Mathematics, Physics and Machine Learning (IST, Lisbon)

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data structures and algorithms machine learning mathematical physics information theory optimization and control data analysis, statistics and probability

Instituto Superior Técnico

Audience: Researchers in the topic
Seminar series time: Thursday 16:00-17:00 in your time zone, UTC
Organizers: Mário Figueiredo, Tiago Domingos, Francisco Melo, Jose Mourao*, Cláudia Nunes, Yasser Omar, Pedro Alexandre Santos, João Seixas, Cláudia Soares, João Xavier
*contact for this listing

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Upcoming talks
Past talks
Your timeSpeakerTitle
ThuJun 0616:00Kathryn HessOf mice and men
ThuFeb 1517:00Pedro DomingosDeep Networks Are Kernel Machines
ThuJan 1117:00Francisco Förster BurónThe ALeRCE astronomical alert broker
FriSep 2213:00Olga MulaOptimal State and Parameter Estimation Algorithms and Applications to Biomedical Problems
ThuJun 2216:00Artemy KolchinskyInformation geometry for nonequilibrium processes
ThuJun 1516:00Mário FigueiredoCausal Discovery from Observations: Introduction and Some Recent Advances
ThuJun 0816:00Sara MagliacaneCausal vs causality-inspired representation learning
ThuJun 0116:00Andreas DöppMachine-learning strategies in laser-plasma physics
ThuMay 1816:00Rui CastroAnomaly detection for a large number of streams: a permutation/rank-based higher criticism approach
ThuMay 1116:00Harry DesmondExhaustive Symbolic Regression (or how to find the best function for your data)
ThuMay 0416:00Diogo GomesMathematics for data science and AI - curriculum design, experiences, and lessons learned
ThuApr 2716:00Paulo RosaDeep Reinforcement Learning based Integrated Guidance and Control for a Launcher Landing Problem
ThuApr 2016:00Rongjie LaiLearning Manifold-Structured Data using Deep Neural Networks: Theory and Applications
ThuMar 2317:00Memming ParkOn learning signals in recurrent networks
ThuMar 1617:00Valentin De BortoliDiffusion models, theory and methodology
ThuMar 0917:00Gonçalo CorreiaLearnable Sparsity and Weak Supervision for Data-Efficient, Transparent, and Compact Neural Models
ThuMar 0217:00Sara A. SollaLow Dimensional Manifolds for Neural Dynamics
ThuFeb 0917:00Ben EdelmanStudies in feature learning through the lens of sparse boolean functions
ThuFeb 0217:00Yang-Hui HeCOLLOQUIUM: Universes as Bigdata: Physics, Geometry and Machine-Learning
ThuJan 1917:00Alhussein FawziDiscovering faster matrix multiplication algorithms with deep reinforcement learning
ThuJan 1217:00Sebastian EngelkeMachine learning beyond the data range: extreme quantile regression
ThuDec 1517:00Bruno LoureiroPhase diagram of Stochastic Gradient Descent in high-dimensional two-layer neural networks
ThuNov 2417:00Markus ReichsteinIntegrating Machine Learning with System Modelling and Observations for a better understanding of the Earth System
ThuNov 1717:00Tom GoldsteinBuilding (and breaking) neural networks that think fast and slow
ThuNov 1017:00João SacramentoThe least-control principle for learning at equilibrium
ThuNov 0317:00Frederico FiuzaAccelerating the understanding of nonlinear dynamical systems using machine learning
ThuOct 2716:00Robert NowakThe Neural Balance Theorem and its Consequences
FriOct 1413:30José Miguel UrbanoSemi-Supervised Learning and the infinite-Laplacian (Lectures 1 & 2)
FriOct 1408:30Diogo GomesFrom Calculus of Variations to Reinforcement Learning (Lectures 1 & 2)
ThuSep 2916:00Petar VeličkovićGeometric Deep Learning: Grids, Graphs, Groups, Geodesics and Gauges
ThuSep 0816:00Inês HipólitoThe Free Energy Principle in the Edge of Chaos
ThuJul 1416:00Joseph BakarjiDimensionally Consistent Learning with Buckingham Pi
ThuJul 0716:00Audrey DurandInteractive learning for Neurosciences - Between Simulation and Reality
ThuJun 3016:00Dario IzzoGeodesy of irregular small bodies via neural density fields: geodesyNets
ThuJun 1617:00John BaezShannon Entropy from Category Theory
ThuJun 0916:00Paulo TabuadaDeep neural networks, universal approximation, and geometric control
ThuJun 0216:00Anja ButterMachine Learning and LHC Event Generation
ThuMay 2616:00Yongji WangPhysics-informed neural networks for solving 3-D Euler equation
ThuMay 1916:00Stanley OsherConservation laws and generalized optimal transport
ThuMay 0516:00Andrea L. BertozziGraph based models in semi-supervised and unsupervised learning
ThuApr 2809:00Emtiyaz KhanThe Bayesian Learning Rule for Adaptive AI
ThuApr 2116:00Rianne van den BergGenerative models for discrete random variables
ThuApr 1416:00Dmitry KrotovModern Hopfield Networks in AI and Neurobiology
ThuMar 3116:00Josef UrbanMachine Learning and Theorem Proving
ThuMar 2417:00Fernando E. RosasTowards a deeper understanding of high-order interdependencies in complex systems
ThuMar 0317:00Jan KieselerThe MODE project
ThuFeb 2416:30André F. T. MartinsFrom Sparse Modeling to Sparse Communication
ThuFeb 0317:00Joosep PataMachine learning for data reconstruction at the LHC
ThuJan 2017:00Anders HansenWhy things don’t work — On the extended Smale's 9th and 18th problems (the limits of AI) and methodological barriers
ThuJan 1317:00Dan RobertsThe Principles of Deep Learning Theory
ThuDec 0917:00Pier Luigi DragottiComputational Imaging for Art investigation and for Neuroscience
ThuDec 0217:00Soledad VillarEquivariant machine learning structure like classical physics
ThuNov 2517:00Suman RavuriSkilful precipitation nowcasting using deep generative models of radar
ThuNov 1117:00Michael ArbelAnnealed Flow Transport Monte Carlo
ThuNov 0417:00George Em KarniadakisOperator regression via DeepOnet: Theory, Algorithms and Applications
ThuOct 2116:00Constantino TsallisStatistical mechanics for complex systems
ThuOct 1416:00Clément HonglerNeural Tangent Kernel
ThuSep 3016:00Volkan CevherOptimization Challenges in Adversarial Machine Learning
ThuSep 2309:00Leong Chuan KwekMachine Learning and Quantum Technology
ThuSep 1616:00J. Nathan KutzDeep learning for the discovery of parsimonious physics models
WedJul 2816:00Simon DuProvable Representation Learning
FriJul 0913:00Usman KhanDistributed ML: Optimal algorithms for distributed stochastic non-convex optimization
FriJul 0213:00Ard LouisDeep neural networks have an inbuilt Occam's razor
FriJun 2513:00Yuejie ChiPolicy Optimization in Reinforcement Learning: A Tale of Preconditioning and Regularization
FriJun 1813:00Ruth MisenerPartition-based formulations for mixed-integer optimization of trained ReLU neural networks
FriJun 1113:00Ulugbek KamilovComputational Imaging: Reconciling Physical and Learned Models
FriJun 0413:00Mathieu BlondelEfficient and Modular Implicit Differentiation
FriMay 2813:00Gustau Camps-VallsPhysics Aware Machine Learning for the Earth Sciences
FriMay 2113:00Kyriakos VamvoudakisLearning-Based Actuator Placement and Receding Horizon Control for Security against Actuation Attacks
FriMay 0713:00Rebecca WillettMachine Learning and Inverse Problems: Deeper and More Robust
WedApr 2817:00Mikhail BelkinTwo mathematical lessons of deep learning
FriApr 2313:00Jan PetersRobot Learning - Quo Vadis?
WedApr 1417:00Gabriel PeyréScaling Optimal Transport for High dimensional Learning
FriApr 0913:00Pedro A. SantosTwo-time scale stochastic approximation for reinforcement learning with linear function approximation
WedMar 3117:00Steve BruntonMachine learning for Fluid Mechanics
MonMar 2217:00Markus HeylQuantum many-body dynamics in two dimensions with artificial neural networks
WedMar 1718:00Hsin Yuan Huang, (Robert)Information-theoretic bounds on quantum advantage in machine learning
WedMar 0318:00A. Pedro AguiarModel based control design combining Lyapunov and optimization tools: Examples in the area of motion control of autonomous robotic vehicles
MonFeb 2217:00Maciej Koch-J8anuszStatistical physics through the lens of real-space mutual information
WedFeb 1718:00Mário FigueiredoDealing with Correlated Variables in Supervised Learning
WedFeb 1018:00Caroline UhlerCausal Inference and Overparameterized Autoencoders in the Light of Drug Repurposing for SARS-CoV-2
WedFeb 0318:00Miguel CouceiroMaking ML Models fairer through explanations, feature dropout, and aggregation
WedJan 2711:00Xavier BressonBenchmarking Graph Neural Networks
WedJan 2018:00James HalversonNeural Networks and Quantum Field Theory
WedJan 1318:00Anna C. GilbertMetric representations: Algorithms and Geometry
WedJan 0618:00Sanjeev AroraThe quest for mathematical understanding of deep learning
WedDec 1618:00René VidalFrom Optimization Algorithms to Dynamical Systems and Back
WedDec 0918:00Samantha KleinbergData, Decisions, and You: Making Causality Useful and Usable in a Complex World
WedDec 0218:00Gitta KutyniokDeep Learning meets Physics: Taking the Best out of Both Worlds in Imaging Science
WedNov 2518:00Tommaso DorigoDealing with Systematic Uncertainties in HEP Analysis with Machine Learning Methods
FriNov 2015:00Carola-Bibiane SchönliebCombining knowledge and data driven methods for solving inverse imaging problems - getting the best from both worlds
WedNov 1111:00Bin DongLearning and Learning to Solve PDEs
WedNov 0418:00Joan BrunaMathematical aspects of neural network learning through measure dynamics
WedOct 2818:00Florent KrzakalaSome exactly solvable models for statistical machine learning
WedOct 2117:00Mauro MaggioniLearning Interaction laws in particle- and agent-based systems
WedOct 1417:00Lindsey GrayGraph Neural Networks for Pattern Recognition in Particle Physics
WedOct 0710:00Weinan EMachine Learning and Scientific Computing
WedSep 3017:00Gunnar CarlssonTopological Data Analysis and Deep Learning
ThuJul 3016:30Masoud MohseniTensorFlow Quantum: An open source framework for hybrid quantum-classical machine learning.
ThuJul 2316:30Marylou GabriéProgress and hurdles in the statistical mechanics of deep learning
ThuJul 1616:30João Miranda LemosReinforcement learning and adaptive control
ThuJul 0916:30Francisco C. SantosClimate action and cooperation dynamics under uncertainty
ThuJul 0216:30Kyle CranmerOn the Interplay between Physics and Deep Learning.
ThuJun 2516:30Csaba SzepesváriConfident Off-Policy Evaluation and Selection through Self-Normalized Importance Weighting
ThuJun 1816:30João XavierLearning from distributed datasets: an introduction with two examples
ThuJun 1116:30Marcelo PereyraEfficient Bayesian computation by proximal Markov chain Monte Carlo: when Langevin meets Moreau
ThuJun 0416:30Afonso BandeiraComputation, statistics, and optimization of random functions
ThuMay 2816:30Hilbert Johan KappenPath integral control theory
ThuMay 2116:30André David MendesHow we discovered the Higgs ahead of schedule - ML's role in unveiling the keystone of elementary particle physics
ThuMay 1416:30Cláudia SoaresThe learning machine and beyond: a tour for the curious
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