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Pranjal Awasthi

54 papers · 2010–2025 · 9 conferences · across top CS/AI conferences

Achievements

πŸƒ Academic Marathon (15) 🐣 Hot Topic Early Bird 🌍 Conference Polyglot (9) πŸŒ‰ Interdisciplinary Bridge 🐝 Cross-Pollinator (3) πŸ—ΊοΈ Taxonomy Completionist (12) 🌈 Renaissance Researcher (8) 🧭 Keyword Pioneer πŸ”¬ Deep Specialist (18) πŸ‘‘ Triple Crown πŸ† Keyword Champion (2) πŸ† Grand Slam πŸ’Ž Century Club (54) πŸ—ƒοΈ Keyword Collector (57) πŸ”₯ Unstoppable (13) πŸ“ˆ Trend Setter ⚑ Prolific Year (8) ❓ The Questioner

Conferences

NIPS (19) ICML (13) COLT (9) AISTATS (5) ALT (3) ICLR (2) AAAI (1) CVPR (1) JMLR (1)

Top co-authors

Aravindan Vijayaraghavan (9) Mehryar Mohri (7) Sreenivas Gollapudi (6) MatthΓ€us Kleindessner (5) Jamie Morgenstern (5) Anqi Mao (4) Abhimanyu Das (4) Satyen Kale (4) Yutao Zhong (4) Nika Haghtalab (4)

Keywords

adversarial robustness (10) active learning (6) neural network (5) surrogate loss (4) learning theory (4) adversarial perturbation (3) bounded noise (3) pac learning (3) generalization bound (2) halfspace learning (2) clustering algorithm (2) robust estimation (2) semi-supervised learning (2) representation learning (2) neural tangent kernel (2) tensor decomposition (2) label noise (2) principal component analysis (2) domain adaptation (2) sample complexity (2)

Papers

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