| 2025 | ICML | Sample, Scrutinize and Scale: Effective Inference-Time Search by Scaling Verification. | Eric Zhao, Pranjal Awasthi, Sreenivas Gollapudi |
| 2024 | ALT | Semi-supervised Group DRO: Combating Sparsity with Unlabeled Data. | Pranjal Awasthi, Satyen Kale, Ankit Pensia |
| 2024 | COLT | Learning Neural Networks with Sparse Activations. | Pranjal Awasthi, Nishanth Dikkala, Pritish Kamath, Raghu Meka |
| 2024 | ISAIM | A Theory of Learning with Competing Objectives and User Feedback. | Pranjal Awasthi, Corinna Cortes, Yishay Mansour, Mehryar Mohri |
| 2023 | AISTATS | Theory and Algorithm for Batch Distribution Drift Problems. | Pranjal Awasthi, Corinna Cortes, Christopher Mohri |
| 2023 | AISTATS | Theoretically Grounded Loss Functions and Algorithms for Adversarial Robustness. | Pranjal Awasthi, Anqi Mao, Mehryar Mohri, Yutao Zhong |
| 2023 | COLT | Open Problem: The Sample Complexity of Multi-Distribution Learning for VC Classes. | Pranjal Awasthi, Nika Haghtalab, Eric Zhao |
| 2023 | ICLR | Agnostic Learning of General ReLU Activation Using Gradient Descent. | Pranjal Awasthi, Alex Tang, Aravindan Vijayaraghavan |
| 2022 | AAAI | Beyond GNNs: An Efficient Architecture for Graph Problems. | Pranjal Awasthi, Abhimanyu Das, Sreenivas Gollapudi |
| 2022 | ALT | Understanding Simultaneous Train and Test Robustness. | Pranjal Awasthi, Sivaraman Balakrishnan, Aravindan Vijayaraghavan |
| 2022 | ICASSP | Effective and Inconspicuous Over-the-Air Adversarial Examples with Adaptive Filtering. | Patrick O'Reilly, Pranjal Awasthi, Aravindan Vijayaraghavan, Bryan Pardo |
| 2022 | ICLR | On the benefits of maximum likelihood estimation for Regression and Forecasting. | Pranjal Awasthi, Abhimanyu Das, Rajat Sen, Ananda Theertha Suresh |
| 2022 | ICML | Active Sampling for Min-Max Fairness. | Jacob D. Abernethy, Pranjal Awasthi, Matthus Kleindessner, Jamie Morgenstern, Chris Russell, Jie Zhang |
| 2022 | ICML | Individual Preference Stability for Clustering. | Saba Ahmadi, Pranjal Awasthi, Samir Khuller, Matthus Kleindessner, Jamie Morgenstern, Pattara Sukprasert, Ali Vakilian |
| 2022 | ICML | Congested Bandits: Optimal Routing via Short-term Resets. | Pranjal Awasthi, Kush Bhatia, Sreenivas Gollapudi, Kostas Kollias |
| 2022 | ICML | Do More Negative Samples Necessarily Hurt In Contrastive Learning? | Pranjal Awasthi, Nishanth Dikkala, Pritish Kamath |
| 2022 | ICML | H-Consistency Bounds for Surrogate Loss Minimizers. | Pranjal Awasthi, Anqi Mao, Mehryar Mohri, Yutao Zhong |
| 2022 | ICML | Agnostic Learnability of Halfspaces via Logistic Loss. | Ziwei Ji, Kwangjun Ahn, Pranjal Awasthi, Satyen Kale, Stefani Karp |
| 2021 | AIES | Measuring Model Fairness under Noisy Covariates: A Theoretical Perspective. | Flavien Prost, Pranjal Awasthi, Nick Blumm, Aditee Kumthekar, Trevor Potter, Li Wei, Xuezhi Wang, Ed H. Chi, Jilin Chen, Alex Beutel |
| 2021 | ALT | A Deep Conditioning Treatment of Neural Networks. | Naman Agarwal, Pranjal Awasthi, Satyen Kale |
| 2021 | COLT | Adversarially Robust Low Dimensional Representations. | Pranjal Awasthi, Vaggos Chatziafratis, Xue Chen, Aravindan Vijayaraghavan |
| 2021 | CVPR | Adversarial Robustness Across Representation Spaces. | Pranjal Awasthi, George Yu, Chun-Sung Ferng, Andrew Tomkins, Da-Cheng Juan |
| 2020 | AISTATS | Equalized odds postprocessing under imperfect group information. | Pranjal Awasthi, Matthus Kleindessner, Jamie Morgenstern |
| 2020 | COLT | Estimating Principal Components under Adversarial Perturbations. | Pranjal Awasthi, Xue Chen, Aravindan Vijayaraghavan |
| 2020 | ICIS | The Impact of Data Philanthropy on Global Health When Mediated Through Digital Epidemiology. | Pranjal Awasthi, Jordana J. George |
| 2020 | ICML | Adversarial Learning Guarantees for Linear Hypotheses and Neural Networks. | Pranjal Awasthi, Natalie Frank, Mehryar Mohri |
| 2019 | AISTATS | Robust Matrix Completion from Quantized Observations. | Jie Shen, Pranjal Awasthi, Ping Li |
| 2019 | ESA | Bilu-Linial Stability, Certified Algorithms and the Independent Set Problem. | Haris Angelidakis, Pranjal Awasthi, Avrim Blum, Vaggos Chatziafratis, Chen Dan |
| 2019 | ICALP | Robust Communication-Optimal Distributed Clustering Algorithms. | Pranjal Awasthi, Ainesh Bakshi, Maria-Florina Balcan, Colin White, David P. Woodruff |
| 2019 | ICML | Fair k-Center Clustering for Data Summarization. | Matthus Kleindessner, Pranjal Awasthi, Jamie Morgenstern |
| 2019 | ICML | Guarantees for Spectral Clustering with Fairness Constraints. | Matthus Kleindessner, Samira Samadi, Pranjal Awasthi, Jamie Morgenstern |
| 2018 | AISTATS | Robust Vertex Enumeration for Convex Hulls in High Dimensions. | Pranjal Awasthi, Bahman Kalantari, Yikai Zhang |
| 2018 | FOCS | Towards Learning Sparsely Used Dictionaries with Arbitrary Supports. | Pranjal Awasthi, Aravindan Vijayaraghavan |
| 2018 | ICML | Clustering Semi-Random Mixtures of Gaussians. | Pranjal Awasthi, Aravindan Vijayaraghavan |
| 2018 | ICML | Crowdsourcing with Arbitrary Adversaries. | Matthus Kleindessner, Pranjal Awasthi |
| 2017 | COLT | Efficient PAC Learning from the Crowd. | Pranjal Awasthi, Avrim Blum, Nika Haghtalab, Yishay Mansour |
| 2016 | COLT | Learning and 1-bit Compressed Sensing under Asymmetric Noise. | Pranjal Awasthi, Maria-Florina Balcan, Nika Haghtalab, Hongyang Zhang |
| 2015 | COLT | Efficient Learning of Linear Separators under Bounded Noise. | Pranjal Awasthi, Maria-Florina Balcan, Nika Haghtalab, Ruth Urner |
| 2015 | COLT | Label optimal regret bounds for online local learning. | Pranjal Awasthi, Moses Charikar, Kevin A. Lai, Andrej Risteski |
| 2014 | ICML | Local algorithms for interactive clustering. | Pranjal Awasthi, Maria-Florina Balcan, Konstantin Voevodski |
| 2014 | STOC | The power of localization for efficiently learning linear separators with noise. | Pranjal Awasthi, Maria-Florina Balcan, Philip M. Long |
| 2013 | COLT | Learning Using Local Membership Queries. | Pranjal Awasthi, Vitaly Feldman, Varun Kanade |
| 2010 | COLT | Improved Guarantees for Agnostic Learning of Disjunctions. | Pranjal Awasthi, Avrim Blum, Or Sheffet |
| 2010 | FOCS | Stability Yields a PTAS for k-Median and k-Means Clustering. | Pranjal Awasthi, Avrim Blum, Or Sheffet |
| 2010 | SAGT | On Nash-Equilibria of Approximation-Stable Games. | Pranjal Awasthi, Maria-Florina Balcan, Avrim Blum, Or Sheffet, Santosh S. Vempala |
| 2009 | IJCAI | Online Stochastic Optimization in the Large: Application to Kidney Exchange. | Pranjal Awasthi, Tuomas Sandholm |
| 2007 | IJCAI | Image Modeling Using Tree Structured Conditional Random Fields. | Pranjal Awasthi, Aakanksha Gagrani, Balaraman Ravindran |
| 2007 | PODS | Decision trees for entity identification: approximation algorithms and hardness results. | Venkatesan T. Chakaravarthy, Vinayaka Pandit, Sambuddha Roy, Pranjal Awasthi, Mukesh K. Mohania |