification (BAI) problem in multi-armed bandits (MAB) in which there are two sets of arms (source and target), and the objective is to determine the best target arm while only pulling source arms.

Multi-Armed Bandits Transfer Learning

Local Signal Adaptivity: Provable Feature Learning in Neural Networks Beyond Kernels

1 code implementation NeurIPS 2021 Stefani Karp, Ezra Winston, Yuanzhi Li, Aarti Singh

We therefore propose the "local signal adaptivity" (LSA) phenomenon as one explanation for the superiority of neural networks over kernel methods.

Image Classification

Smooth Bandit Optimization: Generalization to Hölder Space

no code implementations11 Dec 2020 Yusha Liu, Yining Wang, Aarti Singh

We also study adaptation to unknown function smoothness over a continuous scale of H\"older spaces indexed by $\alpha$, with a bandit model selection approach applied with our proposed two-layer algorithms.

Model Selection

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

no code implementations30 Nov 2020 Ivan Stelmakh, Nihar B. Shah, Aarti Singh, Hal Daumé III

Modern machine learning and computer science conferences are experiencing a surge in the number of submissions that challenges the quality of peer review as the number of competent reviewers is growing at a much slower rate.

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

no code implementations30 Nov 2020 Ivan Stelmakh, Nihar B. Shah, Aarti Singh, Hal Daumé III

Conference peer review constitutes a human-computation process whose importance cannot be overstated: not only it identifies the best submissions for acceptance, but, ultimately, it impacts the future of the whole research area by promoting some ideas and restraining others.

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

no code implementations30 Nov 2020 Ivan Stelmakh, Charvi Rastogi, Nihar B. Shah, Aarti Singh, Hal Daumé III

Peer review is the backbone of academia and humans constitute a cornerstone of this process, being responsible for reviewing papers and making the final acceptance/rejection decisions.

Decision Making

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

no code implementations8 Oct 2020 Ivan Stelmakh, Nihar B. Shah, Aarti Singh

We consider the issue of strategic behaviour in various peer-assessment tasks, including peer grading of exams or homeworks and peer review in hiring or promotions.

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

no code implementations21 Jun 2020 Charvi Rastogi, Sivaraman Balakrishnan, Nihar B. Shah, Aarti Singh

We also provide testing algorithms and associated sample complexity bounds for the problem of two-sample testing with partial (or total) ranking data. Furthermore, we empirically evaluate our results via extensive simulations as well as two real-world datasets consisting of pairwise comparisons.

Two-sample testing

Preference-based Reinforcement Learning with Finite-Time Guarantees

no code implementations NeurIPS 2020 Yichong Xu, Ruosong Wang, Lin F. Yang, Aarti Singh, Artur Dubrawski

If preferences are stochastic, and the preference probability relates to the hidden reward values, we present algorithms for PbRL, both with and without a simulator, that are able to identify the best policy up to accuracy $\varepsilon$ with high probability.

reinforcement-learning

Zeroth Order Non-convex optimization with Dueling-Choice Bandits

no code implementations3 Nov 2019 Yichong Xu, Aparna Joshi, Aarti Singh, Artur Dubrawski

We consider a novel setting of zeroth order non-convex optimization, where in addition to querying the function value at a given point, we can also duel two points and get the point with the larger function value.

Active Learning for Graph Neural Networks via Node Feature Propagation

no code implementations16 Oct 2019 Yuexin Wu, Yichong Xu, Aarti Singh, Yiming Yang, Artur Dubrawski

Graph Neural Networks (GNNs) for prediction tasks like node classification or edge prediction have received increasing attention in recent machine learning from graphically structured data.

Active Learning General Classification +1

Thresholding Bandit Problem with Both Duels and Pulls

no code implementations14 Oct 2019 Yichong Xu, Xi Chen, Aarti Singh, Artur Dubrawski

The Thresholding Bandit Problem (TBP) aims to find the set of arms with mean rewards greater than a given threshold.

Active Learning Graph Neural Networks via Node Feature Propagation

no code implementations25 Sep 2019 Yuexin Wu, Yichong Xu, Aarti Singh, Artur Dubrawski, Yiming Yang

Graph Neural Networks (GNNs) for prediction tasks like node classification or edge prediction have received increasing attention in recent machine learning from graphically structured data.

Active Learning Node Classification

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

no code implementations NeurIPS 2018 Simon S. Du, Yining Wang, Xiyu Zhai, Sivaraman Balakrishnan, Ruslan R. Salakhutdinov, Aarti Singh

We show that for an $m$-dimensional convolutional filter with linear activation acting on a $d$-dimensional input, the sample complexity of achieving population prediction error of $\epsilon$ is $\widetilde{O(m/\epsilon^2)$, whereas the sample-complexity for its FNN counterpart is lower bounded by $\Omega(d/\epsilon^2)$ samples.

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

no code implementations25 Oct 2018 Yining Wang, Erva Ulu, Aarti Singh, Levent Burak Kara

Our approach uses a computationally tractable experimental design method to select number of sample force locations based on geometry only, without inspecting the stress response that requires computationally expensive finite-element analysis.

Experimental Design

Gradient Descent Provably Optimizes Over-parameterized Neural Networks

no code implementations ICLR 2019 Simon S. Du, Xiyu Zhai, Barnabas Poczos, Aarti Singh

One of the mysteries in the success of neural networks is randomly initialized first order methods like gradient descent can achieve zero training loss even though the objective function is non-convex and non-smooth.

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

no code implementations ICML 2018 Yichong Xu, Hariank Muthakana, Sivaraman Balakrishnan, Aarti Singh, Artur Dubrawski

Finally, we present experiments that show the efficacy of RR and investigate its robustness to various sources of noise and model-misspecification.

PeerReview4All: Fair and Accurate Reviewer Assignment in Peer Review

no code implementations16 Jun 2018 Ivan Stelmakh, Nihar B. Shah, Aarti Singh

Our fairness objective is to maximize the review quality of the most disadvantaged paper, in contrast to the commonly used objective of maximizing the total quality over all papers.

Fairness

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

no code implementations ICML 2018 Yichong Xu, Sivaraman Balakrishnan, Aarti Singh, Artur Dubrawski

In supervised learning, we typically leverage a fully labeled dataset to design methods for function estimation or prediction.

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

no code implementations26 May 2018 Simon S. Du, Yining Wang, Sivaraman Balakrishnan, Pradeep Ravikumar, Aarti Singh

We first show that a simple local binning median step can effectively remove the adversary noise and this median estimator is minimax optimal up to absolute constants over the H\"{o}lder function class with smoothness parameters smaller than or equal to 1.

How Many Samples are Needed to Estimate a Convolutional or Recurrent Neural Network?

no code implementations NeurIPS 2018 Simon S. Du, Yining Wang, Xiyu Zhai, Sivaraman Balakrishnan, Ruslan Salakhutdinov, Aarti Singh

It is widely believed that the practical success of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) owes to the fact that CNNs and RNNs use a more compact parametric representation than their Fully-Connected Neural Network (FNN) counterparts, and consequently require fewer training examples to accurately estimate their parameters.

Local White Matter Architecture Defines Functional Brain Dynamics

no code implementations22 Apr 2018 Yo Joong Choe, Sivaraman Balakrishnan, Aarti Singh, Jean M. Vettel, Timothy Verstynen

If communication efficiency is fundamentally constrained by the integrity along the entire length of a white matter bundle, then variability in the functional dynamics of brain networks should be associated with variability in the local connectome.

Variable Selection

Optimization of Smooth Functions with Noisy Observations: Local Minimax Rates

no code implementations NeurIPS 2018 Yining Wang, Sivaraman Balakrishnan, Aarti Singh

In this setup, an algorithm is allowed to adaptively query the underlying function at different locations and receives noisy evaluations of function values at the queried points (i. e. the algorithm has access to zeroth-order information).

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

no code implementations13 Feb 2018 Yifan Wu, Barnabas Poczos, Aarti Singh

A major challenge in understanding the generalization of deep learning is to explain why (stochastic) gradient descent can exploit the network architecture to find solutions that have good generalization performance when using high capacity models.

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

no code implementations ICML 2018 Simon S. Du, Jason D. Lee, Yuandong Tian, Barnabas Poczos, Aarti Singh

We consider the problem of learning a one-hidden-layer neural network with non-overlapping convolutional layer and ReLU activation, i. e., $f(\mathbf{Z}, \mathbf{w}, \mathbf{a}) = \sum_j a_j\sigma(\mathbf{w}^T\mathbf{Z}_j)$, in which both the convolutional weights $\mathbf{w}$ and the output weights $\mathbf{a}$ are parameters to be learned.

Noise-Tolerant Interactive Learning Using Pairwise Comparisons

no code implementations NeurIPS 2017 Yichong Xu, Hongyang Zhang, Kyle Miller, Aarti Singh, Artur Dubrawski

We study the problem of interactively learning a binary classifier using noisy labeling and pairwise comparison oracles, where the comparison oracle answers which one in the given two instances is more likely to be positive.

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

no code implementations14 Nov 2017 Zeyuan Allen-Zhu, Yuanzhi Li, Aarti Singh, Yining Wang

The experimental design problem concerns the selection of k points from a potentially large design pool of p-dimensional vectors, so as to maximize the statistical efficiency regressed on the selected k design points.

Experimental Design

Stochastic Zeroth-order Optimization in High Dimensions

no code implementations29 Oct 2017 Yining Wang, Simon Du, Sivaraman Balakrishnan, Aarti Singh

We consider the problem of optimizing a high-dimensional convex function using stochastic zeroth-order queries.

feature selection

Uncorrelation and Evenness: a New Diversity-Promoting Regularizer

no code implementations ICML 2017 Pengtao Xie, Aarti Singh, Eric P. Xing

Latent space models (LSMs) provide a principled and effective way to extract hidden patterns from observed data.

Near-Optimal Design of Experiments via Regret Minimization

no code implementations ICML 2017 Zeyuan Allen-Zhu, Yuanzhi Li, Aarti Singh, Yining Wang

We consider computationally tractable methods for the experimental design problem, where k out of n design points of dimension p are selected so that certain optimality criteria are approximately satisfied.

Experimental Design

Gradient Descent Can Take Exponential Time to Escape Saddle Points

no code implementations NeurIPS 2017 Simon S. Du, Chi Jin, Jason D. Lee, Michael. I. Jordan, Barnabas Poczos, Aarti Singh

Although gradient descent (GD) almost always escapes saddle points asymptotically [Lee et al., 2016], this paper shows that even with fairly natural random initialization schemes and non-pathological functions, GD can be significantly slowed down by saddle points, taking exponential time to escape.

Noise-Tolerant Interactive Learning from Pairwise Comparisons

no code implementations19 Apr 2017 Yichong Xu, Hongyang Zhang, Aarti Singh, Kyle Miller, Artur Dubrawski

We study the problem of interactively learning a binary classifier using noisy labeling and pairwise comparison oracles, where the comparison oracle answers which one in the given two instances is more likely to be positive.

Computationally Efficient Robust Estimation of Sparse Functionals

no code implementations24 Feb 2017 Simon S. Du, Sivaraman Balakrishnan, Aarti Singh

Many conventional statistical procedures are extremely sensitive to seemingly minor deviations from modeling assumptions.

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

no code implementations NeurIPS 2017 Simon S. Du, Yining Wang, Aarti Singh

This observation leads to many interesting results on general high-rank matrix estimation problems, which we briefly summarize below ($A$ is an $n\times n$ high-rank PSD matrix and $A_k$ is the best rank-$k$ approximation of $A$): (1) High-rank matrix completion: By observing $\Omega(\frac{n\max\{\epsilon^{-4}, k^2\}\mu_0^2\|A\|_F^2\log n}{\sigma_{k+1}(A)^2})$ elements of $A$ where $\sigma_{k+1}\left(A\right)$ is the $\left(k+1\right)$-th singular value of $A$ and $\mu_0$ is the incoherence, the truncated SVD on a zero-filled matrix satisfies $\|\widehat{A}_k-A\|_F \leq (1+O(\epsilon))\|A-A_k\|_F$ with high probability.

Matrix Completion

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

no code implementations9 Feb 2017 Yining Wang, Jialei Wang, Sivaraman Balakrishnan, Aarti Singh

We consider the problems of estimation and of constructing component-wise confidence intervals in a sparse high-dimensional linear regression model when some covariates of the design matrix are missing completely at random.

Hypothesis Transfer Learning via Transformation Functions

no code implementations NeurIPS 2017 Simon Shaolei Du, Jayanth Koushik, Aarti Singh, Barnabas Poczos

We consider the Hypothesis Transfer Learning (HTL) problem where one incorporates a hypothesis trained on the source domain into the learning procedure of the target domain.

Transfer Learning

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

no code implementations24 Oct 2016 Yining Wang, Yu-Xiang Wang, Aarti Singh

Subspace clustering is the problem of partitioning unlabeled data points into a number of clusters so that data points within one cluster lie approximately on a low-dimensional linear subspace.

Dimensionality Reduction

Classification accuracy as a proxy for two sample testing

no code implementations6 Feb 2016 Ilmun Kim, Aaditya Ramdas, Aarti Singh, Larry Wasserman

We prove two results that hold for all classifiers in any dimensions: if its true error remains $\epsilon$-better than chance for some $\epsilon>0$ as $d, n \to \infty$, then (a) the permutation-based test is consistent (has power approaching to one), (b) a computationally efficient test based on a Gaussian approximation of the null distribution is also consistent.

Classification General Classification +1

Active Learning Algorithms for Graphical Model Selection

no code implementations1 Feb 2016 Gautam Dasarathy, Aarti Singh, Maria-Florina Balcan, Jong Hyuk Park

The problem of learning the structure of a high dimensional graphical model from data has received considerable attention in recent years.

Active Learning Model Selection

Minimax Lower Bounds for Linear Independence Testing

no code implementations23 Jan 2016 Aaditya Ramdas, David Isenberg, Aarti Singh, Larry Wasserman

Linear independence testing is a fundamental information-theoretic and statistical problem that can be posed as follows: given $n$ points $\{(X_i, Y_i)\}^n_{i=1}$ from a $p+q$ dimensional multivariate distribution where $X_i \in \mathbb{R}^p$ and $Y_i \in\mathbb{R}^q$, determine whether $a^T X$ and $b^T Y$ are uncorrelated for every $a \in \mathbb{R}^p, b\in \mathbb{R}^q$ or not.

Two-sample testing

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

no code implementations9 Jan 2016 Yining Wang, Adams Wei Yu, Aarti Singh

We derive computationally tractable methods to select a small subset of experiment settings from a large pool of given design points.

Combinatorial Optimization

Signal Representations on Graphs: Tools and Applications

no code implementations16 Dec 2015 Siheng Chen, Rohan Varma, Aarti Singh, Jelena Kovačević

For each class, we provide an explicit definition of the graph signals and construct a corresponding graph dictionary with desirable properties.

Differentially private subspace clustering

no code implementations NeurIPS 2015 Yining Wang, Yu-Xiang Wang, Aarti Singh

Subspace clustering is an unsupervised learning problem that aims at grouping data points into multiple ``clusters'' so that data points in a single cluster lie approximately on a low-dimensional linear subspace.

Motion Segmentation

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

no code implementations4 Aug 2015 Aaditya Ramdas, Sashank J. Reddi, Barnabas Poczos, Aarti Singh, Larry Wasserman

We formally characterize the power of popular tests for GDA like the Maximum Mean Discrepancy with the Gaussian kernel (gMMD) and bandwidth-dependent variants of the Energy Distance with the Euclidean norm (eED) in the high-dimensional MDA regime.

Two-sample testing

A statistical perspective of sampling scores for linear regression

no code implementations21 Jul 2015 Siheng Chen, Rohan Varma, Aarti Singh, Jelena Kovačević

In this paper, we consider a statistical problem of learning a linear model from noisy samples.

Extreme Compressive Sampling for Covariance Estimation

no code implementations2 Jun 2015 Martin Azizyan, Akshay Krishnamurthy, Aarti Singh

This paper studies the problem of estimating the covariance of a collection of vectors using only highly compressed measurements of each vector.

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

no code implementations17 May 2015 Yining Wang, Aarti Singh

We consider the problem of matrix column subset selection, which selects a subset of columns from an input matrix such that the input can be well approximated by the span of the selected columns.

Recommendation Systems

Algorithmic Connections Between Active Learning and Stochastic Convex Optimization

no code implementations15 May 2015 Aaditya Ramdas, Aarti Singh

Combining these two parts yields an algorithm that solves stochastic convex optimization of uniformly convex and smooth functions using only noisy gradient signs by repeatedly performing active learning, achieves optimal rates and is adaptive to all unknown convexity and smoothness parameters.

Active Learning

An Analysis of Active Learning With Uniform Feature Noise

no code implementations15 May 2015 Aaditya Ramdas, Barnabas Poczos, Aarti Singh, Larry Wasserman

For larger $\sigma$, the \textit{unflattening} of the regression function on convolution with uniform noise, along with its local antisymmetry around the threshold, together yield a behaviour where noise \textit{appears} to be beneficial.

Active Learning

Signal Recovery on Graphs: Random versus Experimentally Designed Sampling

no code implementations21 Apr 2015 Siheng Chen, Rohan Varma, Aarti Singh, Jelena Kovačević

We study signal recovery on graphs based on two sampling strategies: random sampling and experimentally designed sampling.

Graph Connectivity in Noisy Sparse Subspace Clustering

no code implementations4 Apr 2015 Yining Wang, Yu-Xiang Wang, Aarti Singh

A line of recent work (4, 19, 24, 20) provided strong theoretical guarantee for sparse subspace clustering (4), the state-of-the-art algorithm for subspace clustering, on both noiseless and noisy data sets.

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

no code implementations23 Nov 2014 Aaditya Ramdas, Sashank J. Reddi, Barnabas Poczos, Aarti Singh, Larry Wasserman

The current literature is split into two kinds of tests - those which are consistent without any assumptions about how the distributions may differ (\textit{general} alternatives), and those which are designed to specifically test easier alternatives, like a difference in means (\textit{mean-shift} alternatives).

Two-sample testing

On the Power of Adaptivity in Matrix Completion and Approximation

no code implementations14 Jul 2014 Akshay Krishnamurthy, Aarti Singh

We show that adaptive sampling allows one to eliminate standard incoherence assumptions on the matrix row space that are necessary for passive sampling procedures.

Matrix Completion

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

no code implementations20 Jun 2014 Yining Wang, Aarti Singh

We present a simple noise-robust margin-based active learning algorithm to find homogeneous (passing the origin) linear separators and analyze its error convergence when labels are corrupted by noise.

Active Learning

Efficient Sparse Clustering of High-Dimensional Non-spherical Gaussian Mixtures

no code implementations9 Jun 2014 Martin Azizyan, Aarti Singh, Larry Wasserman

We consider the problem of clustering data points in high dimensions, i. e. when the number of data points may be much smaller than the number of dimensions.

Subspace Learning from Extremely Compressed Measurements

no code implementations3 Apr 2014 Akshay Krishnamurthy, Martin Azizyan, Aarti Singh

Our theoretical results show that even a constant number of measurements per column suffices to approximate the principal subspace to arbitrary precision, provided that the number of vectors is large.

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

no code implementations NeurIPS 2013 James Sharpnack, Akshay Krishnamurthy, Aarti Singh

The detection of anomalous activity in graphs is a statistical problem that arises in many applications, such as network surveillance, disease outbreak detection, and activity monitoring in social networks.

Anomaly Detection

FuSSO: Functional Shrinkage and Selection Operator

no code implementations10 Nov 2013 Junier B. Oliva, Barnabas Poczos, Timothy Verstynen, Aarti Singh, Jeff Schneider, Fang-Cheng Yeh, Wen-Yih Tseng

We present the FuSSO, a functional analogue to the LASSO, that efficiently finds a sparse set of functional input covariates to regress a real-valued response against.

On the Bootstrap for Persistence Diagrams and Landscapes

1 code implementation2 Nov 2013 Frédéric Chazal, Brittany Terese Fasy, Fabrizio Lecci, Alessandro Rinaldo, Aarti Singh, Larry Wasserman

Persistent homology probes topological properties from point clouds and functions.

Algebraic Topology Computational Geometry Applications

Tight Lower Bounds for Homology Inference

no code implementations29 Jul 2013 Sivaraman Balakrishnan, Alessandro Rinaldo, Aarti Singh, Larry Wasserman

In this note we use a different construction based on the direct analysis of the likelihood ratio test to show that the upper bound of Niyogi, Smale and Weinberger is in fact tight, thus establishing rate optimal asymptotic minimax bounds for the problem.

Cluster Trees on Manifolds

no code implementations NeurIPS 2013 Sivaraman Balakrishnan, Srivatsan Narayanan, Alessandro Rinaldo, Aarti Singh, Larry Wasserman

In this paper we investigate the problem of estimating the cluster tree for a density $f$ supported on or near a smooth $d$-dimensional manifold $M$ isometrically embedded in $\mathbb{R}^D$.

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

no code implementations NeurIPS 2013 Martin Azizyan, Aarti Singh, Larry Wasserman

While several papers have investigated computationally and statistically efficient methods for learning Gaussian mixtures, precise minimax bounds for their statistical performance as well as fundamental limits in high-dimensional settings are not well-understood.

feature selection

Recovering Graph-Structured Activations using Adaptive Compressive Measurements

no code implementations1 May 2013 Akshay Krishnamurthy, James Sharpnack, Aarti Singh

We study the localization of a cluster of activated vertices in a graph, from adaptively designed compressive measurements.

Low-Rank Matrix and Tensor Completion via Adaptive Sampling

no code implementations NeurIPS 2013 Akshay Krishnamurthy, Aarti Singh

In the absence of noise, we show that one can exactly recover a $n \times n$ matrix of rank $r$ from merely $\Omega(n r^{3/2}\log(r))$ matrix entries.

Confidence sets for persistence diagrams

no code implementations28 Mar 2013 Brittany Terese Fasy, Fabrizio Lecci, Alessandro Rinaldo, Larry Wasserman, Sivaraman Balakrishnan, Aarti Singh

Persistent homology is a method for probing topological properties of point clouds and functions.

Recovering Block-structured Activations Using Compressive Measurements

no code implementations15 Sep 2012 Sivaraman Balakrishnan, Mladen Kolar, Alessandro Rinaldo, Aarti Singh

We consider the problems of detection and localization of a contiguous block of weak activation in a large matrix, from a small number of noisy, possibly adaptive, compressive (linear) measurements.

Density-sensitive semisupervised inference

no code implementations7 Apr 2012 Martin Azizyan, Aarti Singh, Larry Wasserman

Semisupervised methods are techniques for using labeled data $(X_1, Y_1),\ldots,(X_n, Y_n)$ together with unlabeled data $X_{n+1},\ldots, X_N$ to make predictions.

Noise Thresholds for Spectral Clustering

no code implementations NeurIPS 2011 Sivaraman Balakrishnan, Min Xu, Akshay Krishnamurthy, Aarti Singh

Although spectral clustering has enjoyed considerable empirical success in machine learning, its theoretical properties are not yet fully developed.

Minimax Localization of Structural Information in Large Noisy Matrices

no code implementations NeurIPS 2011 Mladen Kolar, Sivaraman Balakrishnan, Alessandro Rinaldo, Aarti Singh

We consider the problem of identifying a sparse set of relevant columns and rows in a large data matrix with highly corrupted entries.

Two-sample testing

Identifying graph-structured activation patterns in networks

no code implementations NeurIPS 2010 James Sharpnack, Aarti Singh

We consider the problem of identifying an activation pattern in a complex, large-scale network that is embedded in very noisy measurements.

Unlabeled data: Now it helps, now it doesn't

no code implementations NeurIPS 2008 Aarti Singh, Robert Nowak, Jerry Zhu

We show that there are large classes of problems for which SSL can significantly outperform supervised learning, in finite sample regimes and sometimes also in terms of error convergence rates.

Cannot find the paper you are looking for? You can Submit a new open access paper.