Structural and visual similarity learning for Web page archiving. - Marc T. Law; Carlos Sureda Gutierrez; Nicolas Thome; Stéphane Gançarski (CBMI, 2012)

Structural and visual comparisons for web page archiving. - Marc Teva Law; Nicolas Thome; Stéphane Gançarski; Matthieu Cord (ACM Symposium on Document Engineering, 2012)

Hybrid Pooling Fusion in the BoW Pipeline. - Marc T. Law; Nicolas Thome; Matthieu Cord (ECCV Workshops (3), 2012)

Quadruplet-Wise Image Similarity Learning. - Marc T. Law; Nicolas Thome; Matthieu Cord (ICCV, 2013)

Fantope Regularization in Metric Learning. - Marc T. Law; Nicolas Thome; Matthieu Cord (CVPR, 2014)

Bag-of-Words Image Representation: Key Ideas and Further Insight. - Marc T. Law; Nicolas Thome; Matthieu Cord (Fusion in Computer Vision, 2014)

Apprentissage de métrique appliqué à la détection de changement de page Web et aux attributs relatifs. - Marc Teva Law; Nicolas Thome; Stéphane Gançarski; Matthieu Cord (CORIA, 2015)

Distance metric learning for image and webpage comparison. (Apprentissage de distance pour la comparaison d'images et de pages Web). - Marc Teva Law (2015)

Closed-Form Training of Mahalanobis Distance for Supervised Clustering. - Marc T. Law; Yaoliang Yu; Matthieu Cord; Eric P. Xing (CVPR, 2016)

Efficient Multiple Instance Metric Learning Using Weakly Supervised Data. - Marc T. Law; Yaoliang Yu; Raquel Urtasun; Richard S. Zemel; Eric P. Xing (CVPR, 2017)

Deep Spectral Clustering Learning. - Marc T. Law; Raquel Urtasun; Richard S. Zemel (ICML, 2017)

Learning a Distance Metric from Relative Comparisons between Quadruplets of Images. - Marc T. Law; Nicolas Thome; Matthieu Cord (Int. J. Comput. Vis., 2017)

Representing Relative Visual Attributes with a Reference-Point-Based Decision Model. - Marc T. Law; Paul Weng (ICPR, 2018)

Centroid-based Deep Metric Learning for Speaker Recognition. - Jixuan Wang; Kuan-Chieh Wang; Marc T. Law; Frank Rudzicz; Michael Brudno (ICASSP, 2019)

Video Face Clustering With Unknown Number of Clusters. - Makarand Tapaswi; Marc T. Law; Sanja Fidler (ICCV, 2019)

Dimensionality Reduction for Representing the Knowledge of Probabilistic Models. - Marc T. Law; Jake Snell; Amir-massoud Farahmand; Raquel Urtasun; Richard S. Zemel (ICLR (Poster), 2019)

Lorentzian Distance Learning for Hyperbolic Representations. - Marc Teva Law; Renjie Liao; Jake Snell; Richard S. Zemel (ICML, 2019)

A Theoretical Analysis of the Number of Shots in Few-Shot Learning. - Tianshi Cao; Marc T. Law; Sanja Fidler (ICLR, 2020)

Ultrahyperbolic Representation Learning. - Marc T. Law; Jos Stam (NeurIPS, 2020)

Sim2SG: Sim-to-Real Scene Graph Generation for Transfer Learning. - Aayush Prakash; Shoubhik Debnath; Jean-Francois Lafleche; Eric Cameracci; Gavriel State; Marc T. Law (CoRR, 2020)

Self-Supervised Real-to-Sim Scene Generation. - Aayush Prakash; Shoubhik Debnath; Jean-Francois Lafleche; Eric Cameracci; Gavriel State; Stan Birchfield; Marc T. Law (ICCV, 2021)

f-Domain Adversarial Learning: Theory and Algorithms. - David Acuna; Guojun Zhang; Marc T. Law; Sanja Fidler (ICML, 2021)

Feature generation for long-tail classification. - Rahul Vigneswaran; Marc T. Law; Vineeth N. Balasubramanian; Makarand Tapaswi (ICVGIP, 2021)

Ultrahyperbolic Neural Networks. - Marc T. Law (NeurIPS, 2021)

How Much More Data Do I Need? Estimating Requirements for Downstream Tasks. - Rafid Mahmood; James Lucas; David Acuna; Daiqing Li; Jonah Philion; José M. Álvarez 004; Zhiding Yu; Sanja Fidler; Marc T. Law (CVPR, 2022)

Domain Adversarial Training: A Game Perspective. - David Acuna; Marc T. Law; Guojun Zhang; Sanja Fidler (ICLR, 2022)

Low-Budget Active Learning via Wasserstein Distance: An Integer Programming Approach. - Rafid Mahmood; Sanja Fidler; Marc T. Law (ICLR, 2022)

Optimizing Data Collection for Machine Learning. - Rafid Mahmood; James Lucas; José M. Álvarez 004; Sanja Fidler; Marc T. Law (NeurIPS, 2022)

Spacetime Representation Learning. - Marc T. Law; James Lucas (ICLR, 2023)

Bridging the Sim2Real gap with CARE: Supervised Detection Adaptation with Conditional Alignment and Reweighting. - Viraj Prabhu; David Acuna; Andrew Liao; Rafid Mahmood; Marc T. Law; Judy Hoffman; Sanja Fidler; James Lucas (CoRR, 2023)

Bridging the Sim2Real gap with CARE: Supervised Detection Adaptation with Conditional Alignment and Reweighting. - Viraj Prabhu; David Acuna; Rafid Mahmood; Marc T. Law; Yuan-Hong Liao; Judy Hoffman; Sanja Fidler; James Lucas (Trans. Mach. Learn. Res., 2023)

Graph Metanetworks for Processing Diverse Neural Architectures. - Derek Lim; Haggai Maron; Marc T. Law; Jonathan Lorraine; James Lucas (ICLR, 2024)

SpaceMesh: A Continuous Representation for Learning Manifold Surface Meshes. - Tianchang Shen; Zhaoshuo Li; Marc T. Law; Matan Atzmon; Sanja Fidler; James Lucas; Jun Gao 004; Nicholas Sharp (SIGGRAPH Asia, 2024)

Breaking the Curse of Dimensionality with Distributed Neural Computation. - Haitz Sáez de Ocáriz Borde; Takashi Furuya; Anastasis Kratsios; Marc T. Law (CoRR, 2024)

Uncertainty Estimation for 3D Object Detection via Evidential Learning. - Nikita Durasov; Rafid Mahmood; Jiwoong Choi; Marc T. Law; James Lucas; Pascal Fua; José M. Álvarez 004 (CoRR, 2024)

Neural Spacetimes for DAG Representation Learning. - Haitz Sáez de Ocáriz Borde; Anastasis Kratsios; Marc T. Law; Xiaowen Dong 001; Michael M. Bronstein (ICLR, 2025)

Approximation Rates and VC-Dimension Bounds for (P)ReLU MLP Mixture of Experts. - Anastasis Kratsios; Haitz Sáez de Ocáriz Borde; Takashi Furuya; Marc T. Law (Trans. Mach. Learn. Res., 2025)

Directed Graph Generation with Heat Kernels. - Marc T. Law; Karsten Kreis; Haggai Maron (Trans. Mach. Learn. Res., 2025)

AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training. - Feiyang Kang; Nadine Chang; Maying Shen; Marc T. Law; Rafid Mahmood; Ruoxi Jia 001; José M. Álvarez 004 (CoRR, 2025)