Wed | Jul 28 | 16:00 | Simon Du | Provable Representation Learning | |

Fri | Jul 09 | 13:00 | Usman Khan | Distributed ML: Optimal algorithms for distributed stochastic non-convex optimization | |

Fri | Jul 02 | 13:00 | Ard Louis | Deep neural networks have an inbuilt Occam's razor | |

Fri | Jun 25 | 13:00 | Yuejie Chi | Policy Optimization in Reinforcement Learning: A Tale of Preconditioning and Regularization | |

Fri | Jun 18 | 13:00 | Ruth Misener | Partition-based formulations for mixed-integer optimization of trained ReLU neural networks | |

Fri | Jun 11 | 13:00 | Ulugbek Kamilov | Computational Imaging: Reconciling Physical and Learned Models | |

Fri | Jun 04 | 13:00 | Mathieu Blondel | Efficient and Modular Implicit Differentiation | |

Fri | May 28 | 13:00 | Gustau Camps-Valls | Physics Aware Machine Learning for the Earth Sciences | |

Fri | May 21 | 13:00 | Kyriakos Vamvoudakis | Learning-Based Actuator Placement and Receding Horizon Control for Security against Actuation Attacks | |

Fri | May 07 | 13:00 | Rebecca Willett | Machine Learning and Inverse Problems: Deeper and More Robust | |

Wed | Apr 28 | 17:00 | Mikhail Belkin | Two mathematical lessons of deep learning | |

Fri | Apr 23 | 13:00 | Jan Peters | Robot Learning - Quo Vadis? | |

Wed | Apr 14 | 17:00 | Gabriel Peyré | Scaling Optimal Transport for High dimensional Learning | |

Fri | Apr 09 | 13:00 | Pedro A. Santos | Two-time scale stochastic approximation for reinforcement learning with linear function approximation | |

Wed | Mar 31 | 17:00 | Steve Brunton | Machine learning for Fluid Mechanics | |

Mon | Mar 22 | 17:00 | Markus Heyl | Quantum many-body dynamics in two dimensions with artificial neural networks | |

Wed | Mar 17 | 18:00 | Hsin Yuan Huang, (Robert) | Information-theoretic bounds on quantum advantage in machine learning | |

Wed | Mar 03 | 18:00 | A. Pedro Aguiar | Model based control design combining Lyapunov and optimization tools: Examples in the area of motion control of autonomous robotic vehicles | |

Mon | Feb 22 | 17:00 | Maciej Koch-J8anusz | Statistical physics through the lens of real-space mutual information | |

Wed | Feb 17 | 18:00 | Mário Figueiredo | Dealing with Correlated Variables in Supervised Learning | |

Wed | Feb 10 | 18:00 | Caroline Uhler | Causal Inference and Overparameterized Autoencoders in the Light of Drug Repurposing for SARS-CoV-2 | |

Wed | Feb 03 | 18:00 | Miguel Couceiro | Making ML Models fairer through explanations, feature dropout, and aggregation | |

Wed | Jan 27 | 11:00 | Xavier Bresson | Benchmarking Graph Neural Networks | |

Wed | Jan 20 | 18:00 | James Halverson | Neural Networks and Quantum Field Theory | |

Wed | Jan 13 | 18:00 | Anna C. Gilbert | Metric representations: Algorithms and Geometry | |

Wed | Jan 06 | 18:00 | Sanjeev Arora | The quest for mathematical understanding of deep learning | |

Wed | Dec 16 | 18:00 | René Vidal | From Optimization Algorithms to Dynamical Systems and Back | |

Wed | Dec 09 | 18:00 | Samantha Kleinberg | Data, Decisions, and You: Making Causality Useful and Usable in a Complex World | |

Wed | Dec 02 | 18:00 | Gitta Kutyniok | Deep Learning meets Physics: Taking the Best out of Both Worlds in Imaging Science | |

Wed | Nov 25 | 18:00 | Tommaso Dorigo | Dealing with Systematic Uncertainties in HEP Analysis with Machine Learning Methods | |

Fri | Nov 20 | 15:00 | Carola-Bibiane Schönlieb | Combining knowledge and data driven methods for solving inverse imaging problems - getting the best from both worlds | |

Wed | Nov 11 | 11:00 | Bin Dong | Learning and Learning to Solve PDEs | |

Wed | Nov 04 | 18:00 | Joan Bruna | Mathematical aspects of neural network learning through measure dynamics | |

Wed | Oct 28 | 18:00 | Florent Krzakala | Some exactly solvable models for statistical machine learning | |

Wed | Oct 21 | 17:00 | Mauro Maggioni | Learning Interaction laws in particle- and agent-based systems | |

Wed | Oct 14 | 17:00 | Lindsey Gray | Graph Neural Networks for Pattern Recognition in Particle Physics | |

Wed | Oct 07 | 10:00 | Weinan E | Machine Learning and Scientific Computing | |

Wed | Sep 30 | 17:00 | Gunnar Carlsson | Topological Data Analysis and Deep Learning | |

Thu | Jul 30 | 16:30 | Masoud Mohseni | TensorFlow Quantum: An open source framework for hybrid quantum-classical machine learning. | |

Thu | Jul 23 | 16:30 | Marylou Gabrié | Progress and hurdles in the statistical mechanics of deep learning | |

Thu | Jul 16 | 16:30 | João Miranda Lemos | Reinforcement learning and adaptive control | |

Thu | Jul 09 | 16:30 | Francisco C. Santos | Climate action and cooperation dynamics under uncertainty | |

Thu | Jul 02 | 16:30 | Kyle Cranmer | On the Interplay between Physics and Deep Learning. | |

Thu | Jun 25 | 16:30 | Csaba Szepesvári | Confident Off-Policy Evaluation and Selection through Self-Normalized Importance Weighting | |

Thu | Jun 18 | 16:30 | João Xavier | Learning from distributed datasets: an introduction with two examples | |

Thu | Jun 11 | 16:30 | Marcelo Pereyra | Efficient Bayesian computation by proximal Markov chain Monte Carlo: when Langevin meets Moreau | |

Thu | Jun 04 | 16:30 | Afonso Bandeira | Computation, statistics, and optimization of random functions | |

Thu | May 28 | 16:30 | Hilbert Johan Kappen | Path integral control theory | |

Thu | May 21 | 16:30 | André David Mendes | How we discovered the Higgs ahead of schedule - ML's role in unveiling the keystone of elementary particle physics | |

Thu | May 14 | 16:30 | Cláudia Soares | The learning machine and beyond: a tour for the curious | |