Pranjal Awasthi

Publication Activity (10 Years)

Years Active: 2007-2024
Publications (10 Years): 120

Top Venues

CoRR

NeurIPS

ICML

COLT

Publications

Hanna Mazzawi, Pranjal Awasthi, Xavi Gonzalvo, Srikumar Ramalingam
Simulated Overparameterization. CoRR (2024)
Pranjal Awasthi, Nishanth Dikkala, Pritish Kamath, Raghu Meka
Learning Neural Networks with Sparse Activations. COLT (2024)
Bernd Bohnet, Kevin Swersky, Rosanne Liu, Pranjal Awasthi, Azade Nova, Javier Snaider, Hanie Sedghi, Aaron T. Parisi, Michael Collins, Angeliki Lazaridou, Orhan Firat, Noah Fiedel
Long-Span Question-Answering: Automatic Question Generation and QA-System Ranking via Side-by-Side Evaluation. CoRR (2024)
Naman Agarwal, Pranjal Awasthi, Satyen Kale, Eric Zhao
Stacking as Accelerated Gradient Descent. CoRR (2024)
Pranjal Awasthi, Nishanth Dikkala, Pritish Kamath, Raghu Meka
Learning Neural Networks with Sparse Activations. CoRR (2024)
Pranjal Awasthi, Satyen Kale, Ankit Pensia
Semi-supervised Group DRO: Combating Sparsity with Unlabeled Data. ALT (2024)
Mehran Kazemi, Nishanth Dikkala, Ankit Anand, Petar Devic, Ishita Dasgupta, Fangyu Liu, Bahare Fatemi, Pranjal Awasthi, Dee Guo, Sreenivas Gollapudi, Ahmed Qureshi
ReMI: A Dataset for Reasoning with Multiple Images. CoRR (2024)
Pranjal Awasthi, Corinna Cortes, Yishay Mansour, Mehryar Mohri
A Theory of Learning with Competing Objectives and User Feedback. ISAIM (2024)
Pranjal Awasthi, Corinna Cortes, Mehryar Mohri
Best-effort adaptation. Ann. Math. Artif. Intell. 92 (2) (2024)
Maximilian Böther, Abraham Sebastian, Pranjal Awasthi, Ana Klimovic, Srikumar Ramalingam
On Distributed Larger-Than-Memory Subset Selection With Pairwise Submodular Functions. CoRR (2024)
Hanseul Cho, Jaeyoung Cha, Pranjal Awasthi, Srinadh Bhojanapalli, Anupam Gupta, Chulhee Yun
Position Coupling: Leveraging Task Structure for Improved Length Generalization of Transformers. CoRR (2024)
Pranjal Awasthi, Nika Haghtalab, Eric Zhao
Open Problem: The Sample Complexity of Multi-Distribution Learning for VC Classes. COLT (2023)
Pranjal Awasthi, Corinna Cortes, Christopher Mohri
Theory and Algorithm for Batch Distribution Drift Problems. AISTATS (2023)
Pranjal Awasthi, Kush Bhatia, Sreenivas Gollapudi, Kostas Kollias
Congested Bandits: Optimal Routing via Short-term Resets. CoRR (2023)
Pranjal Awasthi, Alex Tang, Aravindan Vijayaraghavan
Agnostic Learning of General ReLU Activation Using Gradient Descent. ICLR (2023)
Pranjal Awasthi, Anqi Mao, Mehryar Mohri, Yutao Zhong
Theoretically Grounded Loss Functions and Algorithms for Adversarial Robustness. AISTATS (2023)
Pranjal Awasthi, Anupam Gupta
Improving Length-Generalization in Transformers via Task Hinting. CoRR (2023)
Pranjal Awasthi, Nika Haghtalab, Eric Zhao
The Sample Complexity of Multi-Distribution Learning for VC Classes. CoRR (2023)
Pranjal Awasthi, Corinna Cortes, Mehryar Mohri
Best-Effort Adaptation. CoRR (2023)
Jordana J. George, Jie (Kevin) Yan, Dorothy E. Leidner, Pranjal Awasthi
Does Engaging in Data Philanthropy Impact Business Value? Inf. Syst. Manag. 40 (2) (2023)
Pranjal Awasthi, Christopher Jung, Jamie Morgenstern
Distributionally Robust Data Join. FORC (2023)
Srikumar Ramalingam, Pranjal Awasthi, Sanjiv Kumar
A Weighted K-Center Algorithm for Data Subset Selection. CoRR (2023)
Patrick O'Reilly, Pranjal Awasthi, Aravindan Vijayaraghavan, Bryan Pardo
Effective and Inconspicuous Over-the-Air Adversarial Examples with Adaptive Filtering. ICASSP (2022)
Pranjal Awasthi, Abhimanyu Das, Rajat Sen, Ananda Theertha Suresh
On the benefits of maximum likelihood estimation for Regression and Forecasting. ICLR (2022)
Joan Puigcerver, Rodolphe Jenatton, Carlos Riquelme, Pranjal Awasthi, Srinadh Bhojanapalli
On the Adversarial Robustness of Mixture of Experts. CoRR (2022)
Pranjal Awasthi, Nishanth Dikkala, Pritish Kamath
Do More Negative Samples Necessarily Hurt In Contrastive Learning? ICML (2022)
Ziwei Ji, Kwangjun Ahn, Pranjal Awasthi, Satyen Kale, Stefani Karp
Agnostic Learnability of Halfspaces via Logistic Loss. CoRR (2022)
Saba Ahmadi, Pranjal Awasthi, Samir Khuller, Matthäus Kleindessner, Jamie Morgenstern, Pattara Sukprasert, Ali Vakilian
Individual Preference Stability for Clustering. CoRR (2022)
Pranjal Awasthi, Kush Bhatia, Sreenivas Gollapudi, Kostas Kollias
Congested Bandits: Optimal Routing via Short-term Resets. ICML (2022)
Pranjal Awasthi, Sivaraman Balakrishnan, Aravindan Vijayaraghavan
Understanding Simultaneous Train and Test Robustness. ALT (2022)
Weihao Kong, Rajat Sen, Pranjal Awasthi, Abhimanyu Das
Trimmed Maximum Likelihood Estimation for Robust Learning in Generalized Linear Models. CoRR (2022)
Pranjal Awasthi, Abhimanyu Das, Weihao Kong, Rajat Sen
Trimmed Maximum Likelihood Estimation for Robust Generalized Linear Model. NeurIPS (2022)
Pranjal Awasthi, Anqi Mao, Mehryar Mohri, Yutao Zhong
H-Consistency Bounds for Surrogate Loss Minimizers. ICML (2022)
Jacob D. Abernethy, Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern, Chris Russell, Jie Zhang
Active Sampling for Min-Max Fairness. ICML (2022)
Joan Puigcerver, Rodolphe Jenatton, Carlos Riquelme, Pranjal Awasthi, Srinadh Bhojanapalli
On the Adversarial Robustness of Mixture of Experts. NeurIPS (2022)
Pranjal Awasthi, Anqi Mao, Mehryar Mohri, Yutao Zhong
H-Consistency Estimation Error of Surrogate Loss Minimizers. CoRR (2022)
Ziwei Ji, Kwangjun Ahn, Pranjal Awasthi, Satyen Kale, Stefani Karp
Agnostic Learnability of Halfspaces via Logistic Loss. ICML (2022)
Pranjal Awasthi, Alex Tang, Aravindan Vijayaraghavan
Agnostic Learning of General ReLU Activation Using Gradient Descent. CoRR (2022)
Pranjal Awasthi, Abhimanyu Das, Sreenivas Gollapudi
Beyond GNNs: An Efficient Architecture for Graph Problems. AAAI (2022)
Nived Rajaraman, Fnu Devvrit, Pranjal Awasthi
Semi-supervised Active Linear Regression. NeurIPS (2022)
Pranjal Awasthi, Nishanth Dikkala, Pritish Kamath
Do More Negative Samples Necessarily Hurt in Contrastive Learning? CoRR (2022)
Saba Ahmadi, Pranjal Awasthi, Samir Khuller, Matthäus Kleindessner, Jamie Morgenstern, Pattara Sukprasert, Ali Vakilian
Individual Preference Stability for Clustering. ICML (2022)
Pranjal Awasthi, Christopher Jung, Jamie Morgenstern
Distributionally Robust Data Join. CoRR (2022)
Pranjal Awasthi, Anqi Mao, Mehryar Mohri, Yutao Zhong
Multi-Class $H$-Consistency Bounds. NeurIPS (2022)
Pranjal Awasthi, Abhimanyu Das, Sreenivas Gollapudi
A Convergence Analysis of Gradient Descent on Graph Neural Networks. NeurIPS (2021)
Pranjal Awasthi, Alex Beutel, Matthäus Kleindessner, Jamie Morgenstern, Xuezhi Wang
Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information. CoRR (2021)
Pranjal Awasthi, Natalie S. Frank, Mehryar Mohri
On the Existence of the Adversarial Bayes Classifier (Extended Version). CoRR (2021)
Naman Agarwal, Pranjal Awasthi, Satyen Kale
A Deep Conditioning Treatment of Neural Networks. ALT (2021)
Pranjal Awasthi, Abhimanyu Das, Rajat Sen, Ananda Theertha Suresh
On the benefits of maximum likelihood estimation for Regression and Forecasting. CoRR (2021)
Fnu Devvrit, Nived Rajaraman, Pranjal Awasthi
Semi-supervised Active Regression. CoRR (2021)
Pranjal Awasthi, George Yu, Chun-Sung Ferng, Andrew Tomkins, Da-Cheng Juan
Adversarial Robustness Across Representation Spaces. CVPR (2021)
Flavien Prost, Pranjal Awasthi, Nick Blumm, Aditee Kumthekar, Trevor Potter, Li Wei, Xuezhi Wang, Ed H. Chi, Jilin Chen, Alex Beutel
Measuring Model Fairness under Noisy Covariates: A Theoretical Perspective. AIES (2021)
Pranjal Awasthi, Alex Tang, Aravindan Vijayaraghavan
Efficient Algorithms for Learning Depth-2 Neural Networks with General ReLU Activations. NeurIPS (2021)
Pranjal Awasthi, Natalie Frank, Anqi Mao, Mehryar Mohri, Yutao Zhong
Calibration and Consistency of Adversarial Surrogate Losses. CoRR (2021)
Pranjal Awasthi, Vaggos Chatziafratis, Xue Chen, Aravindan Vijayaraghavan
Adversarially Robust Low Dimensional Representations. COLT (2021)
Pranjal Awasthi, Natalie Frank, Mehryar Mohri
On the Existence of The Adversarial Bayes Classifier. NeurIPS (2021)
Jacob D. Abernethy, Pranjal Awasthi, Satyen Kale
A Multiclass Boosting Framework for Achieving Fast and Provable Adversarial Robustness. CoRR (2021)
Zhilei Wang, Pranjal Awasthi, Christoph Dann, Ayush Sekhari, Claudio Gentile
Neural Active Learning with Performance Guarantees. NeurIPS (2021)
Flavien Prost, Pranjal Awasthi, Nick Blumm, Aditee Kumthekar, Trevor Potter, Li Wei, Xuezhi Wang, Ed H. Chi, Jilin Chen, Alex Beutel
Measuring Model Fairness under Noisy Covariates: A Theoretical Perspective. CoRR (2021)
Pranjal Awasthi, Alex Tang, Aravindan Vijayaraghavan
Efficient Algorithms for Learning Depth-2 Neural Networks with General ReLU Activations. CoRR (2021)
Pranjal Awasthi, Alex Beutel, Matthäus Kleindessner, Jamie Morgenstern, Xuezhi Wang
Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information. FAccT (2021)
Pranjal Awasthi, Anqi Mao, Mehryar Mohri, Yutao Zhong
A Finer Calibration Analysis for Adversarial Robustness. CoRR (2021)
Pranjal Awasthi, Natalie Frank, Anqi Mao, Mehryar Mohri, Yutao Zhong
Calibration and Consistency of Adversarial Surrogate Losses. NeurIPS (2021)
Pranjal Awasthi, Christoph Dann, Claudio Gentile, Ayush Sekhari, Zhilei Wang
Neural Active Learning with Performance Guarantees. CoRR (2021)
Pranjal Awasthi, Xue Chen, Aravindan Vijayaraghavan
Estimating Principal Components under Adversarial Perturbations. CoRR (2020)
Pranjal Awasthi, Jordana J. George
The Impact of Data Philanthropy on Global Health When Mediated Through Digital Epidemiology. ICIS (2020)
Jacob D. Abernethy, Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern, Jie Zhang
Adaptive Sampling to Reduce Disparate Performance. CoRR (2020)
Chicheng Zhang, Jie Shen, Pranjal Awasthi
Efficient active learning of sparse halfspaces with arbitrary bounded noise. NeurIPS (2020)
Pranjal Awasthi, Himanshu Jain, Ankit Singh Rawat, Aravindan Vijayaraghavan
Adversarial robustness via robust low rank representations. NeurIPS (2020)
Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern
Equalized odds postprocessing under imperfect group information. AISTATS (2020)
Pranjal Awasthi, Bahman Kalantari, Yikai Zhang
Robust vertex enumeration for convex hulls in high dimensions. Ann. Oper. Res. 295 (1) (2020)
Pranjal Awasthi, Satyen Kale, Stefani Karp, Mehryar Mohri
PAC-Bayes Learning Bounds for Sample-Dependent Priors. NeurIPS (2020)
Pranjal Awasthi, Natalie Frank, Mehryar Mohri
Adversarial Learning Guarantees for Linear Hypotheses and Neural Networks. ICML (2020)
Pranjal Awasthi, Himanshu Jain, Ankit Singh Rawat, Aravindan Vijayaraghavan
Adversarial robustness via robust low rank representations. CoRR (2020)
Pranjal Awasthi, Jordana J. George
A case for Data Democratization. AMCIS (2020)
Chicheng Zhang, Jie Shen, Pranjal Awasthi
Efficient active learning of sparse halfspaces with arbitrary bounded noise. CoRR (2020)
Pranjal Awasthi, Xue Chen, Aravindan Vijayaraghavan
Estimating Principal Components under Adversarial Perturbations. COLT (2020)
Pranjal Awasthi, Corinna Cortes, Yishay Mansour, Mehryar Mohri
Beyond Individual and Group Fairness. CoRR (2020)
Matthäus Kleindessner, Pranjal Awasthi, Jamie Morgenstern
A Notion of Individual Fairness for Clustering. CoRR (2020)
Naman Agarwal, Pranjal Awasthi, Satyen Kale
A Deep Conditioning Treatment of Neural Networks. CoRR (2020)
Pranjal Awasthi, Natalie Frank, Mehryar Mohri
On the Rademacher Complexity of Linear Hypothesis Sets. CoRR (2020)
Pranjal Awasthi, Natalie Frank, Mehryar Mohri
Adversarial Learning Guarantees for Linear Hypotheses and Neural Networks. CoRR (2020)
Pranjal Awasthi, George Yu, Chun-Sung Ferng, Andrew Tomkins, Da-Cheng Juan
Adversarial Robustness Across Representation Spaces. CoRR (2020)
Pranjal Awasthi, Ainesh Bakshi, Maria-Florina Balcan, Colin White, David P. Woodruff
Robust Communication-Optimal Distributed Clustering Algorithms. ICALP (2019)
Haris Angelidakis, Pranjal Awasthi, Avrim Blum, Vaggos Chatziafratis, Chen Dan
Bilu-Linial Stability, Certified Algorithms and the Independent Set Problem. ESA (2019)
Matthäus Kleindessner, Samira Samadi, Pranjal Awasthi, Jamie Morgenstern
Guarantees for Spectral Clustering with Fairness Constraints. ICML (2019)
Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern
Effectiveness of Equalized Odds for Fair Classification under Imperfect Group Information. CoRR (2019)
Matthäus Kleindessner, Samira Samadi, Pranjal Awasthi, Jamie Morgenstern
Guarantees for Spectral Clustering with Fairness Constraints. CoRR (2019)
Jie Shen, Pranjal Awasthi, Ping Li
Robust Matrix Completion from Quantized Observations. AISTATS (2019)
Pranjal Awasthi, Abhratanu Dutta, Aravindan Vijayaraghavan
On Robustness to Adversarial Examples and Polynomial Optimization. NeurIPS (2019)
Matthäus Kleindessner, Pranjal Awasthi, Jamie Morgenstern
Fair k-Center Clustering for Data Summarization. CoRR (2019)
Pranjal Awasthi, Vaggos Chatziafratis, Xue Chen, Aravindan Vijayaraghavan
Adversarially Robust Low Dimensional Representations. CoRR (2019)
Matthäus Kleindessner, Pranjal Awasthi, Jamie Morgenstern
Fair k-Center Clustering for Data Summarization. ICML (2019)
Pranjal Awasthi, Abhratanu Dutta, Aravindan Vijayaraghavan
On Robustness to Adversarial Examples and Polynomial Optimization. CoRR (2019)
Pranjal Awasthi, Vineet Goyal, Brian Y. Lu
On the adaptivity gap in two-stage robust linear optimization under uncertain packing constraints. Math. Program. 173 (1-2) (2019)
Haris Angelidakis, Pranjal Awasthi, Avrim Blum, Vaggos Chatziafratis, Chen Dan
Bilu-Linial stability, certified algorithms and the Independent Set problem. CoRR (2018)
Pranjal Awasthi, Aravindan Vijayaraghavan
Towards Learning Sparsely Used Dictionaries with Arbitrary Supports. CoRR (2018)
Matthäus Kleindessner, Pranjal Awasthi
Crowdsourcing with Arbitrary Adversaries. ICML (2018)
Pranjal Awasthi, Bahman Kalantari, Yikai Zhang
Robust Vertex Enumeration for Convex Hulls in High Dimensions. AISTATS (2018)
Pranjal Awasthi, Bahman Kalantari, Yikai Zhang
Robust Vertex Enumeration for Convex Hulls in High Dimensions. CoRR (2018)

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Publication Activity (10 Years)

Top Topics

Top Venues

Pranjal Awasthi