Pranjal Awasthi

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PAC-Bayes Learning Bounds for Sample-Dependent Priors

Pranjal Awasthi, Satyen Kale, Mehryar Mohri, 2020, NeurIPS.

A Deep Conditioning Treatment of Neural Networks pdf

Pranjal Awasthi, Naman Agarwal, Satyen Kale, 2020, ALT.

The Power of Localization for Efficiently Learning Linear Separators with Noise

Maria-Florina Balcan, Philip M. Long, Pranjal Awasthi, 2013, J. ACM.

Improved Spectral-Norm Bounds for Clustering

Pranjal Awasthi, Or Sheffet, Pranjal Awasthi, 2012, APPROX-RANDOM.

Equalized odds postprocessing under imperfect group information

Pranjal Awasthi, Jamie Morgenstern, Matthaus Kleindessner, 2019, AISTATS.

Based Clustering : A Foundational Perspective

Maria-Florina Balcan, Pranjal Awasthi, 2014 .

Adversarial robustness via robust low rank representations

A. Rawat, Aravindan Vijayaraghavan, Pranjal Awasthi, 2020, NeurIPS.

Efficient PAC Learning from the Crowd Pranjal Awasthi

Pranjal Awasthi, CS. RUTGERS. Edu, 2017 .

On the Existence of the Adversarial Bayes Classifier (Extended Version) pdf

Mehryar Mohri, Pranjal Awasthi, Natalie S. Frank, 2021, NeurIPS.

On the Adaptivity Gap in Two-Stage Robust Linear Optimization under Uncertain Constraints

Pranjal Awasthi, Vineet Goyal, Brian Y. Lu, 2015 .

Testing Lipschitz Functions on Hypergrid Domains

Sofya Raskhodnikova, Marco Molinaro, Pranjal Awasthi, 2012, Algorithmica.

Do More Negative Samples Necessarily Hurt in Contrastive Learning?

Pranjal Awasthi, Nishanth Dikkala, Pritish Kamath, 2022, ICML.

The power of localization for efficiently learning linear separators with noise

Philip M. Long, Maria-Florina Balcan, Pranjal Awasthi, 2014, STOC.

Individual Preference Stability for Clustering

S. Khuller, Jamie H. Morgenstern, Pranjal Awasthi, 2022, ICML.

Congested Bandits: Optimal Routing via Short-term Resets

Pranjal Awasthi, K. Bhatia, Sreenivas Gollapudi, 2023, ICML.

Understanding Simultaneous Train and Test Robustness

Sivaraman Balakrishnan, Aravindan Vijayaraghavan, Pranjal Awasthi, 2022, ALT.

Trimmed Maximum Likelihood Estimation for Robust Learning in Generalized Linear Models

Rajat Sen, Pranjal Awasthi, Weihao Kong, 2022, ArXiv.

Harmonizing strategic advantage with social good through Data Philanthropy

Jordana J. George, Pranjal Awasthi, 2019 .

Beyond GNNs: An Efficient Architecture for Graph Problems

Pranjal Awasthi, Sreenivas Gollapudi, Abhimanyu Das, 2022, AAAI.

Limitations of Local Filters of Lipschitz and Monotone Functions

Sofya Raskhodnikova, Marco Molinaro, Pranjal Awasthi, 2015, TOCT.

Active Sampling for Min-Max Fairness

Jacob D. Abernethy, J. Abernethy, Jamie H. Morgenstern, 2020, ICML.

Trimmed Maximum Likelihood Estimation for Robust Learning in Generalized Linear Models

Rajat Sen, Pranjal Awasthi, Weihao Kong, 2022, ArXiv.

On the Adversarial Robustness of Mixture of Experts

Srinadh Bhojanapalli, Rodolphe Jenatton, Pranjal Awasthi, 2022, NeurIPS.

Multi-Class H -Consistency Bounds

M. Mohri, Pranjal Awasthi, Anqi Mao, 2022 .

Robust vertex enumeration for convex hulls in high dimensions

Pranjal Awasthi, B. Kalantari, Yikai Zhang, 2020, Annals of Operations Research.

On the adaptivity gap in two-stage robust linear optimization under uncertain packing constraints

Pranjal Awasthi, Vineet Goyal, Brian Y. Lu, 2018, Mathematical Programming.

Agnostic Learning of General ReLU Activation Using Gradient Descent

Aravindan Vijayaraghavan, Pranjal Awasthi, Alex K. Tang, 2022, ICLR.

Testing Lipschitz Functions on Hypergrid Domains

Pranjal Awasthi, Sofya Raskhodnikova, M. Molinaro, 2015, Algorithmica.

Towards Learning Sparsely Used Dictionaries with Arbitrary Supports

Aravindan Vijayaraghavan, Pranjal Awasthi, Aravindan Vijayaraghavan, 2018, 2018 IEEE 59th Annual Symposium on Foundations of Computer Science (FOCS).

A Notion of Individual Fairness for Clustering pdf

Pranjal Awasthi, Jamie Morgenstern, Matthaus Kleindessner, 2020, ArXiv.

Fair k-Center Clustering for Data Summarization

Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern, 2019, ICML.

Robust Communication-Optimal Distributed Clustering Algorithms

David P. Woodruff, Maria-Florina Balcan, Ainesh Bakshi, 2017, ICALP.

General and Robust Communication-Efficient Algorithms for Distributed Clustering pdf

Maria-Florina Balcan, Pranjal Awasthi, Colin White, 2017, ArXiv.

Guarantees for Spectral Clustering with Fairness Constraints

Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern, 2019, ICML.

On the Adaptivity Gap in Two-Stage Robust Linear Optimization under Uncertain Constraints

Pranjal Awasthi, Vineet Goyal, Brian Y. Lu, 2015 .

Distributionally Robust Data Join

Jamie H. Morgenstern, Pranjal Awasthi, Christopher Jung, 2022, ArXiv.

Semi-supervised Active Regression pdf

Pranjal Awasthi, Nived Rajaraman, Fnu Devvrit, 2021, ArXiv.

Testing Lipschitz Functions on Hypergrid Domains

Sofya Raskhodnikova, Marco Molinaro, Pranjal Awasthi, 2012, Algorithmica.

Adversarially Robust Low Dimensional Representations pdf

Aravindan Vijayaraghavan, Pranjal Awasthi, Vaggos Chatziafratis, 2019, COLT.

Estimating Principal Components under Adversarial Perturbations

Xue Chen, Aravindan Vijayaraghavan, Pranjal Awasthi, 2020, COLT.

Local algorithms for interactive clustering

Maria-Florina Balcan, Pranjal Awasthi, Konstantin Voevodski, 2013, ICML.

Adversarial Learning Guarantees for Linear Hypotheses and Neural Networks

Pranjal Awasthi, Mehryar Mohri, Natalie Frank, 2020, ICML.

On Nash-Equilibria of Approximation-Stable Games

Santosh S. Vempala, Maria-Florina Balcan, Avrim Blum, 2010, SAGT.

On the benefits of maximum likelihood estimation for Regression and Forecasting pdf

Abhimanyu Das, Pranjal Awasthi, Ananda Theertha Suresh, 2021, ICLR.

On some provably correct cases of variational inference for topic models

Andrej Risteski, Pranjal Awasthi, Pranjal Awasthi, 2015, NIPS.

Adaptive Sampling to Reduce Disparate Performance pdf

Jacob Abernethy, Jie Zhang, Pranjal Awasthi, 2020, ArXiv.

On the adaptivity gap in two-stage robust linear optimization under uncertain packing constraints

Pranjal Awasthi, Vineet Goyal, Brian Y. Lu, 2019, Math. Program..

Relax, No Need to Round: Integrality of Clustering Formulations

Ravishankar Krishnaswamy, Rachel Ward, Moses Charikar, 2014, ITCS.

Does Engaging in Data Philanthropy Impact Business Value?

Jordana J. George, Pranjal Awasthi, D. Leidner, 2022, Inf. Syst. Manag..

Beyond Individual and Group Fairness pdf

Yishay Mansour, Corinna Cortes, Pranjal Awasthi, 2020, ArXiv.

Agnostic Learnability of Halfspaces via Logistic Loss

Satyen Kale, Pranjal Awasthi, Stefani Karp, 2022, ICML.

Calibration and Consistency of Adversarial Surrogate Losses pdf

Mehryar Mohri, Pranjal Awasthi, Natalie Frank, 2021, NeurIPS.

Neural Active Learning with Performance Guarantees pdf

Claudio Gentile, Pranjal Awasthi, Christoph Dann, 2021, NeurIPS.

Equalized odds postprocessing under imperfect group information

Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern, 2019, AISTATS.

Crowdsourcing with Arbitrary Adversaries

Pranjal Awasthi, Matthäus Kleindessner, Pranjal Awasthi, 2018, ICML.

Efficient PAC Learning from the Crowd

Yishay Mansour, Avrim Blum, Pranjal Awasthi, 2017, COLT.

Theory and Algorithm for Batch Distribution Drift Problems

Corinna Cortes, Pranjal Awasthi, Christopher Mohri, 2023, AISTATS.

On Robustness to Adversarial Examples and Polynomial Optimization

Aravindan Vijayaraghavan, Pranjal Awasthi, Abhratanu Dutta, 2019, NeurIPS.

Decision trees for entity identification: approximation algorithms and hardness results

Mukesh K. Mohania, Sambuddha Roy, Venkatesan T. Chakaravarthy, 2007, TALG.

Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information

Pranjal Awasthi, Jamie Morgenstern, Alex Beutel, 2021, FAccT.

Learning Using Local Membership Queries

Varun Kanade, Vitaly Feldman, Pranjal Awasthi, 2012, COLT.

Approximation Algorithms and New Models for Clustering and Learning

Pranjal Awasthi, Pranjal Awasthi, 2013 .

Efficient active learning of sparse halfspaces with arbitrary bounded noise

Pranjal Awasthi, Chicheng Zhang, Jie Shen, 2020, NeurIPS.

Robust Matrix Completion from Quantized Observations

Ping Li, Pranjal Awasthi, Jie Shen, 2019, AISTATS.

Image Modeling Using Tree Structured Conditional Random Fields

Balaraman Ravindran, Pranjal Awasthi, Aakanksha Gagrani, 2007, IJCAI.

On the Rademacher Complexity of Linear Hypothesis Sets pdf

Pranjal Awasthi, Mehryar Mohri, Natalie Frank, 2020, ArXiv.

Learning Mixtures of Ranking Models

Avrim Blum, Aravindan Vijayaraghavan, Pranjal Awasthi, 2014, NIPS.

Center-based clustering under perturbation stability

Avrim Blum, Pranjal Awasthi, Or Sheffet, 2010, Inf. Process. Lett..

Center Based Clustering: A Foundational Perspective

Maria-Florina Balcan, Pranjal Awasthi, Maria-Florina Balcan, 2014 .

Spectral Embedding of k-Cliques, Graph Partitioning and k-Means

Ravishankar Krishnaswamy, Moses Charikar, Pranjal Awasthi, 2016, ITCS.

A Finer Calibration Analysis for Adversarial Robustness pdf

Mehryar Mohri, Pranjal Awasthi, Anqi Mao, 2021, ArXiv.

H-Consistency Bounds for Surrogate Loss Minimizers

M. Mohri, Pranjal Awasthi, Anqi Mao, 2022, International Conference on Machine Learning.

Theoretically Grounded Loss Functions and Algorithms for Adversarial Robustness

M. Mohri, Pranjal Awasthi, Anqi Mao, 2023, AISTATS.

DC-Programming for Neural Network Optimizations

M. Mohri, Pranjal Awasthi, Anqi Mao, 2023 .

Clustering Semi-Random Mixtures of Gaussians

Aravindan Vijayaraghavan, Pranjal Awasthi, Aravindan Vijayaraghavan, 2017, ICML.

A case for Data Democratization

Pranjal Awasthi, Jordana J. George, Jordana J. George, 2020, AMCIS.

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

Aravindan Vijayaraghavan, Pranjal Awasthi, Alex Tang, 2021, Neural Information Processing Systems.

The Hardness of Approximation of Euclidean k-Means

Ravishankar Krishnaswamy, Moses Charikar, Pranjal Awasthi, 2015, SoCG.

Distributionally Robust Data Join

Pranjal Awasthi, Christopher Jung, Jamie Morgenstern, 2022, FORC.

Part Of Speech Tagging and Chunking with HMM and CRF

Balaraman Ravindran, Pranjal Awasthi, Delip Rao, 2006 .

Measuring Model Fairness under Noisy Covariates: A Theoretical Perspective

Ed H. Chi, Pranjal Awasthi, Alex Beutel, 2021, AIES.

Learning and 1-bit Compressed Sensing under Asymmetric Noise

Maria-Florina Balcan, Pranjal Awasthi, Hongyang Zhang, 2016, COLT.

Efficient Learning of Linear Separators under Bounded Noise

Maria-Florina Balcan, Ruth Urner, Pranjal Awasthi, 2015, COLT.

Effectiveness of Equalized Odds for Fair Classification under Imperfect Group Information

Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern, 2019, ArXiv.

Trimmed Maximum Likelihood Estimation for Robust Generalized Linear Model

Rajat Sen, Pranjal Awasthi, Weihao Kong, 2022, NeurIPS.

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

Avrim Blum, Chen Dan, Pranjal Awasthi, 2018, ESA.

Decision trees for entity identification: Approximation algorithms and hardness results

Venkatesan T. Chakaravarthy, Vinayaka Pandit, Pranjal Awasthi, 2011, TALG.

The Power of Localization for Efficiently Learning Linear Separators with Malicious Noise pdf

Maria-Florina Balcan, Philip M. Long, Pranjal Awasthi, 2013, ArXiv.

Online Stochastic Optimization in the Large: Application to Kidney Exchange

Tuomas Sandholm, Pranjal Awasthi, Pranjal Awasthi, 2009, IJCAI.

Improving Length-Generalization in Transformers via Task Hinting

Pranjal Awasthi, Anupam Gupta, 2023, arXiv.org.

Learning using Local Membership Queries under Smooth Distributions pdf

Varun Kanade, Pranjal Awasthi, Varun Kanade, 2012, ArXiv.

Learning Mixtures of Ranking Models

Pranjal Awasthi, 2014, NIPS.

Supervised Clustering

Pranjal Awasthi, Reza Bosagh Zadeh, R. Zadeh, 2010, NIPS.

Stability Yields a PTAS for k-Median and k-Means Clustering

Avrim Blum, Pranjal Awasthi, Or Sheffet, 2010, 2010 IEEE 51st Annual Symposium on Foundations of Computer Science.

Effective and Inconspicuous Over-the-Air Adversarial Examples with Adaptive Filtering

Aravindan Vijayaraghavan, Pranjal Awasthi, P. O'Reilly, 2022, ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

A Theory of Learning with Competing Objectives and User Feedback

Y. Mansour, M. Mohri, Pranjal Awasthi, 2022 .

ReMI: A Dataset for Reasoning with Multiple Images

Pranjal Awasthi, Sreenivas Gollapudi, Nishanth Dikkala, 2024, ArXiv.