M. Pawan Kumar

According to our database, M. Pawan Kumar authored at least 96 papers between 2002 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Mathematical discoveries from program search with large language models.

Bernardino Romera-Paredes, Mohammadamin Barekatain, Alexander Novikov, Matej Balog, M. Pawan Kumar, Emilien Dupont, Francisco J. R. Ruiz, Jordan S. Ellenberg, Pengming Wang, Omar Fawzi, Pushmeet Kohli, Alhussein Fawzi

Nat., January, 2024

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 Dvijotham, M. Pawan Kumar, Robert Stanforth, Alessio Lomuscio

CoRR, 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

2022

Faking Interpolation Until You Make It.

Alasdair Paren, Rudra P. K. Poudel, M. Pawan Kumar

Trans. Mach. Learn. Res., 2022

Lookback for Learning to Branch.

Prateek Gupta, Elias Boutros Khalil, Didier Chételat, Maxime Gasse, Andrea Lodi, Yoshua Bengio, M. Pawan Kumar

Trans. 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

A Stochastic Bundle Method for Interpolation.

Alasdair Paren, Leonard Berrada, Rudra P. K. Poudel, M. Pawan Kumar

J. Mach. Learn. Res., 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 Interpolating Networks.

Alasdair Paren, Leonard Berrada, Rudra P. K. Poudel, 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

Learning to be adversarially robust and differentially private.

Jamie Hayes, Borja Balle, M. Pawan Kumar

CoRR, 2022

2021

Improving Local Effectiveness for Global robust training.

Jingyue Lu, M. Pawan Kumar

CoRR, 2021

Neural Network Branch-and-Bound for Neural Network Verification.

Florian Jaeckle, Jingyue Lu, M. Pawan Kumar

CoRR, 2021

Comment on Stochastic Polyak Step-Size: Performance of ALI-G.

Leonard Berrada, Andrew Zisserman, 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

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

Scaling the Convex Barrier with Sparse Dual Algorithms.

Alessandro De Palma, Harkirat Singh Behl, Rudy Bunel, Philip H. S. Torr, M. Pawan Kumar

CoRR, 2021

Generating adversarial examples with graph neural networks.

Florian Jaeckle, M. Pawan Kumar

Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 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

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Overcoming the Convex Barrier for Simplex Inputs.

Harkirat Singh Behl, M. Pawan Kumar, Philip H. S. Torr, Krishnamurthy Dvijotham

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Scaling the Convex Barrier with Active Sets.

Alessandro De Palma, Harkirat S. Behl, Rudy Bunel, Philip H. S. Torr, M. Pawan Kumar

Proceedings of the 9th International Conference on Learning Representations, 2021

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

Hybrid Models for Learning to Branch.

Prateek Gupta, Maxime Gasse, Elias B. Khalil, M. Pawan Kumar, Andrea Lodi, Yoshua Bengio

CoRR, 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

Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

Training Neural Networks for and by Interpolation.

Leonard Berrada, Andrew Zisserman, M. Pawan Kumar

Proceedings of the 37th International Conference on Machine Learning, 2020

Neural Network Branching for Neural Network Verification.

Jingyue Lu, M. Pawan Kumar

Proceedings of the 8th International Conference on Learning Representations, 2020

Weakly Supervised Instance Segmentation by Learning Annotation Consistent Instances.

Aditya Arun, C. V. Jawahar, M. Pawan Kumar

Proceedings of the Computer Vision - ECCV 2020, 2020

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

Linear programming-based submodular extensions for marginal estimation.

Pankaj Pansari, Chris Russell, M. Pawan Kumar

Comput. Vis. Image Underst., 2019

A Statistical Approach to Assessing Neural Network Robustness.

Stefan Webb, Tom Rainforth, Yee Whye Teh, M. Pawan Kumar

Proceedings of the 7th International Conference on Learning Representations, 2019

Deep Frank-Wolfe For Neural Network Optimization.

Leonard Berrada, Andrew Zisserman, M. Pawan Kumar

Proceedings of the 7th International Conference on Learning Representations, 2019

New Convex Relaxations for MRF Inference With Unknown Graphs.

Zhenhua Wang, Tong Liu, Qinfeng Shi, M. Pawan Kumar, Jianhua Zhang

Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, 2019

Dissimilarity Coefficient Based Weakly Supervised Object Detection.

Aditya Arun, C. V. Jawahar, M. Pawan Kumar

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

2018

Worst-case Optimal Submodular Extensions for Marginal Estimation.

Pankaj Pansari, Chris Russell, M. Pawan Kumar

CoRR, 2018

Smooth Loss Functions for Deep Top-k Classification.

Leonard Berrada, Andrew Zisserman, M. Pawan Kumar

Proceedings of the 6th International Conference on Learning Representations, 2018

Efficient Optimization for Rank-Based Loss Functions.

Pritish Mohapatra, Michal Rolínek, C. V. Jawahar, Vladimir Kolmogorov, M. Pawan Kumar

Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, 2018

Learning Human Poses from Actions.

Aditya Arun, C. V. Jawahar, M. Pawan Kumar

Proceedings of the British Machine Vision Conference 2018, 2018

Optimal Submodular Extensions for Marginal Estimation.

Pankaj Pansari, Chris Russell, M. Pawan Kumar

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

Learning to Round for Discrete Labeling Problems.

Pritish Mohapatra, C. V. Jawahar, M. Pawan Kumar

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

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 superoptimize programs.

Rudy Bunel, Alban Desmaison, M. Pawan Kumar, Philip H. S. Torr, Pushmeet Kohli

Proceedings of the 5th International Conference on Learning Representations, 2017

Trusting SVM for Piecewise Linear CNNs.

Leonard Berrada, Andrew Zisserman, M. Pawan Kumar

Proceedings of the 5th International Conference on Learning Representations, 2017

Truncated Max-of-Convex Models.

Pankaj Pansari, M. Pawan Kumar

Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017

Efficient Linear Programming for Dense CRFs.

Thalaiyasingam Ajanthan, Alban Desmaison, Rudy Bunel, Mathieu Salzmann, Philip H. S. Torr, M. Pawan Kumar

Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017

2016

Rounding-based Moves for Semi-Metric Labeling.

M. Pawan Kumar, Puneet Kumar Dokania

J. Mach. Learn. Res., 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

Learning to superoptimize programs - Workshop Version.

Rudy Bunel, Alban Desmaison, M. Pawan Kumar, Philip H. S. Torr, Pushmeet Kohli

CoRR, 2016

DISCO Nets: DISsimilarity COefficient Networks.

Diane Bouchacourt, M. Pawan Kumar, Sebastian Nowozin

CoRR, 2016

Partial Linearization Based Optimization for Multi-class SVM.

Pritish Mohapatra, Puneet Kumar Dokania, C. V. Jawahar, M. Pawan Kumar

Proceedings of the Computer Vision - ECCV 2016, 2016

Efficient Continuous Relaxations for Dense CRF.

Alban Desmaison, Rudy Bunel, Pushmeet Kohli, Philip H. S. Torr, M. Pawan Kumar

Proceedings of the Computer Vision - ECCV 2016, 2016

Coplanar Repeats by Energy Minimization.

James Pritts, Denys Rozumnyi, M. Pawan Kumar, Ondrej Chum

Proceedings of the British Machine Vision Conference 2016, 2016

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

Optimizing Average Precision Using Weakly Supervised Data.

Aseem Behl, Pritish Mohapatra, C. V. Jawahar, M. Pawan Kumar

IEEE Trans. Pattern Anal. Mach. Intell., 2015

Parsimonious Labeling.

Puneet Kumar Dokania, M. Pawan Kumar

Proceedings of the 2015 IEEE International Conference on Computer Vision, 2015

Entropy-Based Latent Structured Output Prediction.

Diane Bouchacourt, Sebastian Nowozin, M. Pawan Kumar

Proceedings of the 2015 IEEE International Conference on Computer Vision, 2015

2014

Efficient Optimization for Average Precision SVM.

Pritish Mohapatra, C. V. Jawahar, M. Pawan Kumar

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Rounding-based Moves for Metric Labeling.

M. Pawan Kumar

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Learning to Rank Using High-Order Information.

Puneet Kumar Dokania, Aseem Behl, C. V. Jawahar, M. Pawan Kumar

Proceedings of the Computer Vision - ECCV 2014, 2014

Optimizing Average Precision Using Weakly Supervised Data.

Aseem Behl, C. V. Jawahar, M. Pawan Kumar

Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014

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

Discriminative Parameter Estimation for Random Walks Segmentation.

Pierre-Yves Baudin, Danny Goodman, Puneet Kumar, Noura Azzabou, Pierre G. Carlier, Nikos Paragios, M. Pawan Kumar

Proceedings of the Medical Image Computing and Computer-Assisted Intervention - MICCAI 2013, 2013

Learning from M/EEG Data with Variable Brain Activation Delays.

Wojciech Zaremba, M. Pawan Kumar, Alexandre Gramfort, Matthew B. Blaschko

Proceedings of the Information Processing in Medical Imaging, 2013

Weakly Supervised Learning for Structured Output Prediction.

M. Pawan Kumar

, 2013

2012

Max-Margin Min-Entropy Models.

Kevin Miller, M. Pawan Kumar, Benjamin Packer, Danny Goodman, Daphne Koller

Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

A Buffer-Sizing Algorithm for Network-on-Chips with Multiple Voltage-Frequency Islands.

Anish S. Kumar, M. Pawan Kumar, Srinivasan Murali, V. Kamakoti, Luca Benini, Giovanni De Micheli

J. Electr. Comput. Eng., 2012

Modeling Latent Variable Uncertainty for Loss-based Learning.

M. Pawan Kumar, Benjamin Packer, Daphne Koller

Proceedings of the 29th International Conference on Machine Learning, 2012

2011

Improved Moves for Truncated Convex Models.

M. Pawan Kumar, Olga Veksler, Philip H. S. Torr

J. Mach. Learn. Res., 2011

A Simulation Based Buffer Sizing Algorithm for Network on Chips.

Anish S. Kumar, M. Pawan Kumar, Srinivasan Murali, V. Kamakoti, Luca Benini, Giovanni De Micheli

Proceedings of the IEEE Computer Society Annual Symposium on VLSI, 2011

A Method for Integrating Network-on-Chip Topologies with 3D ICs.

M. Pawan Kumar, Anish S. Kumar, Srinivasan Murali, Luca Benini, Kamakoti Veezhinathan

Proceedings of the IEEE Computer Society Annual Symposium on VLSI, 2011

Learning specific-class segmentation from diverse data.

M. Pawan Kumar, Haithem Turki, Dan Preston, Daphne Koller

Proceedings of the IEEE International Conference on Computer Vision, 2011

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

Self-Paced Learning for Latent Variable Models.

M. Pawan Kumar, Benjamin Packer, Daphne Koller

Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

Efficiently selecting regions for scene understanding.

M. Pawan Kumar, Daphne Koller

Proceedings of the Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition, 2010

Energy minimization for linear envelope MRFs.

Pushmeet Kohli, M. Pawan Kumar

Proceedings of the Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition, 2010

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

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

MAP Estimation of Semi-Metric MRFs via Hierarchical Graph Cuts.

M. Pawan Kumar, Daphne Koller

Proceedings of the UAI 2009, 2009

Learning a Small Mixture of Trees.

M. Pawan Kumar, Daphne Koller

Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

Efficient discriminative learning of parts-based models.

M. Pawan Kumar, Andrew Zisserman, Philip H. S. Torr

Proceedings of the IEEE 12th International Conference on Computer Vision, ICCV 2009, Kyoto, Japan, September 27, 2009

2008

Learning Layered Motion Segmentations of Video.

M. Pawan Kumar, Philip H. S. Torr, Andrew Zisserman

Int. J. Comput. Vis., 2008

Improved Moves for Truncated Convex Models.

M. Pawan Kumar, Philip H. S. Torr

Proceedings of the Advances in Neural Information Processing Systems 21, 2008

Efficiently solving convex relaxations for MAP estimation.

M. Pawan Kumar, Philip H. S. Torr

Proceedings of the Machine Learning, 2008

2007

An Invariant Large Margin Nearest Neighbour Classifier.

M. Pawan Kumar, Philip H. S. Torr, Andrew Zisserman

Proceedings of the IEEE 11th International Conference on Computer Vision, 2007

P3 & Beyond: Solving Energies with Higher Order Cliques.

Pushmeet Kohli, M. Pawan Kumar, Philip H. S. Torr

Proceedings of the 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2007), 2007

2006

Learning Class-Specific Edges for Object Detection and Segmentation.

Mukta Prasad, Andrew Zisserman, Andrew W. Fitzgibbon, M. Pawan Kumar, Philip H. S. Torr

Proceedings of the Computer Vision, Graphics and Image Processing, 5th Indian Conference, 2006

Fast Memory-Efficient Generalized Belief Propagation.

M. Pawan Kumar, Philip H. S. Torr

Proceedings of the Computer Vision, 2006

Solving Markov Random Fields using Second Order Cone Programming Relaxations.

M. Pawan Kumar, Philip H. S. Torr, Andrew Zisserman

Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2006), 2006

An Object Category Specific mrffor Segmentation.

M. Pawan Kumar, Philip H. S. Torr, Andrew Zisserman

Proceedings of the Toward Category-Level Object Recognition, 2006

2005

Learning Layered Motion Segmentation of Video.

M. Pawan Kumar, Philip H. S. Torr, Andrew Zisserman

Proceedings of the 10th IEEE International Conference on Computer Vision (ICCV 2005), 2005

OBJ CUT.

M. Pawan Kumar, Philip H. S. Torr, Andrew Zisserman

Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), 2005

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

Learning Layered Pictorial Structures from Video.

M. Pawan Kumar, Philip H. S. Torr, Andrew Zisserman

Proceedings of the ICVGIP 2004, 2004

Building blocks for autonomous navigation using contour correspondences.

M. Pawan Kumar, C. V. Jawahar, P. J. Narayanan

Proceedings of the 2004 International Conference on Image Processing, 2004

Extending Pictorial Structures for Object Recognition.

M. Pawan Kumar, Philip H. S. Torr, Andrew Zisserman

Proceedings of the British Machine Vision Conference, 2004

2002

Polygonal Approximation of Closed Curves across Multiple Views.

M. Pawan Kumar, Saurabh Goyal, C. V. Jawahar, P. J. Narayanan

Proceedings of the ICVGIP 2002, 2002