
Correcting the bias in least squares regression with volumerescaled sampling
Consider linear regression where the examples are generated by an unknow...
10/04/2018 ∙ by Michal Derezinski, et al. ∙ 38 ∙ shareread it

Weakly Supervised Attention Networks for FineGrained Opinion Mining and Public Health
In many review classification applications, a finegrained analysis of t...
09/30/2019 ∙ by Giannis Karamanolakis, et al. ∙ 20 ∙ shareread it

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...
06/13/2018 ∙ by Mikhail Belkin, et al. ∙ 8 ∙ shareread it

Reconciling modern machine learning and the biasvariance tradeoff
The question of generalization in machine learninghow algorithms are ...
12/28/2018 ∙ by Mikhail Belkin, et al. ∙ 6 ∙ shareread it

Two models of double descent for weak features
The "double descent" risk curve was recently proposed to qualitatively d...
03/18/2019 ∙ by Mikhail Belkin, et al. ∙ 6 ∙ shareread it

How many variables should be entered in a principal component regression equation?
We study least squares linear regression over N uncorrelated Gaussian fe...
06/04/2019 ∙ by Ji Xu, et al. ∙ 6 ∙ shareread it

Leveraging Just a Few Keywords for FineGrained Aspect Detection Through Weakly Supervised CoTraining
Usergenerated reviews can be decomposed into finegrained segments (e.g...
09/01/2019 ∙ by Giannis Karamanolakis, et al. ∙ 6 ∙ shareread it

Diameterbased Interactive Structure Search
In this work, we introduce interactive structure search, a generic frame...
06/05/2019 ∙ by Christopher Tosh, et al. ∙ 4 ∙ shareread it

Unbiased estimators for random design regression
In linear regression we wish to estimate the optimum linear least square...
07/08/2019 ∙ by Michał Dereziński, et al. ∙ 4 ∙ shareread it

Privacy Accounting and Quality Control in the Sage Differentially Private ML Platform
Companies increasingly expose machine learning (ML) models trained over ...
09/04/2019 ∙ by Mathias Lecuyer, et al. ∙ 3 ∙ shareread it

Benefits of overparameterization with EM
Expectation Maximization (EM) is among the most popular algorithms for m...
10/26/2018 ∙ by Ji Xu, et al. ∙ 2 ∙ shareread it

A cryptographic approach to black box adversarial machine learning
We propose an ensemble technique for converting any classifier into a co...
06/07/2019 ∙ by Kevin Shi, et al. ∙ 2 ∙ shareread it

A gradual, semidiscrete approach to generative network training via explicit wasserstein minimization
This paper provides a simple procedure to fit generative networks to tar...
06/08/2019 ∙ by Yucheng Chen, et al. ∙ 1 ∙ shareread it

Time Series Compression Based on Adaptive Piecewise Recurrent Autoencoder
Time series account for a large proportion of the data stored in financi...
07/23/2017 ∙ by Daniel Hsu, et al. ∙ 0 ∙ shareread it

Time Series Forecasting Based on Augmented Long ShortTerm Memory
In this paper, we use recurrent autoencoder model to predict the time se...
07/03/2017 ∙ by Daniel Hsu, et al. ∙ 0 ∙ shareread it

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 ...
08/24/2017 ∙ by Daniel Hsu, et al. ∙ 0 ∙ shareread it

Anomaly Detection on Graph Time Series
In this paper, we use variational recurrent neural network to investigat...
08/09/2017 ∙ by Daniel Hsu, et al. ∙ 0 ∙ shareread it

Parameter identification in Markov chain choice models
This work studies the parameter identification problem for the Markov ch...
06/02/2017 ∙ by Arushi Gupta, et al. ∙ 0 ∙ shareread it

Linear regression without correspondence
This article considers algorithmic and statistical aspects of linear reg...
05/19/2017 ∙ by Daniel Hsu, et al. ∙ 0 ∙ shareread it

Global analysis of Expectation Maximization for mixtures of two Gaussians
Expectation Maximization (EM) is among the most popular algorithms for e...
08/26/2016 ∙ by Ji Xu, et al. ∙ 0 ∙ shareread it

Search Improves Label for Active Learning
We investigate active learning with access to two distinct oracles: Labe...
02/23/2016 ∙ by Alina Beygelzimer, et al. ∙ 0 ∙ shareread it

Scalable Nonlinear Learning with Adaptive Polynomial Expansions
Can we effectively learn a nonlinear representation in time comparable t...
10/02/2014 ∙ by Alekh Agarwal, et al. ∙ 0 ∙ shareread it

Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits
We present a new algorithm for the contextual bandit learning problem, w...
02/04/2014 ∙ by Alekh Agarwal, et al. ∙ 0 ∙ shareread it

When are Overcomplete Topic Models Identifiable? Uniqueness of Tensor Tucker Decompositions with Structured Sparsity
Overcomplete latent representations have been very popular for unsupervi...
08/13/2013 ∙ by Daniel Hsu, et al. ∙ 0 ∙ shareread it

Loss minimization and parameter estimation with heavy tails
This work studies applications and generalizations of a simple estimatio...
07/07/2013 ∙ by Daniel Hsu, et al. ∙ 0 ∙ shareread it

A Tensor Approach to Learning Mixed Membership Community Models
Community detection is the task of detecting hidden communities from obs...
02/12/2013 ∙ by Anima Anandkumar, et al. ∙ 0 ∙ shareread it

Learning Sparse LowThreshold Linear Classifiers
We consider the problem of learning a nonnegative linear classifier wit...
12/13/2012 ∙ by Sivan Sabato, et al. ∙ 0 ∙ shareread it

Analysis of a randomized approximation scheme for matrix multiplication
This note gives a simple analysis of a randomized approximation scheme f...
11/23/2012 ∙ by Daniel Hsu, et al. ∙ 0 ∙ shareread it

Tensor decompositions for learning latent variable models
This work considers a computationally and statistically efficient parame...
10/29/2012 ∙ by Anima Anandkumar, et al. ∙ 0 ∙ shareread it

Learning Topic Models and Latent Bayesian Networks Under Expansion Constraints
Unsupervised estimation of latent variable models is a fundamental probl...
09/24/2012 ∙ by Daniel Hsu, et al. ∙ 0 ∙ shareread it

Convergence Rates for Differentially Private Statistical Estimation
Differential privacy is a cryptographicallymotivated definition of priv...
06/27/2012 ∙ by Kamalika Chaudhuri, et al. ∙ 0 ∙ shareread it

A concentration theorem for projections
X in R^D has mean zero and finite second moments. We show that there is ...
06/27/2012 ∙ by Sanjoy Dasgupta, et al. ∙ 0 ∙ shareread it

Learning mixtures of spherical Gaussians: moment methods and spectral decompositions
This work provides a computationally efficient and statistically consist...
06/25/2012 ∙ by Daniel Hsu, et al. ∙ 0 ∙ shareread it

A Spectral Algorithm for Latent Dirichlet Allocation
The problem of topic modeling can be seen as a generalization of the clu...
04/30/2012 ∙ by Dean P. Foster, et al. ∙ 0 ∙ shareread it

An Online Learningbased Framework for Tracking
We study the tracking problem, namely, estimating the hidden state of an...
03/15/2012 ∙ by Kamalika Chaudhuri, et al. ∙ 0 ∙ shareread it

A Method of Moments for Mixture Models and Hidden Markov Models
Mixture models are a fundamental tool in applied statistics and machine ...
03/03/2012 ∙ by Daniel Hsu, et al. ∙ 0 ∙ shareread it

Spectral Methods for Learning Multivariate Latent Tree Structure
This work considers the problem of learning the structure of multivariat...
07/07/2011 ∙ by Kamalika Chaudhuri, et al. ∙ 0 ∙ shareread it

Efficient Optimal Learning for Contextual Bandits
We address the problem of learning in an online setting where the learne...
06/13/2011 ∙ by Miroslav Dudík, et al. ∙ 0 ∙ shareread it

Random design analysis of ridge regression
This work gives a simultaneous analysis of both the ordinary least squar...
06/13/2011 ∙ by Daniel Hsu, et al. ∙ 0 ∙ shareread it

Dimensionfree tail inequalities for sums of random matrices
We derive exponential tail inequalities for sums of random matrices with...
04/09/2011 ∙ by Daniel Hsu, et al. ∙ 0 ∙ shareread it

Robust Matrix Decomposition with Outliers
Suppose a given observation matrix can be decomposed as the sum of a low...
11/05/2010 ∙ by Daniel Hsu, et al. ∙ 0 ∙ shareread it

Tracking using explanationbased modeling
We study the tracking problem, namely, estimating the hidden state of an...
03/16/2009 ∙ by Kamalika Chaudhuri, et al. ∙ 0 ∙ shareread it

Kernel Approximation Methods for Speech Recognition
We study largescale kernel methods for acoustic modeling in speech reco...
01/13/2017 ∙ by Avner May, et al. ∙ 0 ∙ shareread it

NonGaussian information from weak lensing data via deep learning
Weak lensing maps contain information beyond twopoint statistics on sma...
02/04/2018 ∙ by Arushi Gupta, et al. ∙ 0 ∙ shareread it

Tail bounds for volume sampled linear regression
The n × d design matrix in a linear regression problem is given, but the...
02/19/2018 ∙ by Michal Derezinski, et al. ∙ 0 ∙ shareread it

On the Connection between Differential Privacy and Adversarial Robustness in Machine Learning
Adversarial examples in machine learning has been a topic of intense res...
02/09/2018 ∙ by Mathias Lecuyer, et al. ∙ 0 ∙ shareread it

Successive RankOne Approximations for Nearly Orthogonally Decomposable Symmetric Tensors
Many idealized problems in signal processing, machine learning and stati...
05/29/2017 ∙ by Cun Mu, et al. ∙ 0 ∙ shareread it

Greedy Approaches to Symmetric Orthogonal Tensor Decomposition
Finding the symmetric and orthogonal decomposition (SOD) of a tensor is ...
06/05/2017 ∙ by Cun Mu, et al. ∙ 0 ∙ shareread it

Consistent Risk Estimation in HighDimensional Linear Regression
Risk estimation is at the core of many learning systems. The importance ...
02/05/2019 ∙ by Ji Xu, et al. ∙ 0 ∙ shareread it