b'Daniel Hsu'

research

∙ 07/09/2023

On the sample complexity of estimation in logistic regression

The logistic regression model is one of the most popular data generation...

0 Daniel Hsu, et al. ∙

research

∙ 06/05/2023

Representational Strengths and Limitations of Transformers

Attention layers, as commonly used in transformers, form the backbone of...

0 Clayton Sanford, et al. ∙

research

∙ 03/07/2023

Group conditional validity via multi-group learning

We consider the problem of distribution-free conformal prediction and th...

0 Samuel Deng, et al. ∙

research

∙ 06/10/2022

Intrinsic dimensionality and generalization properties of the ℛ-norm inductive bias

We study the structural and statistical properties of ℛ-norm minimizing ...

0 Clayton Sanford, et al. ∙

research

∙ 04/15/2022

Statistical-Computational Trade-offs in Tensor PCA and Related Problems via Communication Complexity

Tensor PCA is a stylized statistical inference problem introduced by Mon...

0 Rishabh Dudeja, et al. ∙

research

∙ 02/18/2022

Masked prediction tasks: a parameter identifiability view

The vast majority of work in self-supervised learning, both theoretical ...

0 Bingbin Liu, et al. ∙

research

∙ 02/10/2022

Near-Optimal Statistical Query Lower Bounds for Agnostically Learning Intersections of Halfspaces with Gaussian Marginals

We consider the well-studied problem of learning intersections of halfsp...

0 Daniel Hsu, et al. ∙

research

∙ 12/22/2021

Simple and near-optimal algorithms for hidden stratification and multi-group learning

Multi-group agnostic learning is a formal learning criterion that is con...

0 Christopher Tosh, et al. ∙

research

∙ 07/03/2021

Bayesian decision-making under misspecified priors with applications to meta-learning

Thompson sampling and other Bayesian sequential decision-making algorith...

13 Max Simchowitz, et al. ∙

research

∙ 05/28/2021

Support vector machines and linear regression coincide with very high-dimensional features

The support vector machine (SVM) and minimum Euclidean norm least square...

0 Navid Ardeshir, et al. ∙

research

∙ 05/27/2021

The piranha problem: Large effects swimming in a small pond

In some scientific fields, it is common to have certain variables of int...

0 Christopher Tosh, et al. ∙

research

∙ 04/12/2021

Generalization bounds via distillation

This paper theoretically investigates the following empirical phenomenon...

15 Daniel Hsu, et al. ∙

research

∙ 02/03/2021

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

This paper considers the following question: how well can depth-two ReLU...

0 Daniel Hsu, et al. ∙

research

∙ 12/04/2020

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

Why do biased predictions arise? What interventions can prevent them? We...

0 Bo Cowgill, et al. ∙

research

∙ 10/11/2020

Detecting Foodborne Illness Complaints in Multiple Languages Using English Annotations Only

Health departments have been deploying text classification systems for t...

2 Ziyi Liu, et al. ∙

research

∙ 10/06/2020

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

Cross-lingual text classification alleviates the need for manually label...

0 Giannis Karamanolakis, et al. ∙

research

∙ 09/22/2020

On the proliferation of support vectors in high dimensions

The support vector machine (SVM) is a well-established classification me...

32 Daniel Hsu, et al. ∙

research

∙ 08/24/2020

Contrastive learning, multi-view redundancy, and linear models

Self-supervised learning is an empirically successful approach to unsupe...

5 Christopher Tosh, et al. ∙

research

∙ 08/10/2020

Statistical Query Lower Bounds for Tensor PCA

In the Tensor PCA problem introduced by Richard and Montanari (2014), on...

13 Rishabh Dudeja, et al. ∙

research

∙ 07/12/2020

Ensuring Fairness Beyond the Training Data

We initiate the study of fair classifiers that are robust to perturbatio...

49 Debmalya Mandal, et al. ∙

research

∙ 05/16/2020

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

We compare classification and regression tasks in the overparameterized ...

40 Vidya Muthukumar, et al. ∙

research

∙ 03/04/2020

Contrastive estimation reveals topic posterior information to linear models

Contrastive learning is an approach to representation learning that util...

6 Christopher Tosh, et al. ∙

research

∙ 12/30/2019

A New Framework for Query Efficient Active Imitation Learning

We seek to align agent policy with human expert behavior in a reinforcem...

13 Daniel Hsu, et al. ∙

research

∙ 09/30/2019

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

In many review classification applications, a fine-grained analysis of t...

20 Giannis Karamanolakis, et al. ∙

research

∙ 09/04/2019

Privacy Accounting and Quality Control in the Sage Differentially Private ML Platform

Companies increasingly expose machine learning (ML) models trained over ...

3 Mathias Lecuyer, et al. ∙

research

∙ 09/01/2019

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...

6 Giannis Karamanolakis, et al. ∙

research

∙ 07/08/2019

Unbiased estimators for random design regression

In linear regression we wish to estimate the optimum linear least square...

4 Michał Dereziński, et al. ∙

research

∙ 06/08/2019

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...

1 Yucheng Chen, et al. ∙

research

∙ 06/07/2019

A cryptographic approach to black box adversarial machine learning

We propose an ensemble technique for converting any classifier into a co...

2 Kevin Shi, et al. ∙

research

∙ 06/05/2019

Diameter-based Interactive Structure Search

In this work, we introduce interactive structure search, a generic frame...

4 Christopher Tosh, et al. ∙

research

∙ 06/04/2019

How many variables should be entered in a principal component regression equation?

We study least squares linear regression over N uncorrelated Gaussian fe...

6 Ji Xu, et al. ∙

research

∙ 03/18/2019

Two models of double descent for weak features

The "double descent" risk curve was recently proposed to qualitatively d...

6 Mikhail Belkin, et al. ∙

research

∙ 02/05/2019

Consistent Risk Estimation in High-Dimensional Linear Regression

Risk estimation is at the core of many learning systems. The importance ...

0 Ji Xu, et al. ∙

research

∙ 12/28/2018

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

The question of generalization in machine learning---how algorithms are ...

6 Mikhail Belkin, et al. ∙

research

∙ 10/26/2018

Benefits of over-parameterization with EM

Expectation Maximization (EM) is among the most popular algorithms for m...

2 Ji Xu, et al. ∙

research

∙ 10/04/2018

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

Consider linear regression where the examples are generated by an unknow...

38 Michal Derezinski, et al. ∙

research

∙ 06/13/2018

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-...

8 Mikhail Belkin, et al. ∙

research

∙ 02/19/2018

Tail bounds for volume sampled linear regression

The n × d design matrix in a linear regression problem is given, but the...

0 Michal Derezinski, et al. ∙

research

∙ 02/09/2018

On the Connection between Differential Privacy and Adversarial Robustness in Machine Learning

Adversarial examples in machine learning has been a topic of intense res...

0 Mathias Lecuyer, et al. ∙

research

∙ 02/04/2018

Non-Gaussian information from weak lensing data via deep learning

Weak lensing maps contain information beyond two-point statistics on sma...

0 Arushi Gupta, et al. ∙

research

∙ 08/24/2017

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 ...

0 Daniel Hsu, et al. ∙

research

∙ 08/09/2017

Anomaly Detection on Graph Time Series

In this paper, we use variational recurrent neural network to investigat...

0 Daniel Hsu, et al. ∙

research

∙ 07/23/2017

Time Series Compression Based on Adaptive Piecewise Recurrent Autoencoder

Time series account for a large proportion of the data stored in financi...

0 Daniel Hsu, et al. ∙

research

∙ 07/03/2017

Time Series Forecasting Based on Augmented Long Short-Term Memory

In this paper, we use recurrent autoencoder model to predict the time se...

0 Daniel Hsu, et al. ∙

research

∙ 06/05/2017

Greedy Approaches to Symmetric Orthogonal Tensor Decomposition

Finding the symmetric and orthogonal decomposition (SOD) of a tensor is ...

0 Cun Mu, et al. ∙

research

∙ 06/02/2017

Parameter identification in Markov chain choice models

This work studies the parameter identification problem for the Markov ch...

0 Arushi Gupta, et al. ∙

research

∙ 05/29/2017

Successive Rank-One Approximations for Nearly Orthogonally Decomposable Symmetric Tensors

Many idealized problems in signal processing, machine learning and stati...

0 Cun Mu, et al. ∙

research

∙ 05/19/2017

Linear regression without correspondence

This article considers algorithmic and statistical aspects of linear reg...

0 Daniel Hsu, et al. ∙

research

∙ 01/13/2017

Kernel Approximation Methods for Speech Recognition

We study large-scale kernel methods for acoustic modeling in speech reco...

0 Avner May, et al. ∙

research

∙ 08/26/2016

Global analysis of Expectation Maximization for mixtures of two Gaussians

Expectation Maximization (EM) is among the most popular algorithms for e...

0 Ji Xu, et al. ∙

Daniel Hsu

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