← Authors

M. Pawan Kumar

31 papers · 2009–2025 · 9 conferences · across top CS/AI conferences

Achievements

🐝 Cross-Pollinator (12) 🌈 Renaissance Researcher (7) 🌍 Conference Polyglot (9) πŸƒ Academic Marathon (16) 🧭 Keyword Pioneer πŸ—ΊοΈ Taxonomy Completionist (38) 🐣 Hot Topic Early Bird πŸŒ‰ Interdisciplinary Bridge πŸ”¬ Deep Specialist (14) πŸ† Keyword Champion (2) πŸ—ƒοΈ Keyword Collector (81) πŸš€ Conference Pioneer πŸ’Ž Century Club (31) πŸ”₯ Unstoppable (9) πŸ“ˆ Trend Setter ⚑ Prolific Year (5)

Conferences

ICLR (7) JMLR (6) CVPR (5) NIPS (4) ICCV (3) ICML (2) UAI (2) AISTATS (1) ECCV (1)

Top co-authors

Leonard Berrada (5) Philip Torr (5) Philip H.S. Torr (4) Rudy Bunel (4) C.V. Jawahar (4) Alessandro De Palma (4) Rudy R Bunel (4) Andrew Zisserman (3) Krishnamurthy Dvijotham (3) Jingyue Lu (2)

Keywords

convex optimization (6) neural network verification (5) energy minimization (5) markov random field (4) linear programming relaxation (4) graph cut (4) adversarial robustness (3) convex relaxation (3) support vector machine (3) combinatorial optimization (3) average precision (3) stochastic optimization (2) optimization algorithm (2) weakly supervised learning (2) graphical model (2) quadratic programming (2) adaptive learning rate (2) map estimation (2) formal verification (2) linear programming (2)

Papers

Do as I do (Safely): Mitigating Task-Specific Fine-tuning Risks in Large Language Models ICLR 2025
Scaling the Convex Barrier with Sparse Dual Algorithms JMLR 2024
Efficient Error Certification for Physics-Informed Neural Networks ICML 2024
Expressive Losses for Verified Robustness via Convex Combinations ICLR 2024
A Stochastic Bundle Method for Interpolation JMLR 2022
Overcoming the Convex Barrier for Simplex Inputs NIPS 2021
Scaling the Convex Barrier with Active Sets ICLR 2021
Make Sure You're Unsure: A Framework for Verifying Probabilistic Specifications NIPS 2021
Generating adversarial examples with graph neural networks UAI 2021
Neural Network Branching for Neural Network Verification ICLR 2020
Weakly Supervised Instance Segmentation by Learning Annotation Consistent Instances ECCV 2020
Training Neural Networks for and by Interpolation ICML 2020
Lagrangian Decomposition for Neural Network Verification UAI 2020
Branch and Bound for Piecewise Linear Neural Network Verification JMLR 2020
A Statistical Approach to Assessing Neural Network Robustness ICLR 2019
Dissimilarity Coefficient Based Weakly Supervised Object Detection CVPR 2019
New Convex Relaxations for MRF Inference With Unknown Graphs ICCV 2019
Deep Frank-Wolfe For Neural Network Optimization ICLR 2019
Smooth Loss Functions for Deep Top-k Classification ICLR 2018
Efficient Optimization for Rank-Based Loss Functions CVPR 2018
Truncated Max-Of-Convex Models CVPR 2017
Efficient Linear Programming for Dense CRFs CVPR 2017
Rounding-based Moves for Semi-Metric Labeling JMLR 2016
Parsimonious Labeling ICCV 2015
Entropy-Based Latent Structured Output Prediction ICCV 2015
Rounding-based Moves for Metric Labeling NIPS 2014
Optimizing Average Precision using Weakly Supervised Data CVPR 2014
Efficient Optimization for Average Precision SVM NIPS 2014
Max-Margin Min-Entropy Models AISTATS 2012
Improved Moves for Truncated Convex Models JMLR 2011
An Analysis of Convex Relaxations for MAP Estimation of Discrete MRFs JMLR 2009