Pranjal Awasthi

Sample, Scrutinize and Scale: Effective Inference-Time Search by Scaling Verification ICML 2025 Position Coupling: Improving Length Generalization of Arithmetic Transformers Using Task Structure NIPS 2024 ReMI: A Dataset for Reasoning with Multiple Images NIPS 2024 Learning Neural Networks with Sparse Activations COLT 2024 Semantic Routing via Autoregressive Modeling NIPS 2024 Semi-supervised Group DRO: Combating Sparsity with Unlabeled Data ALT 2024 Theoretically Grounded Loss Functions and Algorithms for Adversarial Robustness AISTATS 2023 Open Problem: The Sample Complexity of Multi-Distribution Learning for VC Classes COLT 2023 Theory and Algorithm for Batch Distribution Drift Problems AISTATS 2023 Agnostic Learning of General ReLU Activation Using Gradient Descent ICLR 2023 On the benefits of maximum likelihood estimation for Regression and Forecasting ICLR 2022 Agnostic Learnability of Halfspaces via Logistic Loss ICML 2022 Do More Negative Samples Necessarily Hurt In Contrastive Learning? ICML 2022 Multi-Class $H$-Consistency Bounds NIPS 2022 Trimmed Maximum Likelihood Estimation for Robust Generalized Linear Model NIPS 2022 Semi-supervised Active Linear Regression NIPS 2022 On the Adversarial Robustness of Mixture of Experts NIPS 2022 Beyond GNNs: An Efficient Architecture for Graph Problems AAAI 2022 Congested Bandits: Optimal Routing via Short-term Resets ICML 2022 Individual Preference Stability for Clustering ICML 2022 H-Consistency Bounds for Surrogate Loss Minimizers ICML 2022 Understanding Simultaneous Train and Test Robustness ALT 2022 Active Sampling for Min-Max Fairness ICML 2022 Calibration and Consistency of Adversarial Surrogate Losses NIPS 2021 Neural Active Learning with Performance Guarantees NIPS 2021 A Convergence Analysis of Gradient Descent on Graph Neural Networks NIPS 2021 Efficient Algorithms for Learning Depth-2 Neural Networks with General ReLU Activations NIPS 2021 A Deep Conditioning Treatment of Neural Networks ALT 2021 Adversarially Robust Low Dimensional Representations COLT 2021 On the Existence of The Adversarial Bayes Classifier NIPS 2021 Adversarial Robustness Across Representation Spaces CVPR 2021 Adversarial robustness via robust low rank representations NIPS 2020 PAC-Bayes Learning Bounds for Sample-Dependent Priors NIPS 2020 Efficient active learning of sparse halfspaces with arbitrary bounded noise NIPS 2020 Equalized odds postprocessing under imperfect group information AISTATS 2020 Estimating Principal Components under Adversarial Perturbations COLT 2020 Adversarial Learning Guarantees for Linear Hypotheses and Neural Networks ICML 2020 Robust Matrix Completion from Quantized Observations AISTATS 2019 On Robustness to Adversarial Examples and Polynomial Optimization NIPS 2019 Guarantees for Spectral Clustering with Fairness Constraints ICML 2019 Fair k-Center Clustering for Data Summarization ICML 2019 Crowdsourcing with Arbitrary Adversaries ICML 2018 Robust Vertex Enumeration for Convex Hulls in High Dimensions AISTATS 2018 Clustering Semi-Random Mixtures of Gaussians ICML 2018 Efficient PAC Learning from the Crowd COLT 2017 Local algorithms for interactive clustering JMLR 2017 Learning and 1-bit Compressed Sensing under Asymmetric Noise COLT 2016 Label optimal regret bounds for online local learning COLT 2015 Efficient Learning of Linear Separators under Bounded Noise COLT 2015 On some provably correct cases of variational inference for topic models NIPS 2015 Local algorithms for interactive clustering ICML 2014 Learning Mixtures of Ranking Models NIPS 2014 Learning Using Local Membership Queries COLT 2013 Supervised Clustering NIPS 2010