M. Pawan Kumar
31 papers · 2009–2025 · 9 conferences · across top CS/AI conferences
Achievements
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π Conference Polyglot (9) π Interdisciplinary Bridge π£ Hot Topic Early Bird π§ Keyword Pioneer π Academic Marathon (16)
πΊοΈ
Taxonomy Completionist
(38)
π£
Hot Topic Early Bird
π
Interdisciplinary Bridge
π¬
Deep Specialist
(14)
π
Keyword Champion
(2)
ποΈ
Keyword Collector
(81)
π
Conference Pioneer
π
Century Club
(31)
π₯
Unstoppable
(9)
π
Trend Setter
β‘
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
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