Pranjal Awasthi

research

∙ 07/22/2023

The Sample Complexity of Multi-Distribution Learning for VC Classes

Multi-distribution learning is a natural generalization of PAC learning ...

0 Pranjal Awasthi, et al. ∙

research

∙ 05/10/2023

Best-Effort Adaptation

We study a problem of best-effort adaptation motivated by several applic...

0 Pranjal Awasthi, et al. ∙

research

∙ 01/23/2023

Congested Bandits: Optimal Routing via Short-term Resets

For traffic routing platforms, the choice of which route to recommend to...

0 Pranjal Awasthi, et al. ∙

research

∙ 10/19/2022

On the Adversarial Robustness of Mixture of Experts

Adversarial robustness is a key desirable property of neural networks. I...

0 Joan Puigcerver, et al. ∙

research

∙ 08/04/2022

Agnostic Learning of General ReLU Activation Using Gradient Descent

We provide a convergence analysis of gradient descent for the problem of...

0 Pranjal Awasthi, et al. ∙

research

∙ 07/07/2022

Individual Preference Stability for Clustering

In this paper, we propose a natural notion of individual preference (IP)...

6 Saba Ahmadi, et al. ∙

research

∙ 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...

0 Weihao Kong, et al. ∙

research

∙ 05/16/2022

ℋ-Consistency Estimation Error of Surrogate Loss Minimizers

We present a detailed study of estimation errors in terms of surrogate l...

5 Pranjal Awasthi, et al. ∙

research

∙ 05/03/2022

Do More Negative Samples Necessarily Hurt in Contrastive Learning?

Recent investigations in noise contrastive estimation suggest, both empi...

0 Pranjal Awasthi, et al. ∙

research

∙ 02/11/2022

Distributionally Robust Data Join

Suppose we are given two datasets: a labeled dataset and unlabeled datas...

0 Pranjal Awasthi, et al. ∙

research

∙ 01/31/2022

Agnostic Learnability of Halfspaces via Logistic Loss

We investigate approximation guarantees provided by logistic regression ...

0 Ziwei Ji, et al. ∙

research

∙ 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...

12 Pranjal Awasthi, et al. ∙

research

∙ 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 ...

0 Pranjal Awasthi, et al. ∙

research

∙ 06/18/2021

On the benefits of maximum likelihood estimation for Regression and Forecasting

We advocate for a practical Maximum Likelihood Estimation (MLE) approach...

0 Pranjal Awasthi, et al. ∙

research

∙ 06/12/2021

Semi-supervised Active Regression

Labelled data often comes at a high cost as it may require recruiting hu...

0 Fnu Devvrit, et al. ∙

research

∙ 06/06/2021

Neural Active Learning with Performance Guarantees

We investigate the problem of active learning in the streaming setting i...

0 Pranjal Awasthi, et al. ∙

research

∙ 05/04/2021

A Finer Calibration Analysis for Adversarial Robustness

We present a more general analysis of H-calibration for adversarially ro...

15 Pranjal Awasthi, et al. ∙

research

∙ 04/19/2021

Calibration and Consistency of Adversarial Surrogate Losses

Adversarial robustness is an increasingly critical property of classifie...

0 Pranjal Awasthi, et al. ∙

research

∙ 03/01/2021

A Multiclass Boosting Framework for Achieving Fast and Provable Adversarial Robustness

Alongside the well-publicized accomplishments of deep neural networks th...

0 Jacob Abernethy, et al. ∙

research

∙ 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...

16 Pranjal Awasthi, et al. ∙

research

∙ 12/01/2020

Adversarial Robustness Across Representation Spaces

Adversarial robustness corresponds to the susceptibility of deep neural ...

0 Pranjal Awasthi, et al. ∙

research

∙ 08/21/2020

Beyond Individual and Group Fairness

We present a new data-driven model of fairness that, unlike existing sta...

8 Pranjal Awasthi, et al. ∙

research

∙ 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...

0 Pranjal Awasthi, et al. ∙

research

∙ 06/11/2020

Adaptive Sampling to Reduce Disparate Performance

Existing methods for reducing disparate performance of a classifier acro...

1 Jacob Abernethy, et al. ∙

research

∙ 06/08/2020

A Notion of Individual Fairness for Clustering

A common distinction in fair machine learning, in particular in fair cla...

0 Matthäus Kleindessner, et al. ∙

research

∙ 05/31/2020

Estimating Principal Components under Adversarial Perturbations

Robustness is a key requirement for widespread deployment of machine lea...

0 Pranjal Awasthi, et al. ∙

research

∙ 04/28/2020

Adversarial Learning Guarantees for Linear Hypotheses and Neural Networks

Adversarial or test time robustness measures the susceptibility of a cla...

5 Pranjal Awasthi, et al. ∙

research

∙ 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...

11 Chicheng Zhang, et al. ∙

research

∙ 02/04/2020

A Deep Conditioning Treatment of Neural Networks

We study the role of depth in training randomly initialized overparamete...

0 Naman Agarwal, et al. ∙

research

∙ 11/29/2019

Adversarially Robust Low Dimensional Representations

Adversarial or test time robustness measures the susceptibility of a mac...

0 Pranjal Awasthi, et al. ∙

research

∙ 11/12/2019

On Robustness to Adversarial Examples and Polynomial Optimization

We study the design of computationally efficient algorithms with provabl...

16 Pranjal Awasthi, et al. ∙

research

∙ 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...

0 Pranjal Awasthi, et al. ∙

research

∙ 01/24/2019

Guarantees for Spectral Clustering with Fairness Constraints

Given the widespread popularity of spectral clustering (SC) for partitio...

0 Matthäus Kleindessner, et al. ∙

research

∙ 01/24/2019

Fair k-Center Clustering for Data Summarization

In data summarization we want to choose k prototypes in order to summari...

0 Matthäus Kleindessner, et al. ∙

research

∙ 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...

0 Haris Angelidakis, et al. ∙

research

∙ 04/23/2018

Towards Learning Sparsely Used Dictionaries with Arbitrary Supports

Dictionary learning is a popular approach for inferring a hidden basis o...

0 Pranjal Awasthi, et al. ∙

research

∙ 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...

0 Pranjal Awasthi, et al. ∙

research

∙ 11/23/2017

Clustering Semi-Random Mixtures of Gaussians

Gaussian mixture models (GMM) are the most widely used statistical model...

0 Pranjal Awasthi, et al. ∙

research

∙ 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 ...

0 Pranjal Awasthi, et al. ∙

research

∙ 07/31/2013

The Power of Localization for Efficiently Learning Linear Separators with Noise

We introduce a new approach for designing computationally efficient lear...

0 Pranjal Awasthi, et al. ∙

research

∙ 11/05/2012

Learning using Local Membership Queries

We introduce a new model of membership query (MQ) learning, where the le...

0 Pranjal Awasthi, et al. ∙

Pranjal Awasthi

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