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07/22/2023
The Sample Complexity of Multi-Distribution Learning for VC Classes
Multi-distribution learning is a natural generalization of PAC learning ...
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05/10/2023
Best-Effort Adaptation
We study a problem of best-effort adaptation motivated by several applic...
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01/23/2023
Congested Bandits: Optimal Routing via Short-term Resets
For traffic routing platforms, the choice of which route to recommend to...
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10/19/2022
On the Adversarial Robustness of Mixture of Experts
Adversarial robustness is a key desirable property of neural networks. I...
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08/04/2022
Agnostic Learning of General ReLU Activation Using Gradient Descent
We provide a convergence analysis of gradient descent for the problem of...
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07/07/2022
Individual Preference Stability for Clustering
In this paper, we propose a natural notion of individual preference (IP)...
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06/09/2022
Trimmed Maximum Likelihood Estimation for Robust Learning in Generalized Linear Models
We study the problem of learning generalized linear models under adversa...
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05/16/2022
â„‹-Consistency Estimation Error of Surrogate Loss Minimizers
We present a detailed study of estimation errors in terms of surrogate l...
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05/03/2022
Do More Negative Samples Necessarily Hurt in Contrastive Learning?
Recent investigations in noise contrastive estimation suggest, both empi...
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02/11/2022
Distributionally Robust Data Join
Suppose we are given two datasets: a labeled dataset and unlabeled datas...
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01/31/2022
Agnostic Learnability of Halfspaces via Logistic Loss
We investigate approximation guarantees provided by logistic regression ...
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12/03/2021
On the Existence of the Adversarial Bayes Classifier (Extended Version)
Adversarial robustness is a critical property in a variety of modern mac...
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07/21/2021
Efficient Algorithms for Learning Depth-2 Neural Networks with General ReLU Activations
We present polynomial time and sample efficient algorithms for learning ...
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06/18/2021
On the benefits of maximum likelihood estimation for Regression and Forecasting
We advocate for a practical Maximum Likelihood Estimation (MLE) approach...
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06/12/2021
Semi-supervised Active Regression
Labelled data often comes at a high cost as it may require recruiting hu...
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06/06/2021
Neural Active Learning with Performance Guarantees
We investigate the problem of active learning in the streaming setting i...
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05/04/2021
A Finer Calibration Analysis for Adversarial Robustness
We present a more general analysis of H-calibration for adversarially ro...
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04/19/2021
Calibration and Consistency of Adversarial Surrogate Losses
Adversarial robustness is an increasingly critical property of classifie...
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03/01/2021
A Multiclass Boosting Framework for Achieving Fast and Provable Adversarial Robustness
Alongside the well-publicized accomplishments of deep neural networks th...
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02/16/2021
Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information
Training and evaluation of fair classifiers is a challenging problem. Th...
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12/01/2020
Adversarial Robustness Across Representation Spaces
Adversarial robustness corresponds to the susceptibility of deep neural ...
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08/21/2020
Beyond Individual and Group Fairness
We present a new data-driven model of fairness that, unlike existing sta...
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07/21/2020
On the Rademacher Complexity of Linear Hypothesis Sets
Linear predictors form a rich class of hypotheses used in a variety of l...
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06/11/2020
Adaptive Sampling to Reduce Disparate Performance
Existing methods for reducing disparate performance of a classifier acro...
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06/08/2020
A Notion of Individual Fairness for Clustering
A common distinction in fair machine learning, in particular in fair cla...
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05/31/2020
Estimating Principal Components under Adversarial Perturbations
Robustness is a key requirement for widespread deployment of machine lea...
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04/28/2020
Adversarial Learning Guarantees for Linear Hypotheses and Neural Networks
Adversarial or test time robustness measures the susceptibility of a cla...
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02/12/2020
Efficient active learning of sparse halfspaces with arbitrary bounded noise
In this work we study active learning of homogeneous s-sparse halfspaces...
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02/04/2020
A Deep Conditioning Treatment of Neural Networks
We study the role of depth in training randomly initialized overparamete...
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11/29/2019
Adversarially Robust Low Dimensional Representations
Adversarial or test time robustness measures the susceptibility of a mac...
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11/12/2019
On Robustness to Adversarial Examples and Polynomial Optimization
We study the design of computationally efficient algorithms with provabl...
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06/07/2019
Effectiveness of Equalized Odds for Fair Classification under Imperfect Group Information
Most approaches for ensuring or improving a model's fairness with respec...
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01/24/2019
Guarantees for Spectral Clustering with Fairness Constraints
Given the widespread popularity of spectral clustering (SC) for partitio...
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01/24/2019
Fair k-Center Clustering for Data Summarization
In data summarization we want to choose k prototypes in order to summari...
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10/19/2018
Bilu-Linial stability, certified algorithms and the Independent Set problem
We study the notion of Bilu-Linial stability in the context of Independe...
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04/23/2018
Towards Learning Sparsely Used Dictionaries with Arbitrary Supports
Dictionary learning is a popular approach for inferring a hidden basis o...
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02/05/2018
Robust Vertex Enumeration for Convex Hulls in High Dimensions
Computation of the vertices of the convex hull of a set S of n points in...
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11/23/2017
Clustering Semi-Random Mixtures of Gaussians
Gaussian mixture models (GMM) are the most widely used statistical model...
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03/23/2015
On some provably correct cases of variational inference for topic models
Variational inference is a very efficient and popular heuristic used in ...
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07/31/2013
The Power of Localization for Efficiently Learning Linear Separators with Noise
We introduce a new approach for designing computationally efficient lear...
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11/05/2012
