Hierarchical Modeling and Shrinkage for User Session LengthPrediction in Media Streaming. - Antoine Dedieu; Rahul Mazumder; Zhen Zhu; Hossein Vahabi (CIKM, 2018)

Hierarchical Modeling and Shrinkage for User Session Length Prediction in Media Streaming. - Antoine Dedieu; Rahul Mazumder; Zhen Zhu; Hossein Vahabi (CoRR, 2018)

Error bounds for sparse classifiers in high-dimensions. - Antoine Dedieu (AISTATS, 2019)

Solving large-scale L1-regularized SVMs and cousins: the surprising effectiveness of column and constraint generation. - Antoine Dedieu; Rahul Mazumder (CoRR, 2019)

Learning higher-order sequential structure with cloned HMMs. - Antoine Dedieu; Nishad Gothoskar; Scott Swingle; Wolfgang Lehrach; Miguel Lázaro-Gredilla; Dileep George (CoRR, 2019)

Sparse (group) learning with Lipschitz loss functions: a unified analysis. - Antoine Dedieu (CoRR, 2019)

An error bound for Lasso and Group Lasso in high dimensions. - Antoine Dedieu (CoRR, 2019)

Query Training: Learning and inference for directed and undirected graphical models. - Miguel Lázaro-Gredilla; Wolfgang Lehrach; Nishad Gothoskar; Guangyao Zhou; Antoine Dedieu; Dileep George (CoRR, 2020)

Sample-Efficient L0-L2 Constrained Structure Learning of Sparse Ising Models. - Antoine Dedieu; Miguel Lázaro-Gredilla; Dileep George (AAAI, 2021)

Query Training: Learning a Worse Model to Infer Better Marginals in Undirected Graphical Models with Hidden Variables. - Miguel Lázaro-Gredilla; Wolfgang Lehrach; Nishad Gothoskar; Guangyao Zhou; Antoine Dedieu; Dileep George (AAAI, 2021)

Perturb-and-max-product: Sampling and learning in discrete energy-based models. - Miguel Lázaro-Gredilla; Antoine Dedieu; Dileep George (NeurIPS, 2021)

Graphical Models with Attention for Context-Specific Independence and an Application to Perceptual Grouping. - Guangyao Zhou; Wolfgang Lehrach; Antoine Dedieu; Miguel Lázaro-Gredilla; Dileep George (CoRR, 2021)

Learning Sparse Classifiers: Continuous and Mixed Integer Optimization Perspectives. - Antoine Dedieu; Hussein Hazimeh 001; Rahul Mazumder (J. Mach. Learn. Res., 2021)

Solving L1-regularized SVMs and Related Linear Programs: Revisiting the Effectiveness of Column and Constraint Generation. - Antoine Dedieu; Rahul Mazumder; Haoyue Wang (J. Mach. Learn. Res., 2022)

Learning Noisy OR Bayesian Networks with Max-Product Belief Propagation. - Antoine Dedieu; Guangyao Zhou; Dileep George; Miguel Lázaro-Gredilla (ICML, 2023)

Schema-learning and rebinding as mechanisms of in-context learning and emergence. - Sivaramakrishnan Swaminathan; Antoine Dedieu; Rajkumar Vasudeva Raju; Murray Shanahan; Miguel Lázaro-Gredilla; Dileep George (NeurIPS, 2023)

Subset Selection with Shrinkage: Sparse Linear Modeling When the SNR Is Low. - Rahul Mazumder; Peter Radchenko; Antoine Dedieu (Oper. Res., 2023)

Learning Cognitive Maps from Transformer Representations for Efficient Planning in Partially Observed Environments. - Antoine Dedieu; Wolfgang Lehrach; Guangyao Zhou; Dileep George; Miguel Lázaro-Gredilla (ICML, 2024)

DMC-VB: A Benchmark for Representation Learning for Control with Visual Distractors. - Joseph Ortiz; Antoine Dedieu; Wolfgang Lehrach; J. Swaroop Guntupalli; Carter Wendelken; Ahmad Humayun; Sivaramakrishnan Swaminathan; Guangyao Zhou; Miguel Lázaro-Gredilla; Kevin P. Murphy (NeurIPS, 2024)

Diffusion Model Predictive Control. - Guangyao Zhou; Sivaramakrishnan Swaminathan; Rajkumar Vasudeva Raju; J. Swaroop Guntupalli; Wolfgang Lehrach; Joseph Ortiz; Antoine Dedieu; Miguel Lázaro-Gredilla; Kevin Patrick Murphy (Trans. Mach. Learn. Res., 2025)

Improving Transformer World Models for Data-Efficient RL. - Antoine Dedieu; Joseph Ortiz; Xinghua Lou; Carter Wendelken; Wolfgang Lehrach; J. Swaroop Guntupalli; Miguel Lázaro-Gredilla; Kevin Patrick Murphy (CoRR, 2025)