
Correcting the bias in least squares regression with volumerescaled sampling
Consider linear regression where the examples are generated by an unknow...
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Weakly Supervised Attention Networks for FineGrained Opinion Mining and Public Health
In many review classification applications, a finegrained analysis of t...
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A New Framework for Query Efficient Active Imitation Learning
We seek to align agent policy with human expert behavior in a reinforcem...
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Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate
Many modern machine learning models are trained to achieve zero or near...
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Reconciling modern machine learning and the biasvariance tradeoff
The question of generalization in machine learninghow algorithms are ...
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Two models of double descent for weak features
The "double descent" risk curve was recently proposed to qualitatively d...
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How many variables should be entered in a principal component regression equation?
We study least squares linear regression over N uncorrelated Gaussian fe...
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Leveraging Just a Few Keywords for FineGrained Aspect Detection Through Weakly Supervised CoTraining
Usergenerated reviews can be decomposed into finegrained segments (e.g...
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Diameterbased Interactive Structure Search
In this work, we introduce interactive structure search, a generic frame...
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Unbiased estimators for random design regression
In linear regression we wish to estimate the optimum linear least square...
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Privacy Accounting and Quality Control in the Sage Differentially Private ML Platform
Companies increasingly expose machine learning (ML) models trained over ...
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Benefits of overparameterization with EM
Expectation Maximization (EM) is among the most popular algorithms for m...
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A cryptographic approach to black box adversarial machine learning
We propose an ensemble technique for converting any classifier into a co...
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A gradual, semidiscrete approach to generative network training via explicit wasserstein minimization
This paper provides a simple procedure to fit generative networks to tar...
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Time Series Compression Based on Adaptive Piecewise Recurrent Autoencoder
Time series account for a large proportion of the data stored in financi...
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Time Series Forecasting Based on Augmented Long ShortTerm Memory
In this paper, we use recurrent autoencoder model to predict the time se...
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Mixing time estimation in reversible Markov chains from a single sample path
The spectral gap γ of a finite, ergodic, and reversible Markov chain is ...
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Anomaly Detection on Graph Time Series
In this paper, we use variational recurrent neural network to investigat...
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Parameter identification in Markov chain choice models
This work studies the parameter identification problem for the Markov ch...
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Linear regression without correspondence
This article considers algorithmic and statistical aspects of linear reg...
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Global analysis of Expectation Maximization for mixtures of two Gaussians
Expectation Maximization (EM) is among the most popular algorithms for e...
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Search Improves Label for Active Learning
We investigate active learning with access to two distinct oracles: Labe...
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Scalable Nonlinear Learning with Adaptive Polynomial Expansions
Can we effectively learn a nonlinear representation in time comparable t...
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Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits
We present a new algorithm for the contextual bandit learning problem, w...
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When are Overcomplete Topic Models Identifiable? Uniqueness of Tensor Tucker Decompositions with Structured Sparsity
Overcomplete latent representations have been very popular for unsupervi...
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Loss minimization and parameter estimation with heavy tails
This work studies applications and generalizations of a simple estimatio...
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A Tensor Approach to Learning Mixed Membership Community Models
Community detection is the task of detecting hidden communities from obs...
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Learning Sparse LowThreshold Linear Classifiers
We consider the problem of learning a nonnegative linear classifier wit...
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Analysis of a randomized approximation scheme for matrix multiplication
This note gives a simple analysis of a randomized approximation scheme f...
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Tensor decompositions for learning latent variable models
This work considers a computationally and statistically efficient parame...
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Learning Topic Models and Latent Bayesian Networks Under Expansion Constraints
Unsupervised estimation of latent variable models is a fundamental probl...
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Convergence Rates for Differentially Private Statistical Estimation
Differential privacy is a cryptographicallymotivated definition of priv...
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A concentration theorem for projections
X in R^D has mean zero and finite second moments. We show that there is ...
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Learning mixtures of spherical Gaussians: moment methods and spectral decompositions
This work provides a computationally efficient and statistically consist...
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A Spectral Algorithm for Latent Dirichlet Allocation
The problem of topic modeling can be seen as a generalization of the clu...
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An Online Learningbased Framework for Tracking
We study the tracking problem, namely, estimating the hidden state of an...
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A Method of Moments for Mixture Models and Hidden Markov Models
Mixture models are a fundamental tool in applied statistics and machine ...
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Spectral Methods for Learning Multivariate Latent Tree Structure
This work considers the problem of learning the structure of multivariat...
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Efficient Optimal Learning for Contextual Bandits
We address the problem of learning in an online setting where the learne...
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Random design analysis of ridge regression
This work gives a simultaneous analysis of both the ordinary least squar...
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Dimensionfree tail inequalities for sums of random matrices
We derive exponential tail inequalities for sums of random matrices with...
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Robust Matrix Decomposition with Outliers
Suppose a given observation matrix can be decomposed as the sum of a low...
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Tracking using explanationbased modeling
We study the tracking problem, namely, estimating the hidden state of an...
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Kernel Approximation Methods for Speech Recognition
We study largescale kernel methods for acoustic modeling in speech reco...
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NonGaussian information from weak lensing data via deep learning
Weak lensing maps contain information beyond twopoint statistics on sma...
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Tail bounds for volume sampled linear regression
The n × d design matrix in a linear regression problem is given, but the...
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On the Connection between Differential Privacy and Adversarial Robustness in Machine Learning
Adversarial examples in machine learning has been a topic of intense res...
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Successive RankOne Approximations for Nearly Orthogonally Decomposable Symmetric Tensors
Many idealized problems in signal processing, machine learning and stati...
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Greedy Approaches to Symmetric Orthogonal Tensor Decomposition
Finding the symmetric and orthogonal decomposition (SOD) of a tensor is ...
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Consistent Risk Estimation in HighDimensional Linear Regression
Risk estimation is at the core of many learning systems. The importance ...
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