Polygonal Approximation of Closed Curves across Multiple Views. - M. Pawan Kumar; Saurabh Goyal; C. V. Jawahar; P. J. Narayanan (ICVGIP, 2002)

Extending Pictorial Structures for Object Recognition. - M. Pawan Kumar; Philip H. S. Torr; Andrew Zisserman (BMVC, 2004)

Building blocks for autonomous navigation using contour correspondences. - M. Pawan Kumar; C. V. Jawahar; P. J. Narayanan (ICIP, 2004)

Learning Layered Pictorial Structures from Video. - M. Pawan Kumar; Philip H. S. Torr; Andrew Zisserman (ICVGIP, 2004)

Discrete contours in multiple views: approximation and recognition. - M. Pawan Kumar; Saurabh Goyal; Sujit Kuthirummal; C. V. Jawahar; P. J. Narayanan (Image Vis. Comput., 2004)

OBJ CUT. - M. Pawan Kumar; Philip H. S. Torr; Andrew Zisserman (CVPR (1), 2005)

Learning Layered Motion Segmentation of Video. - M. Pawan Kumar; Philip H. S. Torr; Andrew Zisserman (ICCV, 2005)

An Object Category Specific mrffor Segmentation. - M. Pawan Kumar; Philip H. S. Torr; Andrew Zisserman (Toward Category-Level Object Recognition, 2006)

Solving Markov Random Fields using Second Order Cone Programming Relaxations. - M. Pawan Kumar; Philip H. S. Torr; Andrew Zisserman (CVPR (1), 2006)

Fast Memory-Efficient Generalized Belief Propagation. - M. Pawan Kumar; Philip H. S. Torr (ECCV (4), 2006)

Learning Class-Specific Edges for Object Detection and Segmentation. - Mukta Prasad; Andrew Zisserman; Andrew W. Fitzgibbon; M. Pawan Kumar; Philip H. S. Torr (ICVGIP, 2006)

P3 & Beyond: Solving Energies with Higher Order Cliques. - Pushmeet Kohli; M. Pawan Kumar; Philip H. S. Torr (CVPR, 2007)

An Invariant Large Margin Nearest Neighbour Classifier. - M. Pawan Kumar; Philip H. S. Torr; Andrew Zisserman (ICCV, 2007)

Efficiently solving convex relaxations for MAP estimation. - M. Pawan Kumar; Philip H. S. Torr (ICML, 2008)

Improved Moves for Truncated Convex Models. - M. Pawan Kumar; Philip H. S. Torr (NIPS, 2008)

Learning Layered Motion Segmentations of Video. - M. Pawan Kumar; Philip H. S. Torr; Andrew Zisserman (Int. J. Comput. Vis., 2008)

Efficient discriminative learning of parts-based models. - M. Pawan Kumar; Andrew Zisserman; Philip H. S. Torr (ICCV, 2009)

Learning a Small Mixture of Trees. - M. Pawan Kumar; Daphne Koller (NIPS, 2009)

MAP Estimation of Semi-Metric MRFs via Hierarchical Graph Cuts. - M. Pawan Kumar; Daphne Koller (UAI, 2009)

An Analysis of Convex Relaxations for MAP Estimation of Discrete MRFs. - M. Pawan Kumar; Vladimir Kolmogorov; Philip H. S. Torr (J. Mach. Learn. Res., 2009)

P³ & Beyond: Move Making Algorithms for Solving Higher Order Functions. - Pushmeet Kohli; M. Pawan Kumar; Philip H. S. Torr (IEEE Trans. Pattern Anal. Mach. Intell., 2009)

Energy minimization for linear envelope MRFs. - Pushmeet Kohli; M. Pawan Kumar (CVPR, 2010)

Efficiently selecting regions for scene understanding. - M. Pawan Kumar; Daphne Koller (CVPR, 2010)

Self-Paced Learning for Latent Variable Models. - M. Pawan Kumar; Benjamin Packer; Daphne Koller (NIPS, 2010)

OBJCUT: Efficient Segmentation Using Top-Down and Bottom-Up Cues. - M. Pawan Kumar; Philip H. S. Torr; Andrew Zisserman (IEEE Trans. Pattern Anal. Mach. Intell., 2010)

Learning specific-class segmentation from diverse data. - M. Pawan Kumar; Haithem Turki; Dan Preston; Daphne Koller (ICCV, 2011)

A Method for Integrating Network-on-Chip Topologies with 3D ICs. - M. Pawan Kumar; Anish S. Kumar; Srinivasan Murali; Luca Benini; Kamakoti Veezhinathan (ISVLSI, 2011)

A Simulation Based Buffer Sizing Algorithm for Network on Chips. - Anish S. Kumar; M. Pawan Kumar; Srinivasan Murali; V. Kamakoti 001; Luca Benini; Giovanni De Micheli (ISVLSI, 2011)

Improved Moves for Truncated Convex Models. - M. Pawan Kumar; Olga Veksler; Philip H. S. Torr (J. Mach. Learn. Res., 2011)

Modeling Latent Variable Uncertainty for Loss-based Learning. - M. Pawan Kumar; Benjamin Packer; Daphne Koller (ICML, 2012)

A Buffer-Sizing Algorithm for Network-on-Chips with Multiple Voltage-Frequency Islands. - Anish S. Kumar; M. Pawan Kumar; Srinivasan Murali; V. Kamakoti 001; Luca Benini; Giovanni De Micheli (J. Electr. Comput. Eng., 2012)

Max-Margin Min-Entropy Models. - Kevin Miller; M. Pawan Kumar; Benjamin Packer; Danny Goodman; Daphne Koller (AISTATS, 2012)

Weakly Supervised Learning for Structured Output Prediction. - M. Pawan Kumar (2013)

Learning from M/EEG Data with Variable Brain Activation Delays. - Wojciech Zaremba; M. Pawan Kumar; Alexandre Gramfort; Matthew B. Blaschko (IPMI, 2013)

Discriminative Parameter Estimation for Random Walks Segmentation. - Pierre-Yves Baudin; Danny Goodman; Puneet Kumar; Noura Azzabou; Pierre G. Carlier; Nikos Paragios; M. Pawan Kumar (MICCAI (3), 2013)

Discriminative Parameter Estimation for Random Walks Segmentation: Technical Report. - Pierre-Yves Baudin; Danny Goodman; Puneet Kumar; Noura Azzabou; Pierre G. Carlier; Nikos Paragios; M. Pawan Kumar (CoRR, 2013)

Optimizing Average Precision Using Weakly Supervised Data. - Aseem Behl; C. V. Jawahar; M. Pawan Kumar (CVPR, 2014)

Learning to Rank Using High-Order Information. - Puneet Kumar Dokania; Aseem Behl; C. V. Jawahar; M. Pawan Kumar (ECCV (4), 2014)

Rounding-based Moves for Metric Labeling. - M. Pawan Kumar (NIPS, 2014)

Efficient Optimization for Average Precision SVM. - Pritish Mohapatra; C. V. Jawahar; M. Pawan Kumar (NIPS, 2014)

Entropy-Based Latent Structured Output Prediction. - Diane Bouchacourt; Sebastian Nowozin; M. Pawan Kumar (ICCV, 2015)

Parsimonious Labeling. - Puneet Kumar Dokania; M. Pawan Kumar (ICCV, 2015)

Optimizing Average Precision Using Weakly Supervised Data. - Aseem Behl; Pritish Mohapatra; C. V. Jawahar; M. Pawan Kumar (IEEE Trans. Pattern Anal. Mach. Intell., 2015)

Parameter Estimation and Energy Minimization for Region-Based Semantic Segmentation. - M. Pawan Kumar; Haithem Turki; Dan Preston; Daphne Koller (IEEE Trans. Pattern Anal. Mach. Intell., 2015)

Coplanar Repeats by Energy Minimization. - James Pritts; Denys Rozumnyi; M. Pawan Kumar; Ondrej Chum (BMVC, 2016)

Efficient Continuous Relaxations for Dense CRF. - Alban Desmaison; Rudy Bunel; Pushmeet Kohli; Philip H. S. Torr; M. Pawan Kumar (ECCV (2), 2016)

Partial Linearization Based Optimization for Multi-class SVM. - Pritish Mohapatra; Puneet Kumar Dokania; C. V. Jawahar; M. Pawan Kumar (ECCV (5), 2016)

DISCO Nets: DISsimilarity COefficient Networks. - Diane Bouchacourt; M. Pawan Kumar; Sebastian Nowozin (CoRR, 2016)

Learning to superoptimize programs - Workshop Version. - Rudy Bunel; Alban Desmaison; M. Pawan Kumar; Philip H. S. Torr; Pushmeet Kohli (CoRR, 2016)

(Hyper)-Graphs Inference through Convex Relaxations and Move Making Algorithms: Contributions and Applications in Artificial Vision. - Nikos Komodakis; M. Pawan Kumar; Nikos Paragios (Found. Trends Comput. Graph. Vis., 2016)

Rounding-based Moves for Semi-Metric Labeling. - M. Pawan Kumar; Puneet Kumar Dokania (J. Mach. Learn. Res., 2016)

Efficient Linear Programming for Dense CRFs. - Thalaiyasingam Ajanthan; Alban Desmaison; Rudy Bunel; Mathieu Salzmann; Philip H. S. Torr; M. Pawan Kumar (CVPR, 2017)

Truncated Max-of-Convex Models. - Pankaj Pansari; M. Pawan Kumar (CVPR, 2017)

Trusting SVM for Piecewise Linear CNNs. - Leonard Berrada; Andrew Zisserman; M. Pawan Kumar (ICLR (Poster), 2017)

Learning to superoptimize programs. - Rudy Bunel; Alban Desmaison; M. Pawan Kumar; Philip H. S. Torr; Pushmeet Kohli (ICLR (Poster), 2017)

Piecewise Linear Neural Network verification: A comparative study. - Rudy Bunel; Ilker Turkaslan; Philip H. S. Torr; Pushmeet Kohli; M. Pawan Kumar (CoRR, 2017)

Learning to Round for Discrete Labeling Problems. - Pritish Mohapatra; C. V. Jawahar; M. Pawan Kumar (AISTATS, 2018)

Optimal Submodular Extensions for Marginal Estimation. - Pankaj Pansari; Chris Russell 001; M. Pawan Kumar (AISTATS, 2018)

Learning Human Poses from Actions. - Aditya Arun 001; C. V. Jawahar; M. Pawan Kumar (BMVC, 2018)

Efficient Optimization for Rank-Based Loss Functions. - Pritish Mohapatra; Michal Rolínek; C. V. Jawahar; Vladimir Kolmogorov; M. Pawan Kumar (CVPR, 2018)

Smooth Loss Functions for Deep Top-k Classification. - Leonard Berrada; Andrew Zisserman; M. Pawan Kumar (ICLR (Poster), 2018)

Worst-case Optimal Submodular Extensions for Marginal Estimation. - Pankaj Pansari; Chris Russell 001; M. Pawan Kumar (CoRR, 2018)

Dissimilarity Coefficient Based Weakly Supervised Object Detection. - Aditya Arun 001; C. V. Jawahar; M. Pawan Kumar (CVPR, 2019)

New Convex Relaxations for MRF Inference With Unknown Graphs. - Zhenhua Wang 003; Tong Liu; Qinfeng Shi; M. Pawan Kumar; Jianhua Zhang 002 (ICCV, 2019)

Deep Frank-Wolfe For Neural Network Optimization. - Leonard Berrada; Andrew Zisserman; M. Pawan Kumar (ICLR (Poster), 2019)

A Statistical Approach to Assessing Neural Network Robustness. - Stefan Webb; Tom Rainforth; Yee Whye Teh; M. Pawan Kumar (ICLR (Poster), 2019)

Linear programming-based submodular extensions for marginal estimation. - Pankaj Pansari; Chris Russell 001; M. Pawan Kumar (Comput. Vis. Image Underst., 2019)

Efficient Relaxations for Dense CRFs with Sparse Higher-Order Potentials. - Thomas Joy; Alban Desmaison; Thalaiyasingam Ajanthan; Rudy Bunel; Mathieu Salzmann; Pushmeet Kohli; Philip H. S. Torr; M. Pawan Kumar (SIAM J. Imaging Sci., 2019)

Weakly Supervised Instance Segmentation by Learning Annotation Consistent Instances. - Aditya Arun 001; C. V. Jawahar; M. Pawan Kumar (ECCV (28), 2020)

Neural Network Branching for Neural Network Verification. - Jingyue Lu; M. Pawan Kumar (ICLR, 2020)

Training Neural Networks for and by Interpolation. - Leonard Berrada; Andrew Zisserman; M. Pawan Kumar (ICML, 2020)

Lagrangian Decomposition for Neural Network Verification. - Rudy Bunel; Alessandro De Palma; Alban Desmaison; Krishnamurthy Dvijotham; Pushmeet Kohli; Philip H. S. Torr; M. Pawan Kumar (UAI, 2020)

Hybrid Models for Learning to Branch. - Prateek Gupta; Maxime Gasse; Elias B. Khalil; M. Pawan Kumar; Andrea Lodi 001; Yoshua Bengio (CoRR, 2020)

Branch and Bound for Piecewise Linear Neural Network Verification. - Rudy Bunel; Jingyue Lu; Ilker Turkaslan; Philip H. S. Torr; Pushmeet Kohli; M. Pawan Kumar (J. Mach. Learn. Res., 2020)

Scaling the Convex Barrier with Active Sets. - Alessandro De Palma; Harkirat S. Behl; Rudy Bunel; Philip H. S. Torr; M. Pawan Kumar (ICLR, 2021)

Overcoming the Convex Barrier for Simplex Inputs. - Harkirat Singh Behl; M. Pawan Kumar; Philip H. S. Torr; Krishnamurthy Dvijotham (NeurIPS, 2021)

Make Sure You're Unsure: A Framework for Verifying Probabilistic Specifications. - Leonard Berrada; Sumanth Dathathri; Krishnamurthy Dvijotham; Robert Stanforth; Rudy Bunel; Jonathan Uesato; Sven Gowal; M. Pawan Kumar (NeurIPS, 2021)

Generating adversarial examples with graph neural networks. - Florian Jaeckle; M. Pawan Kumar (UAI, 2021)

Verifying Probabilistic Specifications with Functional Lagrangians. - Leonard Berrada; Sumanth Dathathri; Krishnamurthy Dvijotham; Robert Stanforth; Rudy Bunel; Jonathan Uesato; Sven Gowal; M. Pawan Kumar (CoRR, 2021)

Improved Branch and Bound for Neural Network Verification via Lagrangian Decomposition. - Alessandro De Palma; Rudy Bunel; Alban Desmaison; Krishnamurthy Dvijotham; Pushmeet Kohli; Philip H. S. Torr; M. Pawan Kumar (CoRR, 2021)

Comment on Stochastic Polyak Step-Size: Performance of ALI-G. - Leonard Berrada; Andrew Zisserman; M. Pawan Kumar (CoRR, 2021)

Neural Network Branch-and-Bound for Neural Network Verification. - Florian Jaeckle; Jingyue Lu; M. Pawan Kumar (CoRR, 2021)

Improving Local Effectiveness for Global robust training. - Jingyue Lu; M. Pawan Kumar (CoRR, 2021)

Learning to be adversarially robust and differentially private. - Jamie Hayes; Borja Balle; M. Pawan Kumar (CoRR, 2022)

In Defense of the Unitary Scalarization for Deep Multi-Task Learning. - Vitaly Kurin; Alessandro De Palma; Ilya Kostrikov; Shimon Whiteson; M. Pawan Kumar (CoRR, 2022)

A Stochastic Bundle Method for Interpolating Networks. - Alasdair Paren; Leonard Berrada; Rudra P. K. Poudel; M. Pawan Kumar (CoRR, 2022)

IBP Regularization for Verified Adversarial Robustness via Branch-and-Bound. - Alessandro De Palma; Rudy Bunel; Krishnamurthy Dvijotham; M. Pawan Kumar; Robert Stanforth (CoRR, 2022)

A Stochastic Bundle Method for Interpolation. - Alasdair Paren; Leonard Berrada; Rudra P. K. Poudel; M. Pawan Kumar (J. Mach. Learn. Res., 2022)

ANCER: Anisotropic Certification via Sample-wise Volume Maximization. - Francisco Eiras; Motasem Alfarra; Philip H. S. Torr; M. Pawan Kumar; Puneet K. Dokania; Bernard Ghanem; Adel Bibi (Trans. Mach. Learn. Res., 2022)

Lookback for Learning to Branch. - Prateek Gupta; Elias Boutros Khalil; Didier Chételat; Maxime Gasse; Andrea Lodi 001; Yoshua Bengio; M. Pawan Kumar (Trans. Mach. Learn. Res., 2022)

Faking Interpolation Until You Make It. - Alasdair Paren; Rudra P. K. Poudel; M. Pawan Kumar (Trans. Mach. Learn. Res., 2022)

Neural Lower Bounds for Verification. - Florian Jaeckle; M. Pawan Kumar (SaTML, 2023)

Provably Correct Physics-Informed Neural Networks. - Francisco Eiras; Adel Bibi; Rudy Bunel; Krishnamurthy (Dj) Dvijotham; Philip H. S. Torr; M. Pawan Kumar (CoRR, 2023)

Faithful Knowledge Distillation. - Tom A. Lamb; Rudy Bunel; Krishnamurthy (Dj) Dvijotham; M. Pawan Kumar; Philip H. S. Torr; Francisco Eiras (CoRR, 2023)

Expressive Losses for Verified Robustness via Convex Combinations. - Alessandro De Palma; Rudy Bunel; Krishnamurthy (Dj) Dvijotham; M. Pawan Kumar; Robert Stanforth; Alessio Lomuscio (ICLR, 2024)

Efficient Error Certification for Physics-Informed Neural Networks. - Francisco Eiras; Adel Bibi; Rudy Bunel; Krishnamurthy Dj Dvijotham; Philip Torr 001; M. Pawan Kumar (ICML, 2024)

Verified Neural Compressed Sensing. - Rudy Bunel; Krishnamurthy Dvijotham; M. Pawan Kumar; Alessandro De Palma; Robert Stanforth (CoRR, 2024)

Mimicking User Data: On Mitigating Fine-Tuning Risks in Closed Large Language Models. - Francisco Eiras; Aleksandar Petrov; Philip H. S. Torr; M. Pawan Kumar; Adel Bibi (CoRR, 2024)

Scaling the Convex Barrier with Sparse Dual Algorithms. - Alessandro De Palma; Harkirat Singh Behl; Rudy Bunel; Philip H. S. Torr; M. Pawan Kumar (J. Mach. Learn. Res., 2024)

Mathematical discoveries from program search with large language models. - Bernardino Romera-Paredes; Mohammadamin Barekatain; Alexander Novikov 001; Matej Balog; M. Pawan Kumar; Emilien Dupont; Francisco J. R. Ruiz; Jordan S. Ellenberg; Pengming Wang 001; Omar Fawzi; Pushmeet Kohli; Alhussein Fawzi (Nat., 2024)

Do as I do (Safely): Mitigating Task-Specific Fine-tuning Risks in Large Language Models. - Francisco Eiras; Aleksandar Petrov; Philip Torr 001; M. Pawan Kumar; Adel Bibi (ICLR, 2025)

AlphaEvolve: A coding agent for scientific and algorithmic discovery. - Alexander Novikov 001; Ngân Vu; Marvin Eisenberger; Emilien Dupont; Po-Sen Huang; Adam Zsolt Wagner; Sergey Shirobokov; Borislav Kozlovskii; Francisco J. R. Ruiz; Abbas Mehrabian; M. Pawan Kumar; Abigail See; et al. (CoRR, 2025)