M. Pawan Kumar
31 papers · 2009–2025 · 9 venues · across top CS/AI venues
Authors are matched by name, so researchers who share a name ("Yang Liu", "Wei Wang") appear as a single profile and their paper counts, venues and achievements are combined. How identity is handled.
Achievements
Jump to papers ↓+8 more ↓ Show less ↑
πΊοΈ Taxonomy Completionist (43) π Academic Marathon Β· Full π Conference Polyglot Β· Silver π Interdisciplinary Bridge Β· Silver π£ Hot Topic Early Bird
π
Keyword Champion
(2)
π¬
Deep Specialist
(17)
π
Renaissance Researcher
(6)
π₯
Unstoppable Β· Bronze
β‘
Prolific Year Β· Productive
π
Conference Pioneer
π
Century Club Β· Bronze
ποΈ
Keyword Collector
(117)
Top co-authors
Research topics
Machine Learning
(27)
Optimization & Theory Β· Machine Learning
(22)
Artificial Intelligence
(10)
Deep Learning
(10)
Core AI
(10)
Core Methods
(10)
Computer Vision
(7)
Mathematics & Optimization
(6)
Optimization
(6)
Analysis
(5)
Architectures
(4)
Learning Types
(4)
Optimization & Theory Β· Deep Learning
(4)
Application Areas
(3)
Techniques
(3)
Keywords
neural network verification
(7)
convex optimization
(6)
energy minimization
(5)
adversarial robustness
(4)
linear programming relaxation
(4)
formal verification
(4)
graph cut
(4)
markov random field
(4)
support vector machine
(3)
linear programming
(3)
combinatorial optimization
(3)
convex relaxation
(3)
average precision
(3)
weakly supervised learning
(2)
object detection
(2)
stochastic optimization
(2)
adaptive learning rate
(2)
stochastic gradient descent
(2)
branch and bound
(2)
map estimation
(2)
Papers
Do as I do (Safely): Mitigating Task-Specific Fine-tuning Risks in Large Language Models
ICLR 2025
Efficient Error Certification for Physics-Informed Neural Networks
ICML 2024
Expressive Losses for Verified Robustness via Convex Combinations
ICLR 2024
Scaling the Convex Barrier with Sparse Dual Algorithms
JMLR 2024
A Stochastic Bundle Method for Interpolation
JMLR 2022
Generating adversarial examples with graph neural networks
UAI 2021
Make Sure You're Unsure: A Framework for Verifying Probabilistic Specifications
NeurIPS 2021
Overcoming the Convex Barrier for Simplex Inputs
NeurIPS 2021
Scaling the Convex Barrier with Active Sets
ICLR 2021
Branch and Bound for Piecewise Linear Neural Network Verification
JMLR 2020
Lagrangian Decomposition for Neural Network Verification
UAI 2020
Neural Network Branching for Neural Network Verification
ICLR 2020
Training Neural Networks for and by Interpolation
ICML 2020
Weakly Supervised Instance Segmentation by Learning Annotation Consistent Instances
ECCV 2020
A Statistical Approach to Assessing Neural Network Robustness
ICLR 2019
Deep Frank-Wolfe For Neural Network Optimization
ICLR 2019
Dissimilarity Coefficient Based Weakly Supervised Object Detection
CVPR 2019
New Convex Relaxations for MRF Inference With Unknown Graphs
ICCV 2019
Efficient Optimization for Rank-Based Loss Functions
CVPR 2018
Smooth Loss Functions for Deep Top-k Classification
ICLR 2018
Efficient Linear Programming for Dense CRFs
CVPR 2017
Truncated Max-Of-Convex Models
CVPR 2017
Rounding-based Moves for Semi-Metric Labeling
JMLR 2016
Entropy-Based Latent Structured Output Prediction
ICCV 2015
Parsimonious Labeling
ICCV 2015
Efficient Optimization for Average Precision SVM
NeurIPS 2014
Optimizing Average Precision using Weakly Supervised Data
CVPR 2014
Rounding-based Moves for Metric Labeling
NeurIPS 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