M. Pawan Kumar

Publication Activity (10 Years)

Years Active: 2002-2024
Publications (10 Years): 76

Top Venues

CoRR

ICLR (Poster)

CVPR

ICCV

Publications

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
Mathematical discoveries from program search with large language models. Nat. 625 (7995) (2024)
Alessandro De Palma, Rudy Bunel, Krishnamurthy (Dj) Dvijotham, M. Pawan Kumar, Robert Stanforth, Alessio Lomuscio
Expressive Losses for Verified Robustness via Convex Combinations. ICLR (2024)
Rudy Bunel, Krishnamurthy Dvijotham, M. Pawan Kumar, Alessandro De Palma, Robert Stanforth
Verified Neural Compressed Sensing. CoRR (2024)
Francisco Eiras, Aleksandar Petrov, Philip H. S. Torr, M. Pawan Kumar, Adel Bibi
Mimicking User Data: On Mitigating Fine-Tuning Risks in Closed Large Language Models. CoRR (2024)
Florian Jaeckle, M. Pawan Kumar
Neural Lower Bounds for Verification. SaTML (2023)
Francisco Eiras, Adel Bibi, Rudy Bunel, Krishnamurthy (Dj) Dvijotham, Philip H. S. Torr, M. Pawan Kumar
Provably Correct Physics-Informed Neural Networks. CoRR (2023)
Tom A. Lamb, Rudy Bunel, Krishnamurthy (Dj) Dvijotham, M. Pawan Kumar, Philip H. S. Torr, Francisco Eiras
Faithful Knowledge Distillation. CoRR (2023)
Alessandro De Palma, Rudy Bunel, Krishnamurthy Dvijotham, M. Pawan Kumar, Robert Stanforth, Alessio Lomuscio
Expressive Losses for Verified Robustness via Convex Combinations. CoRR (2023)
Alasdair Paren, Leonard Berrada, Rudra P. K. Poudel, M. Pawan Kumar
A Stochastic Bundle Method for Interpolation. J. Mach. Learn. Res. 23 (2022)
Prateek Gupta, Elias B. Khalil, Didier Chételat, Maxime Gasse, Yoshua Bengio, Andrea Lodi, M. Pawan Kumar
Lookback for Learning to Branch. CoRR (2022)
Vitaly Kurin, Alessandro De Palma, Ilya Kostrikov, Shimon Whiteson, M. Pawan Kumar
In Defense of the Unitary Scalarization for Deep Multi-Task Learning. CoRR (2022)
Jamie Hayes, Borja Balle, M. Pawan Kumar
Learning to be adversarially robust and differentially private. CoRR (2022)
Alasdair Paren, Leonard Berrada, Rudra P. K. Poudel, M. Pawan Kumar
A Stochastic Bundle Method for Interpolating Networks. CoRR (2022)
Prateek Gupta, Elias Boutros Khalil, Didier Chételat, Maxime Gasse, Andrea Lodi, Yoshua Bengio, M. Pawan Kumar
Lookback for Learning to Branch. Trans. Mach. Learn. Res. 2022 (2022)
Francisco Eiras, Motasem Alfarra, Philip H. S. Torr, M. Pawan Kumar, Puneet K. Dokania, Bernard Ghanem, Adel Bibi
ANCER: Anisotropic Certification via Sample-wise Volume Maximization. Trans. Mach. Learn. Res. 2022 (2022)
Alessandro De Palma, Rudy Bunel, Krishnamurthy Dvijotham, M. Pawan Kumar, Robert Stanforth
IBP Regularization for Verified Adversarial Robustness via Branch-and-Bound. CoRR (2022)
Alasdair Paren, Rudra P. K. Poudel, M. Pawan Kumar
Faking Interpolation Until You Make It. Trans. Mach. Learn. Res. 2022 (2022)
Florian Jaeckle, M. Pawan Kumar
Generating adversarial examples with graph neural networks. UAI (2021)
Alessandro De Palma, Harkirat S. Behl, Rudy R. Bunel, Philip H. S. Torr, M. Pawan Kumar
Scaling the Convex Barrier with Active Sets. ICLR (2021)
Florian Jaeckle, M. Pawan Kumar
Generating Adversarial Examples with Graph Neural Networks. CoRR (2021)
Alessandro De Palma, Rudy Bunel, Alban Desmaison, Krishnamurthy Dvijotham, Pushmeet Kohli, Philip H. S. Torr, M. Pawan Kumar
Improved Branch and Bound for Neural Network Verification via Lagrangian Decomposition. CoRR (2021)
Leonard Berrada, Sumanth Dathathri, Krishnamurthy Dvijotham, Robert Stanforth, Rudy Bunel, Jonathan Uesato, Sven Gowal, M. Pawan Kumar
Make Sure You're Unsure: A Framework for Verifying Probabilistic Specifications. NeurIPS (2021)
Florian Jaeckle, Jingyue Lu, M. Pawan Kumar
Neural Network Branch-and-Bound for Neural Network Verification. CoRR (2021)
Leonard Berrada, Andrew Zisserman, M. Pawan Kumar
Comment on Stochastic Polyak Step-Size: Performance of ALI-G. CoRR (2021)
Harkirat Singh Behl, M. Pawan Kumar, Philip H. S. Torr, Krishnamurthy Dvijotham
Overcoming the Convex Barrier for Simplex Inputs. NeurIPS (2021)
Leonard Berrada, Sumanth Dathathri, Krishnamurthy Dvijotham, Robert Stanforth, Rudy Bunel, Jonathan Uesato, Sven Gowal, M. Pawan Kumar
Verifying Probabilistic Specifications with Functional Lagrangians. CoRR (2021)
Francisco Eiras, Motasem Alfarra, M. Pawan Kumar, Philip H. S. Torr, Puneet K. Dokania, Bernard Ghanem, Adel Bibi
ANCER: Anisotropic Certification via Sample-wise Volume Maximization. CoRR (2021)
Alessandro De Palma, Harkirat Singh Behl, Rudy Bunel, Philip H. S. Torr, M. Pawan Kumar
Scaling the Convex Barrier with Sparse Dual Algorithms. CoRR (2021)
Jingyue Lu, M. Pawan Kumar
Improving Local Effectiveness for Global robust training. CoRR (2021)
Aditya Arun, C. V. Jawahar, M. Pawan Kumar
Weakly Supervised Instance Segmentation by Learning Annotation Consistent Instances. ECCV (28) (2020)
Rudy Bunel, Alessandro De Palma, Alban Desmaison, Krishnamurthy Dvijotham, Pushmeet Kohli, Philip H. S. Torr, M. Pawan Kumar
Lagrangian Decomposition for Neural Network Verification. UAI (2020)
Leonard Berrada, Andrew Zisserman, M. Pawan Kumar
Training Neural Networks for and by Interpolation. ICML (2020)
Rudy Bunel, Jingyue Lu, Ilker Turkaslan, Philip H. S. Torr, Pushmeet Kohli, M. Pawan Kumar
Branch and Bound for Piecewise Linear Neural Network Verification. J. Mach. Learn. Res. 21 (2020)
Rudy Bunel, Alessandro De Palma, Alban Desmaison, Krishnamurthy Dvijotham, Pushmeet Kohli, Philip H. S. Torr, M. Pawan Kumar
Lagrangian Decomposition for Neural Network Verification. CoRR (2020)
Prateek Gupta, Maxime Gasse, Elias B. Khalil, M. Pawan Kumar, Andrea Lodi, Yoshua Bengio
Hybrid Models for Learning to Branch. CoRR (2020)
Aditya Arun, C. V. Jawahar, M. Pawan Kumar
Weakly Supervised Instance Segmentation by Learning Annotation Consistent Instances. CoRR (2020)
Jingyue Lu, M. Pawan Kumar
Neural Network Branching for Neural Network Verification. ICLR (2020)
Leonard Berrada, Andrew Zisserman, M. Pawan Kumar
Deep Frank-Wolfe For Neural Network Optimization. ICLR (Poster) (2019)
Thomas Joy, Alban Desmaison, Thalaiyasingam Ajanthan, Rudy Bunel, Mathieu Salzmann, Pushmeet Kohli, Philip H. S. Torr, M. Pawan Kumar
Efficient Relaxations for Dense CRFs with Sparse Higher-Order Potentials. SIAM J. Imaging Sci. 12 (1) (2019)
Stefan Webb, Tom Rainforth, Yee Whye Teh, M. Pawan Kumar
A Statistical Approach to Assessing Neural Network Robustness. ICLR (Poster) (2019)
Zhenhua Wang, Tong Liu, Qinfeng Shi, M. Pawan Kumar, Jianhua Zhang
New Convex Relaxations for MRF Inference With Unknown Graphs. ICCV (2019)
Pankaj Pansari, Chris Russell, M. Pawan Kumar
Linear programming-based submodular extensions for marginal estimation. Comput. Vis. Image Underst. 189 (2019)
Jingyue Lu, M. Pawan Kumar
Neural Network Branching for Neural Network Verification. CoRR (2019)
Leonard Berrada, Andrew Zisserman, M. Pawan Kumar
Training Neural Networks for and by Interpolation. CoRR (2019)
Aditya Arun, C. V. Jawahar, M. Pawan Kumar
Dissimilarity Coefficient Based Weakly Supervised Object Detection. CVPR (2019)
Rudy Bunel, Jingyue Lu, Ilker Turkaslan, Philip H. S. Torr, Pushmeet Kohli, M. Pawan Kumar
Branch and Bound for Piecewise Linear Neural Network Verification. CoRR (2019)
Aditya Arun, C. V. Jawahar, M. Pawan Kumar
Dissimilarity Coefficient based Weakly Supervised Object Detection. CoRR (2018)
Pankaj Pansari, Chris Russell, M. Pawan Kumar
Worst-case Optimal Submodular Extensions for Marginal Estimation. CoRR (2018)
Aditya Arun, C. V. Jawahar, M. Pawan Kumar
Learning Human Poses from Actions. BMVC (2018)
Pritish Mohapatra, C. V. Jawahar, M. Pawan Kumar
Learning to Round for Discrete Labeling Problems. AISTATS (2018)
Aditya Arun, C. V. Jawahar, M. Pawan Kumar
Learning Human Poses from Actions. CoRR (2018)
Pankaj Pansari, Chris Russell, M. Pawan Kumar
Optimal Submodular Extensions for Marginal Estimation. AISTATS (2018)
Leonard Berrada, Andrew Zisserman, M. Pawan Kumar
Smooth Loss Functions for Deep Top-k Classification. ICLR (Poster) (2018)
Leonard Berrada, Andrew Zisserman, M. Pawan Kumar
Deep Frank-Wolfe For Neural Network Optimization. CoRR (2018)
Pritish Mohapatra, Michal Rolínek, C. V. Jawahar, Vladimir Kolmogorov, M. Pawan Kumar
Efficient Optimization for Rank-Based Loss Functions. CVPR (2018)
Thomas Joy, Alban Desmaison, Thalaiyasingam Ajanthan, Rudy Bunel, Mathieu Salzmann, Pushmeet Kohli, Philip H. S. Torr, M. Pawan Kumar
Efficient Relaxations for Dense CRFs with Sparse Higher Order Potentials. CoRR (2018)
Leonard Berrada, Andrew Zisserman, M. Pawan Kumar
Smooth Loss Functions for Deep Top-k Classification. CoRR (2018)
Stefan Webb, Tom Rainforth, Yee Whye Teh, M. Pawan Kumar
A Statistical Approach to Assessing Neural Network Robustness. CoRR (2018)
James Pritts, Denys Rozumnyi, M. Pawan Kumar, Ondrej Chum
Coplanar Repeats by Energy Minimization. CoRR (2017)
Rudy Bunel, Alban Desmaison, M. Pawan Kumar, Philip H. S. Torr, Pushmeet Kohli
Learning to superoptimize programs. ICLR (Poster) (2017)
Pankaj Pansari, M. Pawan Kumar
Truncated Max-of-Convex Models. CVPR (2017)
Leonard Berrada, Andrew Zisserman, M. Pawan Kumar
Trusting SVM for Piecewise Linear CNNs. ICLR (Poster) (2017)
Thalaiyasingam Ajanthan, Alban Desmaison, Rudy Bunel, Mathieu Salzmann, Philip H. S. Torr, M. Pawan Kumar
Efficient Linear Programming for Dense CRFs. CVPR (2017)
Rudy Bunel, Ilker Turkaslan, Philip H. S. Torr, Pushmeet Kohli, M. Pawan Kumar
Piecewise Linear Neural Network verification: A comparative study. CoRR (2017)
M. Pawan Kumar, Puneet Kumar Dokania
Rounding-based Moves for Semi-Metric Labeling. J. Mach. Learn. Res. 17 (2016)
Diane Bouchacourt, M. Pawan Kumar, Sebastian Nowozin
DISCO Nets: DISsimilarity COefficient Networks. CoRR (2016)
Pritish Mohapatra, Puneet Kumar Dokania, C. V. Jawahar, M. Pawan Kumar
Partial Linearization Based Optimization for Multi-class SVM. ECCV (5) (2016)
Leonard Berrada, Andrew Zisserman, M. Pawan Kumar
Trusting SVM for Piecewise Linear CNNs. CoRR (2016)
Alban Desmaison, Rudy Bunel, Pushmeet Kohli, Philip H. S. Torr, M. Pawan Kumar
Efficient Continuous Relaxations for Dense CRF. ECCV (2) (2016)
Pritish Mohapatra, Michal Rolinek, C. V. Jawahar, Vladimir Kolmogorov, M. Pawan Kumar
Efficient Optimization for Rank-based Loss Functions. CoRR (2016)
Rudy Bunel, Alban Desmaison, M. Pawan Kumar, Philip H. S. Torr, Pushmeet Kohli
Learning to superoptimize programs. CoRR (2016)
Nikos Komodakis, M. Pawan Kumar, Nikos Paragios
(Hyper)-Graphs Inference through Convex Relaxations and Move Making Algorithms: Contributions and Applications in Artificial Vision. Found. Trends Comput. Graph. Vis. 10 (1) (2016)
Rudy Bunel, Alban Desmaison, M. Pawan Kumar, Philip H. S. Torr, Pushmeet Kohli
Learning to superoptimize programs - Workshop Version. CoRR (2016)
Alban Desmaison, Rudy Bunel, Pushmeet Kohli, Philip H. S. Torr, M. Pawan Kumar
Efficient Continuous Relaxations for Dense CRF. CoRR (2016)
James Pritts, Denys Rozumnyi, M. Pawan Kumar, Ondrej Chum
Coplanar Repeats by Energy Minimization. BMVC (2016)
Thalaiyasingam Ajanthan, Alban Desmaison, Rudy Bunel, Mathieu Salzmann, Philip H. S. Torr, M. Pawan Kumar
Efficient Linear Programming for Dense CRFs. CoRR (2016)
Puneet Kumar Dokania, M. Pawan Kumar
Parsimonious Labeling. ICCV (2015)
M. Pawan Kumar, Haithem Turki, Dan Preston, Daphne Koller
Parameter Estimation and Energy Minimization for Region-Based Semantic Segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 37 (7) (2015)
Diane Bouchacourt, Sebastian Nowozin, M. Pawan Kumar
Entropy-Based Latent Structured Output Prediction. ICCV (2015)
Pankaj Pansari, M. Pawan Kumar
Truncated Max-of-Convex Models. CoRR (2015)
Puneet Kumar Dokania, M. Pawan Kumar
Parsimonious Labeling. CoRR (2015)
Aseem Behl, Pritish Mohapatra, C. V. Jawahar, M. Pawan Kumar
Optimizing Average Precision Using Weakly Supervised Data. IEEE Trans. Pattern Anal. Mach. Intell. 37 (12) (2015)
M. Pawan Kumar
Rounding-based Moves for Metric Labeling. NIPS (2014)
Puneet Kumar Dokania, Aseem Behl, C. V. Jawahar, M. Pawan Kumar
Learning to Rank Using High-Order Information. ECCV (4) (2014)
Aseem Behl, C. V. Jawahar, M. Pawan Kumar
Optimizing Average Precision Using Weakly Supervised Data. CVPR (2014)
Pritish Mohapatra, C. V. Jawahar, M. Pawan Kumar
Efficient Optimization for Average Precision SVM. NIPS (2014)
Pierre-Yves Baudin, Danny Goodman, Puneet Kumar, Noura Azzabou, Pierre G. Carlier, Nikos Paragios, M. Pawan Kumar
Discriminative Parameter Estimation for Random Walks Segmentation: Technical Report. CoRR (2013)
Wojciech Zaremba, M. Pawan Kumar, Alexandre Gramfort, Matthew B. Blaschko
Learning from M/EEG Data with Variable Brain Activation Delays. IPMI (2013)
Pierre-Yves Baudin, Danny Goodman, Puneet Kumar, Noura Azzabou, Pierre G. Carlier, Nikos Paragios, M. Pawan Kumar
Discriminative Parameter Estimation for Random Walks Segmentation. CoRR (2013)
Pierre-Yves Baudin, Danny Goodman, Puneet Kumar, Noura Azzabou, Pierre G. Carlier, Nikos Paragios, M. Pawan Kumar
Discriminative Parameter Estimation for Random Walks Segmentation. MICCAI (3) (2013)
M. Pawan Kumar, Benjamin Packer, Daphne Koller
Modeling Latent Variable Uncertainty for Loss-based Learning. ICML (2012)
Anish S. Kumar, M. Pawan Kumar, Srinivasan Murali, V. Kamakoti, Luca Benini, Giovanni De Micheli
A Buffer-Sizing Algorithm for Network-on-Chips with Multiple Voltage-Frequency Islands. J. Electr. Comput. Eng. 2012 (2012)
M. Pawan Kumar, Benjamin Packer, Daphne Koller
Modeling Latent Variable Uncertainty for Loss-based Learning CoRR (2012)
M. Pawan Kumar, Daphne Koller
MAP Estimation of Semi-Metric MRFs via Hierarchical Graph Cuts CoRR (2012)
Kevin Miller, M. Pawan Kumar, Benjamin Packer, Danny Goodman, Daphne Koller
Max-Margin Min-Entropy Models. AISTATS (2012)
M. Pawan Kumar, Olga Veksler, Philip H. S. Torr
Improved Moves for Truncated Convex Models. J. Mach. Learn. Res. 12 (2011)
M. Pawan Kumar, Haithem Turki, Dan Preston, Daphne Koller
Learning specific-class segmentation from diverse data. ICCV (2011)
M. Pawan Kumar, Anish S. Kumar, Srinivasan Murali, Luca Benini, Kamakoti Veezhinathan
A Method for Integrating Network-on-Chip Topologies with 3D ICs. ISVLSI (2011)
Anish S. Kumar, M. Pawan Kumar, Srinivasan Murali, V. Kamakoti, Luca Benini, Giovanni De Micheli
A Simulation Based Buffer Sizing Algorithm for Network on Chips. ISVLSI (2011)
M. Pawan Kumar, Daphne Koller
Efficiently selecting regions for scene understanding. CVPR (2010)

M. Pawan Kumar.css-eqlu8i{font-size:small;padding-left:4px;}

Publication Activity (10 Years)

Top Topics

Top Venues

M. Pawan Kumar