M. Pawan Kumar

According to our database1, M. Pawan Kumar authored at least 100 papers between 2002 and 2024.

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Bibliography

2024
Mathematical discoveries from program search with large language models.
Nat., January, 2024

Scaling the Convex Barrier with Sparse Dual Algorithms.
J. Mach. Learn. Res., 2024

Mimicking User Data: On Mitigating Fine-Tuning Risks in Closed Large Language Models.
CoRR, 2024

Verified Neural Compressed Sensing.
CoRR, 2024

Efficient Error Certification for Physics-Informed Neural Networks.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Expressive Losses for Verified Robustness via Convex Combinations.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023
Faithful Knowledge Distillation.
CoRR, 2023

Provably Correct Physics-Informed Neural Networks.
CoRR, 2023

Neural Lower Bounds for Verification.
Proceedings of the 2023 IEEE Conference on Secure and Trustworthy Machine Learning, 2023

2022
Faking Interpolation Until You Make It.
Trans. Mach. Learn. Res., 2022

Lookback for Learning to Branch.
Trans. Mach. Learn. Res., 2022

ANCER: Anisotropic Certification via Sample-wise Volume Maximization.
Trans. Mach. Learn. Res., 2022

A Stochastic Bundle Method for Interpolation.
J. Mach. Learn. Res., 2022

IBP Regularization for Verified Adversarial Robustness via Branch-and-Bound.
CoRR, 2022

A Stochastic Bundle Method for Interpolating Networks.
CoRR, 2022

In Defense of the Unitary Scalarization for Deep Multi-Task Learning.
CoRR, 2022

Learning to be adversarially robust and differentially private.
CoRR, 2022

2021
Improving Local Effectiveness for Global robust training.
CoRR, 2021

Neural Network Branch-and-Bound for Neural Network Verification.
CoRR, 2021

Comment on Stochastic Polyak Step-Size: Performance of ALI-G.
CoRR, 2021

Improved Branch and Bound for Neural Network Verification via Lagrangian Decomposition.
CoRR, 2021

Verifying Probabilistic Specifications with Functional Lagrangians.
CoRR, 2021

Generating adversarial examples with graph neural networks.
Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

Make Sure You're Unsure: A Framework for Verifying Probabilistic Specifications.
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.
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.
Proceedings of the 9th International Conference on Learning Representations, 2021

2020
Branch and Bound for Piecewise Linear Neural Network Verification.
J. Mach. Learn. Res., 2020

Hybrid Models for Learning to Branch.
CoRR, 2020

Lagrangian Decomposition for Neural Network Verification.
Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

Training Neural Networks for and by Interpolation.
Proceedings of the 37th International Conference on Machine Learning, 2020

Neural Network Branching for Neural Network Verification.
Proceedings of the 8th International Conference on Learning Representations, 2020

Weakly Supervised Instance Segmentation by Learning Annotation Consistent Instances.
Proceedings of the Computer Vision - ECCV 2020, 2020

2019
Efficient Relaxations for Dense CRFs with Sparse Higher-Order Potentials.
SIAM J. Imaging Sci., 2019

Linear programming-based submodular extensions for marginal estimation.
Comput. Vis. Image Underst., 2019

A Statistical Approach to Assessing Neural Network Robustness.
Proceedings of the 7th International Conference on Learning Representations, 2019

Deep Frank-Wolfe For Neural Network Optimization.
Proceedings of the 7th International Conference on Learning Representations, 2019

New Convex Relaxations for MRF Inference With Unknown Graphs.
Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, 2019

Dissimilarity Coefficient Based Weakly Supervised Object Detection.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

2018
Worst-case Optimal Submodular Extensions for Marginal Estimation.
CoRR, 2018

Smooth Loss Functions for Deep Top-k Classification.
Proceedings of the 6th International Conference on Learning Representations, 2018

Efficient Optimization for Rank-Based Loss Functions.
Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, 2018

Learning Human Poses from Actions.
Proceedings of the British Machine Vision Conference 2018, 2018

Optimal Submodular Extensions for Marginal Estimation.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

Learning to Round for Discrete Labeling Problems.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017
Piecewise Linear Neural Network verification: A comparative study.
CoRR, 2017

Learning to superoptimize programs.
Proceedings of the 5th International Conference on Learning Representations, 2017

Trusting SVM for Piecewise Linear CNNs.
Proceedings of the 5th International Conference on Learning Representations, 2017

Truncated Max-of-Convex Models.
Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017

Efficient Linear Programming for Dense CRFs.
Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017

2016
Rounding-based Moves for Semi-Metric Labeling.
J. Mach. Learn. Res., 2016

(Hyper)-Graphs Inference through Convex Relaxations and Move Making Algorithms: Contributions and Applications in Artificial Vision.
Found. Trends Comput. Graph. Vis., 2016

Learning to superoptimize programs - Workshop Version.
CoRR, 2016

DISCO Nets: DISsimilarity COefficient Networks.
CoRR, 2016

Partial Linearization Based Optimization for Multi-class SVM.
Proceedings of the Computer Vision - ECCV 2016, 2016

Efficient Continuous Relaxations for Dense CRF.
Proceedings of the Computer Vision - ECCV 2016, 2016

Coplanar Repeats by Energy Minimization.
Proceedings of the British Machine Vision Conference 2016, 2016

2015
Parameter Estimation and Energy Minimization for Region-Based Semantic Segmentation.
IEEE Trans. Pattern Anal. Mach. Intell., 2015

Optimizing Average Precision Using Weakly Supervised Data.
IEEE Trans. Pattern Anal. Mach. Intell., 2015

Parsimonious Labeling.
Proceedings of the 2015 IEEE International Conference on Computer Vision, 2015

Entropy-Based Latent Structured Output Prediction.
Proceedings of the 2015 IEEE International Conference on Computer Vision, 2015

2014
Efficient Optimization for Average Precision SVM.
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.
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.
Proceedings of the Computer Vision - ECCV 2014, 2014

Optimizing Average Precision Using Weakly Supervised Data.
Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014

2013
Discriminative Parameter Estimation for Random Walks Segmentation: Technical Report.
CoRR, 2013

Discriminative Parameter Estimation for Random Walks Segmentation.
Proceedings of the Medical Image Computing and Computer-Assisted Intervention - MICCAI 2013, 2013

Learning from M/EEG Data with Variable Brain Activation Delays.
Proceedings of the Information Processing in Medical Imaging, 2013

Weakly Supervised Learning for Structured Output Prediction.
, 2013

2012
Max-Margin Min-Entropy Models.
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.
J. Electr. Comput. Eng., 2012

Modeling Latent Variable Uncertainty for Loss-based Learning.
Proceedings of the 29th International Conference on Machine Learning, 2012

2011
Improved Moves for Truncated Convex Models.
J. Mach. Learn. Res., 2011

A Simulation Based Buffer Sizing Algorithm for Network on Chips.
Proceedings of the IEEE Computer Society Annual Symposium on VLSI, 2011

A Method for Integrating Network-on-Chip Topologies with 3D ICs.
Proceedings of the IEEE Computer Society Annual Symposium on VLSI, 2011

Learning specific-class segmentation from diverse data.
Proceedings of the IEEE International Conference on Computer Vision, 2011

2010
OBJCUT: Efficient Segmentation Using Top-Down and Bottom-Up Cues.
IEEE Trans. Pattern Anal. Mach. Intell., 2010

Self-Paced Learning for Latent Variable Models.
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.
Proceedings of the Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition, 2010

Energy minimization for linear envelope MRFs.
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.
IEEE Trans. Pattern Anal. Mach. Intell., 2009

An Analysis of Convex Relaxations for MAP Estimation of Discrete MRFs.
J. Mach. Learn. Res., 2009

MAP Estimation of Semi-Metric MRFs via Hierarchical Graph Cuts.
Proceedings of the UAI 2009, 2009

Learning a Small Mixture of Trees.
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.
Proceedings of the IEEE 12th International Conference on Computer Vision, ICCV 2009, Kyoto, Japan, September 27, 2009

2008
Learning Layered Motion Segmentations of Video.
Int. J. Comput. Vis., 2008

Improved Moves for Truncated Convex Models.
Proceedings of the Advances in Neural Information Processing Systems 21, 2008

Efficiently solving convex relaxations for MAP estimation.
Proceedings of the Machine Learning, 2008

2007
An Invariant Large Margin Nearest Neighbour Classifier.
Proceedings of the IEEE 11th International Conference on Computer Vision, 2007

P3 & Beyond: Solving Energies with Higher Order Cliques.
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.
Proceedings of the Computer Vision, Graphics and Image Processing, 5th Indian Conference, 2006

Fast Memory-Efficient Generalized Belief Propagation.
Proceedings of the Computer Vision, 2006

Solving Markov Random Fields using Second Order Cone Programming Relaxations.
Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2006), 2006

An Object Category Specific mrffor Segmentation.
Proceedings of the Toward Category-Level Object Recognition, 2006

2005
Learning Layered Motion Segmentation of Video.
Proceedings of the 10th IEEE International Conference on Computer Vision (ICCV 2005), 2005

OBJ CUT.
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.
Image Vis. Comput., 2004

Learning Layered Pictorial Structures from Video.
Proceedings of the ICVGIP 2004, 2004

Building blocks for autonomous navigation using contour correspondences.
Proceedings of the 2004 International Conference on Image Processing, 2004

Extending Pictorial Structures for Object Recognition.
Proceedings of the British Machine Vision Conference, 2004

2002
Polygonal Approximation of Closed Curves across Multiple Views.
Proceedings of the ICVGIP 2002, 2002


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