Videos

2024/02/15

Pedro Domingos

Deep Networks Are Kernel Machines

2024/01/11

Francisco Förster Burón

The ALeRCE astronomical alert broker

2023/09/22

Olga Mula

Optimal State and Parameter Estimation Algorithms and Applications to Biomedical Problems

2023/06/22

Artemy Kolchinsky

Information geometry for nonequilibrium processes

2023/06/15

Mário Figueiredo

Causal Discovery from Observations: Introduction and Some Recent Advances

2023/06/08

Sara Magliacane

Causal vs causality-inspired representation learning

2023/06/01

Andreas Döpp

Machine-learning strategies in laser-plasma physics

2023/05/18

Rui Castro

Anomaly detection for a large number of streams: a permutation/rank-based higher criticism approach

2023/05/11

Harry Desmond

Exhaustive Symbolic Regression (or how to find the best function for your data)

2023/05/04

Diogo Gomes

Mathematics for data science and AI - curriculum design, experiences, and lessons learned

2023/04/27

Paulo Rosa

Deep Reinforcement Learning based Integrated Guidance and Control for a Launcher Landing Problem

2023/04/20

Rongjie Lai

Learning Manifold-Structured Data using Deep Neural Networks: Theory and Applications

2023/03/23

Memming Park

On learning signals in recurrent networks

2023/03/16

Valentin De Bortoli

Diffusion models, theory and methodology

2023/03/09

Gonçalo Correia

Learnable Sparsity and Weak Supervision for Data-Efficient, Transparent, and Compact Neural Models

2023/03/02

Sara A. Solla

Low Dimensional Manifolds for Neural Dynamics

2023/02/09

Ben Edelman

Studies in feature learning through the lens of sparse boolean functions

2023/02/02

Yang-Hui He

Universes as Bigdata: Physics, Geometry and Machine-Learning

2023/02/01

Yang-Hui He

A data science driven approach to physics and mathematics V

2023/02/01

Yang-Hui He

A data science driven approach to physics and mathematics IV

2023/01/31

Yang-Hui He

A data science driven approach to physics and mathematics III

2023/01/31

Yang-Hui He

A data science driven approach to physics and mathematics II

2023/01/30

Yang-Hui He

A data science driven approach to physics and mathematics I

2023/01/19

Alhussein Fawzi

Discovering faster matrix multiplication algorithms with deep reinforcement learning

2023/01/12

Sebastian Engelke

Machine learning beyond the data range: extreme quantile regression

2022/12/15

Bruno Loureiro

Phase diagram of Stochastic Gradient Descent in high-dimensional two-layer neural networks

2022/11/24

Markus Reichstein

Integrating Machine Learning with System Modelling and Observations for a better understanding of the Earth System

2022/11/17

Tom Goldstein

Building (and breaking) neural networks that think fast and slow

2022/11/10

João Sacramento

The least-control principle for learning at equilibrium

2022/11/03

Frederico Fiuza

Accelerating the understanding of nonlinear dynamical systems using machine learning

2022/10/27

Robert Nowak

The Neural Balance Theorem and its Consequences

2022/10/14

José Miguel Urbano

Semi-Supervised Learning and the $\infty$-Laplacian I

2022/10/14

Diogo Gomes

From Calculus of Variations to Reinforcement Learning II

2022/10/14

Diogo Gomes

From Calculus of Variations to Reinforcement Learning I

2022/09/29

Petar Veličković

Geometric Deep Learning: Grids, Graphs, Groups, Geodesics and Gauges

2022/09/08

Inês Hipólito

The Free Energy Principle in the Edge of Chaos

2022/07/21

Paulo Tabuada

Deep neural networks, universal approximation, and geometric control

2022/07/14

Joseph Bakarji

Dimensionally Consistent Learning with Buckingham Pi

2022/07/07

Audrey Durand

Interactive learning for Neurosciences - Between Simulation and Reality

2022/06/30

Dario Izzo

Geodesy of irregular small bodies via neural density fields: geodesyNets

2022/06/16

John Baez

Shannon Entropy from Category Theory

2022/06/02

Anja Butter

Machine Learning and LHC Event Generation

2022/05/26

Yongji Wang

Physics-informed neural networks for solving 3-D Euler equation

2022/05/19

Stanley Osher

Conservation laws and generalized optimal transport

2022/05/05

Andrea L. Bertozzi

Graph based models in semi-supervised and unsupervised learning

2022/04/28

Emtiyaz Khan

The Bayesian Learning Rule for Adaptive AI

2022/04/21

Rianne van den Berg

Generative models for discrete random variables

2022/04/14

Dmitry Krotov

Modern Hopfield Networks in AI and Neurobiology

2022/03/31

Josef Urban

Machine Learning and Theorem Proving

2022/03/24

Fernando E. Rosas

Towards a deeper understanding of high-order interdependencies in complex systems

2022/03/03

Jan Kieseler

The MODE project

2022/02/24

André F. T. Martins

From Sparse Modeling to Sparse Communication

2022/02/03

Joosep Pata

Machine learning for data reconstruction at the LHC

2022/01/20

Anders Hansen

Why things don’t work — On the extended Smale's 9th and 18th problems (the limits of AI) and methodological barriers

2022/01/13

Dan Roberts

The Principles of Deep Learning Theory

2021/12/09

Pier Luigi Dragotti

Computational Imaging for Art investigation and for Neuroscience

2021/12/02

Soledad Villar

Equivariant machine learning structure like classical physics

2021/11/25

Suman Ravuri

Skilful precipitation nowcasting using deep generative models of radar

2021/11/11

Michael Arbel

Annealed Flow Transport Monte Carlo

2021/11/04

George Em Karniadakis

Operator regression via DeepOnet: Theory, Algorithms and Applications

2021/10/21

Constantino Tsallis

Statistical mechanics for complex systems

2021/10/14

Clément Hongler

Neural Tangent Kernel

2021/09/30

Volkan Cevher

Optimization Challenges in Adversarial Machine Learning

2021/09/23

Leong Chuan Kwek

Machine Learning and Quantum Technology

2021/09/16

J. Nathan Kutz

Deep learning for the discovery of parsimonious physics models

2021/07/28

Simon Du

Provable Representation Learning

2021/07/09

Usman Khan

Distributed ML: Optimal algorithms for distributed stochastic non-convex optimization

2021/07/02

Ard Louis

Deep neural networks have an inbuilt Occam's razor

2021/06/25

Yuejie Chi

Policy Optimization in Reinforcement Learning: A Tale of Preconditioning and Regularization

2021/06/18

Ruth Misener

Partition-based formulations for mixed-integer optimization of trained ReLU neural networks

2021/06/11

Ulugbek Kamilov

Computational Imaging: Reconciling Physical and Learned Models

2021/05/28

Gustau Camps-Valls

Physics Aware Machine Learning for the Earth Sciences

2021/05/21

Kyriakos Vamvoudakis

Learning-Based Actuator Placement and Receding Horizon Control for Security against Actuation Attacks

2021/05/07

Rebecca Willett

Machine Learning and Inverse Problems: Deeper and More Robust

2021/04/28

Mikhail Belkin

Two mathematical lessons of deep learning

2021/04/23

Jan Peters

Robot Learning - Quo Vadis?

2021/04/14

Gabriel Peyré

Scaling Optimal Transport for High dimensional Learning

2021/04/09

Pedro A. Santos

Two-time scale stochastic approximation for reinforcement learning with linear function approximation

2021/03/31

Steve Brunton

Machine learning for Fluid Mechanics

2021/03/22

Markus Heyl

Quantum many-body dynamics in two dimensions with artificial neural networks

2021/03/17

Hsin Yuan Huang, (Robert)

Information-theoretic bounds on quantum advantage in machine learning

2021/03/03

A. Pedro Aguiar

Model based control design combining Lyapunov and optimization tools: Examples in the area of motion control of autonomous robotic vehicles

2021/02/22

Maciej Koch-Janusz

Statistical physics through the lens of real-space mutual information

2021/02/17

Mário Figueiredo

Dealing with Correlated Variables in Supervised Learning

2021/02/10

Caroline Uhler

Causal Inference and Overparameterized Autoencoders in the Light of Drug Repurposing for SARS-CoV-2

2021/02/03

Miguel Couceiro

Making ML Models fairer through explanations, feature dropout, and aggregation

2021/01/27

Xavier Bresson

Benchmarking Graph Neural Networks

2021/01/20

James Halverson

Neural Networks and Quantum Field Theory

2021/01/13

Anna C. Gilbert

Metric representations: Algorithms and Geometry

2021/01/06

Sanjeev Arora

The quest for mathematical understanding of deep learning

2020/12/16

René Vidal

From Optimization Algorithms to Dynamical Systems and Back

2020/12/09

Samantha Kleinberg

Data, Decisions, and You: Making Causality Useful and Usable in a Complex World

2020/12/02

Gitta Kutyniok

Deep Learning meets Physics: Taking the Best out of Both Worlds in Imaging Science

2020/11/25

Tommaso Dorigo

Dealing with Systematic Uncertainties in HEP Analysis with Machine Learning Methods

2020/11/20

Carola-Bibiane Schönlieb

Combining knowledge and data driven methods for solving inverse imaging problems — getting the best from both worlds

2020/11/11

Bin Dong

Learning and Learning to Solve PDEs

2020/11/04

Joan Bruna

Mathematical aspects of neural network learning through measure dynamics

2020/10/28

Florent Krzakala

Some exactly solvable models for statistical machine learning

2020/10/21

Mauro Maggioni

Learning Interaction laws in particle- and agent-based systems

2020/10/14

Lindsey Gray

Graph Neural Networks for Pattern Recognition in Particle Physics

2020/10/07

Weinan E

Machine Learning and Scientific Computing

2020/09/30

Gunnar Carlsson

Topological Data Analysis and Deep Learning

2020/07/30

Masoud Mohseni

TensorFlow Quantum: An open source framework for hybrid quantum-classical machine learning

2020/07/23

Marylou Gabrié

Progress and hurdles in the statistical mechanics of deep learning

2020/07/16

João Miranda Lemos

Reinforcement Learning and Adaptive Control

2020/07/09

Francisco C. Santos

Climate action and cooperation dynamics under uncertainty

2020/07/02

Kyle Cranmer

On the Interplay between Physics and Deep Learning

2020/06/25

Csaba Szepesvári

Confident Off-Policy Evaluation and Selection through Self-Normalized Importance Weighting

2020/06/18

João Xavier

Learning from distributed datasets: an introduction with two examples

2020/06/11

Marcelo Pereyra

Efficient Bayesian computation by proximal Markov chain Monte Carlo: when Langevin meets Moreau

2020/06/04

Afonso Bandeira

Computation, Statistics, and Optimization of random functions

2020/05/28

Hilbert Johan Kappen

Path integral control theory

2020/05/21

André David Mendes

How we discovered the Higgs ahead of schedule: ML’s role in unveiling the keystone of elementary particle physics

2020/05/14

Cláudia Soares

The learning machine and beyond: a tour for the curious