Daniel Hsu

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Microsoft

Assistant professor at Columbia University

On the Approximation Power of Two-Layer Networks of Random ReLUs

This paper considers the following question: how well can depth-two ReLU...
02/03/2021 ∙ by Daniel Hsu, et al. ∙ 0 ∙ share

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Biased Programmers? Or Biased Data? A Field Experiment in Operationalizing AI Ethics

Why do biased predictions arise? What interventions can prevent them? We...
12/04/2020 ∙ by Bo Cowgill, et al. ∙ 0 ∙ share

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Detecting Foodborne Illness Complaints in Multiple Languages Using English Annotations Only

Health departments have been deploying text classification systems for t...
10/11/2020 ∙ by Ziyi Liu, et al. ∙ 2 ∙ share

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Cross-Lingual Text Classification with Minimal Resources by Transferring a Sparse Teacher

Cross-lingual text classification alleviates the need for manually label...
10/06/2020 ∙ by Giannis Karamanolakis, et al. ∙ 0 ∙ share

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On the proliferation of support vectors in high dimensions

The support vector machine (SVM) is a well-established classification me...
09/22/2020 ∙ by Daniel Hsu, et al. ∙ 32 ∙ share

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Contrastive learning, multi-view redundancy, and linear models

Self-supervised learning is an empirically successful approach to unsupe...
08/24/2020 ∙ by Christopher Tosh, et al. ∙ 5 ∙ share

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Statistical Query Lower Bounds for Tensor PCA

In the Tensor PCA problem introduced by Richard and Montanari (2014), on...
08/10/2020 ∙ by Rishabh Dudeja, et al. ∙ 13 ∙ share

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Ensuring Fairness Beyond the Training Data

We initiate the study of fair classifiers that are robust to perturbatio...
07/12/2020 ∙ by Debmalya Mandal, et al. ∙ 49 ∙ share

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Classification vs regression in overparameterized regimes: Does the loss function matter?

We compare classification and regression tasks in the overparameterized ...
05/16/2020 ∙ by Vidya Muthukumar, et al. ∙ 40 ∙ share

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Contrastive estimation reveals topic posterior information to linear models

Contrastive learning is an approach to representation learning that util...
03/04/2020 ∙ by Christopher Tosh, et al. ∙ 6 ∙ 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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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Daniel Hsu

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