Pranjal Awasthi

Efficient Algorithms for Learning Depth-2 Neural Networks with General ReLU Activations

We present polynomial time and sample efficient algorithms for learning ...
07/21/2021 ∙ by Pranjal Awasthi, et al. ∙ 0 ∙ share

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On the benefits of maximum likelihood estimation for Regression and Forecasting

We advocate for a practical Maximum Likelihood Estimation (MLE) approach...
06/18/2021 ∙ by Pranjal Awasthi, et al. ∙ 0 ∙ share

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Semi-supervised Active Regression

Labelled data often comes at a high cost as it may require recruiting hu...
06/12/2021 ∙ by Fnu Devvrit, et al. ∙ 0 ∙ share

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Neural Active Learning with Performance Guarantees

We investigate the problem of active learning in the streaming setting i...
06/06/2021 ∙ by Pranjal Awasthi, et al. ∙ 0 ∙ share

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A Finer Calibration Analysis for Adversarial Robustness

We present a more general analysis of H-calibration for adversarially ro...
05/04/2021 ∙ by Pranjal Awasthi, et al. ∙ 15 ∙ share

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Calibration and Consistency of Adversarial Surrogate Losses

Adversarial robustness is an increasingly critical property of classifie...
04/19/2021 ∙ by Pranjal Awasthi, et al. ∙ 0 ∙ share

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A Multiclass Boosting Framework for Achieving Fast and Provable Adversarial Robustness

Alongside the well-publicized accomplishments of deep neural networks th...
03/01/2021 ∙ by Jacob Abernethy, et al. ∙ 0 ∙ share

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Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information

Training and evaluation of fair classifiers is a challenging problem. Th...
02/16/2021 ∙ by Pranjal Awasthi, et al. ∙ 16 ∙ share

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Adversarial Robustness Across Representation Spaces

Adversarial robustness corresponds to the susceptibility of deep neural ...
12/01/2020 ∙ by Pranjal Awasthi, et al. ∙ 0 ∙ share

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Beyond Individual and Group Fairness

We present a new data-driven model of fairness that, unlike existing sta...
08/21/2020 ∙ by Pranjal Awasthi, et al. ∙ 8 ∙ share

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On the Rademacher Complexity of Linear Hypothesis Sets

Linear predictors form a rich class of hypotheses used in a variety of l...
07/21/2020 ∙ by Pranjal Awasthi, et al. ∙ 0 ∙ share

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Adaptive Sampling to Reduce Disparate Performance

Existing methods for reducing disparate performance of a classifier acro...
06/11/2020 ∙ by Jacob Abernethy, et al. ∙ 1 ∙ share

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A Notion of Individual Fairness for Clustering

A common distinction in fair machine learning, in particular in fair cla...
06/08/2020 ∙ by Matthäus Kleindessner, et al. ∙ 0 ∙ share

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Estimating Principal Components under Adversarial Perturbations

Robustness is a key requirement for widespread deployment of machine lea...
05/31/2020 ∙ by Pranjal Awasthi, et al. ∙ 0 ∙ share

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Adversarial Learning Guarantees for Linear Hypotheses and Neural Networks

Adversarial or test time robustness measures the susceptibility of a cla...
04/28/2020 ∙ by Pranjal Awasthi, et al. ∙ 5 ∙ share

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Efficient active learning of sparse halfspaces with arbitrary bounded noise

In this work we study active learning of homogeneous s-sparse halfspaces...
02/12/2020 ∙ by Chicheng Zhang, et al. ∙ 11 ∙ share

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A Deep Conditioning Treatment of Neural Networks

We study the role of depth in training randomly initialized overparamete...
02/04/2020 ∙ by Naman Agarwal, et al. ∙ 0 ∙ share

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Adversarially Robust Low Dimensional Representations

Adversarial or test time robustness measures the susceptibility of a mac...
11/29/2019 ∙ by Pranjal Awasthi, et al. ∙ 0 ∙ share

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On Robustness to Adversarial Examples and Polynomial Optimization

We study the design of computationally efficient algorithms with provabl...
11/12/2019 ∙ by Pranjal Awasthi, et al. ∙ 16 ∙ share

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Effectiveness of Equalized Odds for Fair Classification under Imperfect Group Information

Most approaches for ensuring or improving a model's fairness with respec...
06/07/2019 ∙ by Pranjal Awasthi, et al. ∙ 0 ∙ share

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Guarantees for Spectral Clustering with Fairness Constraints

Given the widespread popularity of spectral clustering (SC) for partitio...
01/24/2019 ∙ by Matthäus Kleindessner, et al. ∙ 0 ∙ share

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Fair k-Center Clustering for Data Summarization

In data summarization we want to choose k prototypes in order to summari...
01/24/2019 ∙ by Matthäus Kleindessner, et al. ∙ 0 ∙ share

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Bilu-Linial stability, certified algorithms and the Independent Set problem

We study the notion of Bilu-Linial stability in the context of Independe...
10/19/2018 ∙ by Haris Angelidakis, et al. ∙ 0 ∙ share

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Towards Learning Sparsely Used Dictionaries with Arbitrary Supports

Dictionary learning is a popular approach for inferring a hidden basis o...
04/23/2018 ∙ by Pranjal Awasthi, et al. ∙ 0 ∙ share

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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...
02/05/2018 ∙ by Pranjal Awasthi, et al. ∙ 0 ∙ share

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Clustering Semi-Random Mixtures of Gaussians

Gaussian mixture models (GMM) are the most widely used statistical model...
11/23/2017 ∙ by Pranjal Awasthi, et al. ∙ 0 ∙ share

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On some provably correct cases of variational inference for topic models

Variational inference is a very efficient and popular heuristic used in ...
03/23/2015 ∙ by Pranjal Awasthi, et al. ∙ 0 ∙ share

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The Power of Localization for Efficiently Learning Linear Separators with Noise

We introduce a new approach for designing computationally efficient lear...
07/31/2013 ∙ by Pranjal Awasthi, et al. ∙ 0 ∙ share

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Learning using Local Membership Queries

We introduce a new model of membership query (MQ) learning, where the le...
11/05/2012 ∙ by Pranjal Awasthi, et al. ∙ 0 ∙ share

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Pranjal Awasthi

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