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07/09/2023
On the sample complexity of estimation in logistic regression
The logistic regression model is one of the most popular data generation...
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06/05/2023
Representational Strengths and Limitations of Transformers
Attention layers, as commonly used in transformers, form the backbone of...
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03/07/2023
Group conditional validity via multi-group learning
We consider the problem of distribution-free conformal prediction and th...
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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 ...
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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...
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02/18/2022
Masked prediction tasks: a parameter identifiability view
The vast majority of work in self-supervised learning, both theoretical ...
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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...
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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...
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07/03/2021
Bayesian decision-making under misspecified priors with applications to meta-learning
Thompson sampling and other Bayesian sequential decision-making algorith...
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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...
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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...
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04/12/2021
Generalization bounds via distillation
This paper theoretically investigates the following empirical phenomenon...
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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...
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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...
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10/11/2020
Detecting Foodborne Illness Complaints in Multiple Languages Using English Annotations Only
Health departments have been deploying text classification systems for t...
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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...
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09/22/2020
On the proliferation of support vectors in high dimensions
The support vector machine (SVM) is a well-established classification me...
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08/24/2020
Contrastive learning, multi-view redundancy, and linear models
Self-supervised learning is an empirically successful approach to unsupe...
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08/10/2020
Statistical Query Lower Bounds for Tensor PCA
In the Tensor PCA problem introduced by Richard and Montanari (2014), on...
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07/12/2020
Ensuring Fairness Beyond the Training Data
We initiate the study of fair classifiers that are robust to perturbatio...
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05/16/2020
Classification vs regression in overparameterized regimes: Does the loss function matter?
We compare classification and regression tasks in the overparameterized ...
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03/04/2020
Contrastive estimation reveals topic posterior information to linear models
Contrastive learning is an approach to representation learning that util...
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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...
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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...
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09/04/2019
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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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...
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07/08/2019
Unbiased estimators for random design regression
In linear regression we wish to estimate the optimum linear least square...
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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...
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06/07/2019
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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06/05/2019
Diameter-based Interactive Structure Search
In this work, we introduce interactive structure search, a generic frame...
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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...
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03/18/2019
Two models of double descent for weak features
The "double descent" risk curve was recently proposed to qualitatively d...
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02/05/2019
Consistent Risk Estimation in High-Dimensional Linear Regression
Risk estimation is at the core of many learning systems. The importance ...
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12/28/2018
Reconciling modern machine learning and the bias-variance trade-off
The question of generalization in machine learning---how algorithms are ...
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10/26/2018
Benefits of over-parameterization with EM
Expectation Maximization (EM) is among the most popular algorithms for m...
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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...
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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-...
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02/19/2018
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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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...
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02/04/2018
Non-Gaussian information from weak lensing data via deep learning
Weak lensing maps contain information beyond two-point statistics on sma...
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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 ...
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08/09/2017
Anomaly Detection on Graph Time Series
In this paper, we use variational recurrent neural network to investigat...
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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...
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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...
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06/05/2017
Greedy Approaches to Symmetric Orthogonal Tensor Decomposition
Finding the symmetric and orthogonal decomposition (SOD) of a tensor is ...
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06/02/2017
Parameter identification in Markov chain choice models
This work studies the parameter identification problem for the Markov ch...
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05/29/2017
Successive Rank-One Approximations for Nearly Orthogonally Decomposable Symmetric Tensors
Many idealized problems in signal processing, machine learning and stati...
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05/19/2017
Linear regression without correspondence
This article considers algorithmic and statistical aspects of linear reg...
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01/13/2017
Kernel Approximation Methods for Speech Recognition
We study large-scale kernel methods for acoustic modeling in speech reco...
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08/26/2016
