Daniel Hsu

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Microsoft

Assistant professor at Columbia University

Correcting the bias in least squares regression with volume-rescaled sampling

Consider linear regression where the examples are generated by an unknow...
10/04/2018 ∙ by Michal Derezinski, et al. ∙ 38 ∙ share

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Weakly Supervised Attention Networks for Fine-Grained Opinion Mining and Public Health

In many review classification applications, a fine-grained analysis of t...
09/30/2019 ∙ by Giannis Karamanolakis, et al. ∙ 20 ∙ share

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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...
12/30/2019 ∙ by Daniel Hsu, et al. ∙ 13 ∙ share

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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-...
06/13/2018 ∙ by Mikhail Belkin, et al. ∙ 8 ∙ share

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Reconciling modern machine learning and the bias-variance trade-off

The question of generalization in machine learning---how algorithms are ...
12/28/2018 ∙ by Mikhail Belkin, et al. ∙ 6 ∙ share

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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 ∙ share

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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...
06/04/2019 ∙ by Ji Xu, et al. ∙ 6 ∙ share

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Leveraging Just a Few Keywords for Fine-Grained Aspect Detection Through Weakly Supervised Co-Training

User-generated reviews can be decomposed into fine-grained segments (e.g...
09/01/2019 ∙ by Giannis Karamanolakis, et al. ∙ 6 ∙ share

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Diameter-based Interactive Structure Search

In this work, we introduce interactive structure search, a generic frame...
06/05/2019 ∙ by Christopher Tosh, et al. ∙ 4 ∙ share

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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 ∙ share

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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 ...
09/04/2019 ∙ by Mathias Lecuyer, et al. ∙ 3 ∙ share

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Benefits of over-parameterization with EM

Expectation Maximization (EM) is among the most popular algorithms for m...
10/26/2018 ∙ by Ji Xu, et al. ∙ 2 ∙ share

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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...
06/07/2019 ∙ by Kevin Shi, et al. ∙ 2 ∙ share

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A gradual, semi-discrete 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 ∙ share

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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...
07/23/2017 ∙ by Daniel Hsu, et al. ∙ 0 ∙ share

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Time Series Forecasting Based on Augmented Long Short-Term Memory

In this paper, we use recurrent autoencoder model to predict the time se...
07/03/2017 ∙ by Daniel Hsu, et al. ∙ 0 ∙ share

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

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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 ∙ share

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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 ∙ share

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Linear regression without correspondence

This article considers algorithmic and statistical aspects of linear reg...
05/19/2017 ∙ by Daniel Hsu, et al. ∙ 0 ∙ share

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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...
08/26/2016 ∙ by Ji Xu, et al. ∙ 0 ∙ share

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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 ∙ share

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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 ∙ share

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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...
02/04/2014 ∙ by Alekh Agarwal, et al. ∙ 0 ∙ share

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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...
08/13/2013 ∙ by Daniel Hsu, et al. ∙ 0 ∙ share

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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 ∙ share

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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 ∙ share

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Learning Sparse Low-Threshold Linear Classifiers

We consider the problem of learning a non-negative linear classifier wit...
12/13/2012 ∙ by Sivan Sabato, et al. ∙ 0 ∙ share

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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...
11/23/2012 ∙ by Daniel Hsu, et al. ∙ 0 ∙ share

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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 ∙ share

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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 ∙ share

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Convergence Rates for Differentially Private Statistical Estimation

Differential privacy is a cryptographically-motivated definition of priv...
06/27/2012 ∙ by Kamalika Chaudhuri, et al. ∙ 0 ∙ share

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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 ...
06/27/2012 ∙ by Sanjoy Dasgupta, et al. ∙ 0 ∙ share

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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 ∙ share

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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...
04/30/2012 ∙ by Dean P. Foster, et al. ∙ 0 ∙ share

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An Online Learning-based Framework for Tracking

We study the tracking problem, namely, estimating the hidden state of an...
03/15/2012 ∙ by Kamalika Chaudhuri, et al. ∙ 0 ∙ share

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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 ...
03/03/2012 ∙ by Daniel Hsu, et al. ∙ 0 ∙ share

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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 ∙ share

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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 ∙ share

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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 ∙ share

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Dimension-free 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 ∙ share

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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 ∙ share

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Tracking using explanation-based modeling

We study the tracking problem, namely, estimating the hidden state of an...
03/16/2009 ∙ by Kamalika Chaudhuri, et al. ∙ 0 ∙ share

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Kernel Approximation Methods for Speech Recognition

We study large-scale kernel methods for acoustic modeling in speech reco...
01/13/2017 ∙ by Avner May, et al. ∙ 0 ∙ share

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Non-Gaussian information from weak lensing data via deep learning

Weak lensing maps contain information beyond two-point statistics on sma...
02/04/2018 ∙ by Arushi Gupta, et al. ∙ 0 ∙ share

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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...
02/19/2018 ∙ by Michal Derezinski, et al. ∙ 0 ∙ share

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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...
02/09/2018 ∙ by Mathias Lecuyer, et al. ∙ 0 ∙ share

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Successive Rank-One 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 ∙ share

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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 ∙ share

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Consistent Risk Estimation in High-Dimensional Linear Regression

Risk estimation is at the core of many learning systems. The importance ...
02/05/2019 ∙ by Ji Xu, et al. ∙ 0 ∙ share

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Daniel Hsu

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