Antoine Dedieu

About me

I am a Research Scientist at Google DeepMind. My current research interests lie in solving challenging (a) object-centric vision, (b) planning, and (c) navigation problems, which current AI models struggle with. Over the past years, I have mainly addressed these tasks by building novel generative probabilistic graphical models (PGMs). To achieve this, I have proposed new methods for learning and inference in complex PGMs. More recently, I have proposed novel deep learning variants for these tasks.

Prior to joining DeepMind, I worked 3.5 years as a Research Scientist then as a Senior Research Scientist at Vicarious (acquired by Alphabet) where I was building an AI layer for robots. Prior to joining Vicarious, I graduated from MIT Operations Research Center Master of Science, where I was advised by Prof. Rahul Mazumder. My thesis lied at the intersection of Machine Learning, Optimization and Statistics, building new algorithms to compute interpretable estimators, and studying their statistical performance. Before MIT, I earned an MS in Applied Mathematics from Ecole Polytechnique.

I am a movie-fan, a sports addict, and a world-traveller. As an amateur photograph, I share my journeys around on my website.

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Journal articles

• PGMax: Factor Graphs for Discrete Probabilistic Graphical Models and Loopy Belief Propagation in JAX. [Preprint] [Code]
  Journal of Machine Learning Research (accepted with minor revisions), 2023. Guangyao Zhou, Nishanth Kumar, Antoine Dedieu, Miguel Lázaro-Gredilla, Shrinu Kushagra, Dileep George.
• Learning attention-controllable border-ownership for objectness inference and binding. [Preprint]
  Antoine Dedieu, Rajeev V. Rikhye, Miguel Lázaro-Gredilla, Dileep George.
• A detailed mathematical theory of thalamic and cortical microcircuits based on inference in a generative vision model. [Preprint]
  Dileep George, Miguel Lázaro-Gredilla, Wolfgang Lehrach, Antoine Dedieu, Guangyao Zhou.
• Solving L1-regularized SVMs and related linear programs: Revisiting the effectiveness of Column and Constraint Generation. [Preprint] [Code]
  Journal of Machine Learning Research, 2022. Antoine Dedieu, Rahul Mazumder, Haoyue Wang
• Subset Selection with Shrinkage: Sparse Linear Modeling when the SNR is low. [Preprint] [Code]
  Operations Research, 2022. Rahul Mazumder, Peter Radchenko, Antoine Dedieu.
• Learning Sparse Classifiers: Continuous and Mixed Integer Optimization Perspectives. [Preprint] [Code]
  Journal of Machine Learning Research, 2021. Antoine Dedieu, Hussein Hazimeh, Rahul Mazumder.
• Clone-structured graph representations enable flexible learning and vicarious evaluation of cognitive maps. [Preprint]
  Nature Communications, 2021. Dileep George, Rajeev V Rikhye, Nishad Gothoskar, J Swaroop Guntupalli, Antoine Dedieu, Miguel Lázaro-Gredilla.

Conference articles

• Schema-learning and rebinding as mechanisms of in-context learning and emergence. [Preprint]
  To appear at Neurips 2023. Siva Swaminathan, Antoine Dedieu, Rajkumar Vasudeva Raju, Murray Shanahan, Miguel Lázaro-Gredilla, Dileep George.
• Learning noisy-OR Bayesian Networks with Max-Product Belief Propagation. [Preprint] [Code]
  ICML 2023. Antoine Dedieu, Guangyao Zhou, Dileep George, Miguel Lázaro-Gredilla.
• Graphical Models with Attention for Context-Specific Independence and an Application to Perceptual Grouping. [Preprint] [Code]
  Guangyao Zhou, Wolfgang Lehrach, Antoine Dedieu, Miguel Lázaro-Gredilla, Dileep George.
• Perturb-and-max-product: Sampling and learning in discrete energy-based models. [Preprint] [Code]
  Neurips 2021. Miguel Lázaro-Gredilla, Antoine Dedieu, Dileep George.
• Sample-Efficient L0-L2 Constrained Structure Learning of Sparse Ising Models. [Preprint] [Code]
  AAAI 2021. Antoine Dedieu, Miguel Lázaro-Gredilla, Dileep George.
• Query Training: Learning a Worse Model to Infer Better Marginals in Undirected Graphical Models with Hidden Variables. [Preprint] [Code]
  AAAI 2021. Miguel Lázaro-Gredilla, Wolfgang Lehrach, Nishad Gothoskar, Guangyao Zhou, Antoine Dedieu, Dileep George.
• Improved error rates for sparse (group) learning with Lipschitz loss functions. [Preprint]
  Antoine Dedieu.
• An error bound for Lasso and Group Lasso in high dimensions. [Preprint]
  Antoine Dedieu.
• Learning higher-order sequential structure with cloned HMMs. [Preprint]
  Antoine Dedieu, Nishad Gothoskar, Scott Swingle, Wolfgang Lehrach, Miguel Lázaro-Gredilla, Dileep George.
• Error bounds for sparse classifiers in high-dimensions. [Preprint]
  AiStats 2019. Antoine Dedieu.
• Hierarchical Modeling and Shrinkage for User Session Length Prediction in Media Streaming. [Preprint] [Code]
  CIKM 2018. Antoine Dedieu, Rahul Mazumder, Zhen Zhu, Hossein Vahabi.

Thesis

• Sparse learning: statistical and optimization perspectives.
  MIT Libraries. Antoine Dedieu.