Deepmind/ELLIS CSML Seminar Series

The DeepMind/ELLIS Computational Statistics and Machine Learning (CSML) Seminar Series present a wide selection of talks covering the diverse interests of the ELLIS Unit. The seminars range from invited external speakers to PhD and postdoc presentations designed to foster collaboration between the large number of machine learning and statistics researchers in the ELLIS Unit.

The seminar series consists of both in-person and virtual talks, usually held on Fridays. In-person events have traditionally been organised around lunch time, while virtual events are held on Zoom at various times during the day to allow for the possibility to host international speakers. PhD students and postdocs of the AI Centre, Gatsby Unit, Computer Science and Statistics departments are particularly encouraged to attend.

The CSML seminar series is kindly sponsored by DeepMind.

Information regarding our upcoming talks can be found here, as well as on our Twitter page. The recordings of most of the seminars are uploaded to our YouTube channel.

If you would like to be added to the mailing list for seminar announcements, please complete the request form.

Contact: Kai Teh
Mailing list: request form
Social: Twitter
Recordings: YouTube
Usual time: Fridays (12pm for in-person events, time varies for virtual events)
Zoom link: https://ucl.zoom.us/j/97245943682
Calendar: WebCal link & URL link

Upcoming Events

Date Time Description Location
Fri, 27 Apr 2012 12:30-14:00 Mark Girolami (UCL): Riemann Manifold Langevin and Hamiltonian Monte Carlo Methods Zoom
Fri, 11 May 2012 12:30-14:00 Tom Furmston (UCL): Gradient-based algorithms for policy search Zoom
Fri, 25 May 2012 12:30-14:00 Gabi Teodoru (UCL): Spectral Learning of Latent Variable Models and its Interpretation as an Optimization Problem Zoom
Mon, 11 Jun 2012 12:00-13:30 Larry Wasserman (Carnegie Mellon University): Discussion Zoom
Tue, 12 Jun 2012 12:30-14:00 Shivani Lamba, (Founder/CEO of Chechako) and Marshall Levine, (Wise Counsel for Chechako Ltd) (Multiple): Startup Pitch Zoom
Fri, 22 Jun 2012 12:30-14:00 Adam Sykulski (UCL): Statistical modelling and estimation of physical phenomena in ocean surface trajectories Zoom
Mon, 02 Jul 2012 12:30-14:00 Yuan (Alan) Qi (Purdue University): Bayesian learning with big data: virtual vector machines and Gaussian processes with sparse eigenval Zoom
Fri, 13 Jul 2012 12:30-14:00 Vinayak Rao (UCL): Efficient MCMC for Continuous Time Discrete State Systems Zoom
Fri, 28 Sep 2012 12:30-14:00 Janaina Mourao-Miranda, Jane Maryam Rondina, Maria Joao Rosa (UCL): Machine learning approaches for clinical neuroimaging data Zoom
Fri, 12 Oct 2012 12:30-14:00 Jan Gasthaus (UCL): Hierarchical Bayesian Nonparametric Models for Sequences Zoom
Fri, 26 Oct 2012 12:30-14:00 Dino Sejdinovic (UCL): Equivalence of distance-based and RKHS-based statistics in hypothesis testing Zoom
Fri, 09 Nov 2012 12:30-14:00 Steffen Grunewalder (UCL): Conditional Expectation Estimates for Discrete Control Zoom
Fri, 23 Nov 2012 12:30-14:00 Ben Calderhead and Simon Byrne (UCL): The use of geometry in MCMC Zoom
Thu, 29 Nov 2012 13:00-14:30 Juan Carlos Martinez-Ovando (Banco de México): Non- and semi-parametric construction of stationary dependent models Zoom
Fri, 11 Jan 2013 12:30-14:00 Ed Challis (UCL): Variational approximate inference in linear latent variable models Zoom
Fri, 25 Jan 2013 12:30-14:00 Andriy Mnih (UCL): A fast and simple algorithm for training neural probabilistic language models Zoom
Fri, 08 Feb 2013 12:30-14:00 Gary Macindoe (UCL): A hybrid Cholesky decomposition algorithm for multicore CPUs with GPU accelerators Zoom
Fri, 22 Feb 2013 12:30-14:00 Matthew Higgs (UCL): A Population Approach to Ubicomp System Design (APAUSD) Zoom
Fri, 01 Mar 2013 12:30-14:00 Tamara Broderick (University of California, Berkeley): Feature allocations, probability functions, and paintboxes Zoom
Fri, 08 Mar 2013 12:30-14:00 David Silver (UCL): Reinforcement Learning and Simulation-Based Search Zoom
Fri, 22 Mar 2013 12:30-14:00 Vladimir Krylov (UCL): Extraction of geometrical objects from images with MCMC methods Zoom
Fri, 05 Apr 2013 12:30-14:00 Robert Jenssen (University of Tromso, Norway): Entropy-Relevant Dimensions in Kernel Feature Space Zoom
Fri, 26 Apr 2013 12:30-14:00 Chris Bracegirdle (UCL (CS)): Probabilistic Inference for Changepoints and Cointegration Zoom
Fri, 10 May 2013 12:30-14:00 Isadora Antoniano-Villalobos (Department of Decision Sciences, Bocconi University, Italy): Bayesian inference for nonparametric mixture models with intractable normalizing constants Zoom
Fri, 01 Nov 2013 13:00-14:00 Sam Livingstone (UCL, Statistics): Diffusions with position-dependent volatility and the Metropolis-adjusted Langevin algorithm Zoom
Fri, 15 Nov 2013 13:00-14:00 Thore Graepel (Microsoft Research Cambridge and Chair of Machine Learning, Department of Computer Science, UCL): Private traits and attributes are predictable from digital records of human behavior Zoom
Fri, 22 Nov 2013 13:00-14:00 Mario Marchand (Universite Laval): Risk Bounds and Learning Algorithms for the Regression Approach to Structured Output Prediction Zoom
Fri, 29 Nov 2013 13:00-14:00 Alfredo Kalaitzis, Bernadino Romero Paredes, Dino Sejdinovic (UCL): NIPS preview talks Zoom
Fri, 10 Jan 2014 13:00-14:00 Stephen Pasteris (UCL, Computer Science): Online Similarity Prediction of Networked Data Zoom
Fri, 24 Jan 2014 13:00-14:00 Ioanna Manolopoulou (UCL, Statistics): Diffusion modelling of motion trajectories under the influence of covariates Zoom
Fri, 14 Feb 2014 13:00-14:00 Peter Forbes (Oxford, Department of Statistics): Quantifying Fingerprint Evidence using Bayesian Alignment Zoom
Fri, 21 Feb 2014 13:00-14:00 Marc Deisenroth (Imperial College, London): Statistical Machine Learning for Autonomous Systems Zoom
Fri, 28 Feb 2014 13:00-14:00 Trevor Cohn (University of Sheffield): Language processing using Gaussian Processes Zoom
Fri, 07 Mar 2014 13:00-14:00 Robert Stojnic (Cambridge Systems Biology Centre): Bayesian Molecular LEGO Zoom
Fri, 14 Mar 2014 13:00-14:00 Srini Turaga (Gatsby Unit & Wolfson Institute for Biomedical Research): Using ConvNets, MALIS and crowd-sourcing to map the retinal connectome. Zoom
Fri, 21 Mar 2014 13:00-14:00 Lloyd Elliott (UCL, Gatsby): Bayesian nonparametric dynamic-clustering and genetic imputation Zoom
Fri, 04 Apr 2014 13:00-14:00 Sarah Chisholm (UCL, Computer Science): Statistical Methods for Analysing Time Series Data of Animal Movement Zoom
Fri, 11 Apr 2014 13:00-14:00 Dimitrios Athanasakis (UCL): Principled Non-Linear Feature Selection (with applications in representation learning) Zoom
Fri, 25 Apr 2014 13:00-14:00 Alex Graves (Google Deepmind): Generating Sequences with Recurrent Neural Networks Zoom
Fri, 02 May 2014 13:00-14:00 Zoltan Szabo (UCL, Gatsby): Distribution Regression - the Set Kernel Heuristic is Consistent Zoom
Fri, 09 May 2014 14:00-15:00 Demis Hassabis (Google DeepMind): General Artificial Intelligence Zoom
Fri, 16 May 2014 13:00-14:00 Chris Watkins (Royal Holloway University of London): Evolution as a standard Monte-Carlo algorithm Zoom
Fri, 23 May 2014 13:00-14:00 Remi Bardenet (Deptartment of Statistics, Oxford): Scaling up MCMC: a subsampling approach Zoom
Fri, 13 Jun 2014 13:00-14:00 David Barber, Kacper Chwiałkowski, Dino Sejdinovic (UCL): ICML preview talks Zoom
Thu, 19 Jun 2014 11:00-12:00 Gerhard Neumann (TU Darmstadt): Learning Modular Control Policies in Robotics Zoom
Fri, 12 Sep 2014 13:00-14:00 Remi Munos (INRIA Lille): Two generic principles in modern bandits: the optimistic principle and Thompson sampling Zoom
Fri, 17 Oct 2014 16:00-17:00 Peter Flach (University of Bristol): Comparing apples and oranges -- reinterpreting common evaluation metrics in classification Zoom
Fri, 07 Nov 2014 13:00-14:00 Chris Williams (Edinburgh University): Switching Linear Dynamical Systems for Condition Monitoring in the Intensive Care Unit Zoom
Fri, 14 Nov 2014 13:00-14:00 Ata Kaban (University of Birmingham): Learning with random projections Zoom
Fri, 21 Nov 2014 13:00-14:00 Amos Storkey (Edinburgh University): Series Expansion Methods for Approximate Learning, Filtering and Smoothing in Diffusions Zoom
Fri, 28 Nov 2014 13:00-14:00 Stephen Roberts (Oxford University): Planets, Pulsars, People and Petabytes: Explorations of Machine Learning in Astronomy Zoom
Fri, 05 Dec 2014 13:00-14:00 Andrew McDonald, Kacper Chwialkowski, Balaji Lakshminarayanan (UCL/Gatsby): NIPS Previews Zoom
Fri, 16 Jan 2015 13:00-14:00 Kamil Ciosek (UCL, Computer Science): Combining state abstraction and temporal abstraction in MDP solving Zoom
Fri, 23 Jan 2015 13:00-14:00 Vladimir Vovk (Royal Holloway University of London): Probabilistic prediction in machine learning Zoom
Fri, 06 Feb 2015 13:00-14:00 Peter Tino (University of Birmingham): Learning from Temporal Data Using Dynamical Feature Space Zoom
Fri, 27 Feb 2015 13:00-14:00 Matt Hoffmann (University Of Cambridge): Predictive Entropy Search Zoom
Wed, 04 Mar 2015 13:00-14:00 Jason Weston (Facebook, New York): Memory Networks Zoom
Fri, 06 Mar 2015 13:00-14:00 Heiko Strathmann (covering co-author Mark Girolami who cannot make it) (Gatsby Unit, UCL): Unbiased Bayes for Big Data: Paths of Partial Posteriors Zoom
Fri, 13 Mar 2015 13:00-14:00 Peter Sollich (King's College, University Of London): Gaussian process regression on graphs Zoom
Fri, 20 Mar 2015 13:00-14:00 Iain Murray (Edinburgh University): Flexible and deep models for density estimation Zoom
Fri, 27 Mar 2015 13:00-14:00 Adrian Weller (University of Cambridge): Recent results on the Bethe approximation Zoom
Fri, 10 Apr 2015 13:00-14:00 Seppo Virtanen (University of Warwick): Non-parametric Bayes to infer playing strategies adopted in a population of mobile gamers Zoom
Fri, 17 Apr 2015 11:00-12:00 Csaba Szepesvari (University of Alberta, Canada): Optimistic Algorithms for Online Learning in Structured Decision Problems Zoom
Fri, 08 May 2015 13:00-14:00 Patrick Conrad (University of Warwick): Probability Measures on Numerical Solutions of ODEs and PDEs for Uncertainty Quant. and Inference Zoom
Fri, 15 May 2015 13:00-14:00 Zhenwen Dai (University of Sheffield): Variational Hierarchical Community of Experts Zoom
Fri, 29 May 2015 13:00-14:00 Javier Gonzalez (University of Sheffield): Batch Bayesian Optimization via Local Penalization Zoom
Mon, 29 Jun 2015 12:00-13:00 Manik Varma (Microsoft Research India): Extreme Classification: A New Paradigm for Ranking & Recommendation Zoom
Fri, 24 Jul 2015 13:00-14:00 Ribana Roscher (Freie Universität Berlin): Discriminative and Reconstructive Methods for Classification of Remote Sensing Images Zoom
Fri, 28 Aug 2015 13:00-14:00 Elad Hazan (Princeton University): Classification with Low Rank and Missing Data Zoom
Fri, 16 Oct 2015 13:00-14:00 Roberto Calandra (TU Darmstadt): Bayesian Modeling for Optimization and Control in Robotics Zoom
Fri, 13 Nov 2015 13:00-14:00 Yarin Gal (University of Cambridge): Modern Deep Learning through Bayesian Eyes Zoom
Fri, 20 Nov 2015 13:00-14:00 Shakir Mohamed (Google Deepmind): Memory-based Bayesian Reasoning with Deep Learning Zoom
Fri, 27 Nov 2015 13:00-14:00 Tom Schaul (Google Deepmind): Universal Value Function Approximators Zoom
Fri, 04 Dec 2015 13:00-14:00 Stephen Pasteris, Wittawat Jitkrittum, Ricardo Silver (UCL): NIPS previews Zoom
Fri, 19 Feb 2016 13:00-14:00 Thore Graepel (DeepMind, University College London): DeepMind's Quest for Artificial General Intelligence: From Atari to AlphaGo and beyond Zoom
Fri, 04 Mar 2016 13:00-14:00 James Hensman (Lancaster University): Variational Inference in Gaussian Process Models Zoom
Fri, 11 Mar 2016 13:00-14:00 Francois-Xavier Briol (University of Warwick): Probabilistic Numerics Approaches to Integration Zoom
Fri, 18 Mar 2016 13:00-14:00 Emtiyaz Khan (EPFL): Approximate Bayesian Inference: Bringing Statistics, Optimization, and Machine Learning Together. Zoom
Thu, 24 Mar 2016 13:00-14:00 David Silver (Google DeepMind, University College London): AlphaGO: Mastering the game of Go with deep neural networks and tree search Zoom
Fri, 15 Apr 2016 13:00-14:00 Chris Oates (University of Technology Sydney): Stein Operators on Hilbert Spaces Zoom
Fri, 22 Apr 2016 13:00-14:00 Ingmar Schuster (Université Paris-Dauphine): Kernel Sequential Monte Carlo Zoom
Fri, 06 May 2016 13:00-14:00 Ted Meeds (University of Amsterdam): Likelihood-free Inference by Controlling Simulator Noise Zoom
Wed, 01 Jun 2016 13:00-14:00 Yee-Whye Teh (University of Oxford): Distributed Bayesian Learning Zoom
Fri, 10 Jun 2016 13:00-14:00 Andrew Fitzgibbon (Microsoft Research Cambridge): Lifting, VarPro, ICP, and all that. Zoom
Wed, 29 Jun 2016 13:00-14:00 Wolfgang Gatterbauer (CMU): Approximate lifted inference with probabilistic databases Zoom
Wed, 19 Oct 2016 13:00-14:00 Iasonas Kokkinos (UCL): Deeplab to UberNet: from task-specific to task-agnostic deep learning in computer vision Zoom
Wed, 26 Oct 2016 13:00-14:00 Hrishi Aradhye (Google Research/ Google Play): Personalized app/games recommendations on Google Play using machine learning Zoom
Fri, 28 Oct 2016 13:00-14:00 Shakir Mohamed (Google DeepMind): Building Machines that Imagine and Reason: Principles and Applications of Deep Generative Models Zoom
Fri, 04 Nov 2016 13:00-14:00 Remi Munos (Google DeepMind): Safe and efficient off-policy reinforcement learning Zoom
Fri, 11 Nov 2016 13:00-14:00 Daniel Tarlow (Microsoft Research Cambridge): Learning to Code: Machine Learning for Program Induction Zoom
Fri, 18 Nov 2016 13:00-14:00 Ryota Tomioka (Microsoft Research Cambridge): f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization Zoom
Fri, 25 Nov 2016 13:00-14:00 Theo Trouillon (Xerox Research, Univ. Grenoble Alpes): Complex-Valued Embeddings for Knowledge Base Completion Zoom
Fri, 02 Dec 2016 13:00-14:00 Various (UCL): NIPS Previews Zoom
Fri, 27 Jan 2017 13:00-14:00 Mijung Park (Amsterdam Machine Learning Lab): Variational Bayes In Private Settings (VIPS) Zoom
Fri, 20 Oct 2017 13:00-14:00 Hugh Salimbeni (Imperial College London): Doubly Stochastic Variational Inference for Deep Gaussian Processes Zoom
Fri, 03 Nov 2017 13:00-14:00 Aapo Hyvarinen (UCL, Gatsby): Nonlinear ICA using temporal structure: a principled framework for unsupervised deep learning Zoom
Fri, 17 Nov 2017 13:00-14:00 Mark van der Wilk (University of Cambridge): Convolutional Gaussian processes Zoom
Fri, 24 Nov 2017 12:00-14:00 Various (UCL): NIPS Accepted Papers Zoom
Fri, 01 Dec 2017 13:00-14:00 Yingzhen Li (University of Cambridge): Wild approximate inference: why and how Zoom
Fri, 08 Dec 2017 13:00-14:00 Relja Arandjelovic (DeepMind): Look, Listen and Learn Zoom
Fri, 15 Dec 2017 13:00-14:00 Dougal Sutherland (UCL, Gatsby): Efficient score estimation with infinite-dimensional exponential families Zoom
Fri, 12 Jan 2018 13:00-14:00 Tamara Fernandez (UCL Gatsby): A Gaussian process model for survival analysis Zoom
Fri, 26 Jan 2018 13:00-14:00 Edouard Oyallon (CentraleSupelec): Invariance & invertibility in CNNs Zoom
Fri, 02 Feb 2018 13:00-14:00 Maria Lomeli (University of Cambridge): Kernel Monte Carlo estimators for partial rankings Zoom
Fri, 09 Feb 2018 13:00-14:00 Sebastian Nowozin (Microsoft Research): Opportunities and Challenges in Generative Adversarial Networks – Looking beyond the Hype Zoom
Fri, 16 Feb 2018 13:00-14:00 Zbigniew Wojna (UCL): Architectures for big scale 2D imagery Zoom
Fri, 23 Feb 2018 13:00-14:00 Ricardo Silva (UCL Statistical Science): Some Machine Learning Tools to Aid Causal Inference Zoom
Fri, 09 Mar 2018 13:00-14:00 Sam Livingstone (UCL Statistical Science): What we talk about when we talk about non-reversible MCMC Zoom
Fri, 13 Apr 2018 13:00-14:00 Seth Flaxman (Imperial College): Predictor Variable Prioritization in Nonlinear Models: A Genetic Association Case Study Zoom
Fri, 20 Apr 2018 13:00-14:00 Lucas Theis (Twitter): Evaluating generative models Zoom
Fri, 27 Apr 2018 13:00-14:00 Stefanos Zafeiriou (Imperial College London): Discovering correlations in the modern era: robust and deep learning approaches Zoom
Fri, 25 May 2018 13:00-14:00 Marco Cuturi (CREST-ENSAE/Université Paris-Saclay): Regularization for Optimal Transport and Dynamic Time Warping Distances Zoom
Fri, 01 Jun 2018 13:00-14:00 Piotr Mirowski (DeepMind): Learning to Navigate Zoom
Fri, 08 Jun 2018 13:00-14:00 Edward Grefenstette (DeepMind): Learning to follow grounded language instructions in the "real" world Zoom
Fri, 19 Oct 2018 13:00-14:00 Kai Arulkumaran (Imperial College): Tutorial on Deep RL Zoom
Fri, 16 Nov 2018 13:00-14:00 Kayvan Sadeghi (UCL): Probabilistic Independence, Graphs, and Random Networks Zoom
Fri, 23 Nov 2018 13:00-16:00 Various (UCL, DeepMind, Imperial): NIPS Previews Zoom
Fri, 30 Nov 2018 13:00-14:00 Artur Garcez (City University): Logic Tensor Networks: A System for Deep Learning with Symbolic Reasoning Zoom
Fri, 14 Dec 2018 13:00-14:00 Ricardo Pio Monti (UCL (Gatsby)): Causal discovery with general non-linear relationships using non-linear ICA Zoom
Fri, 11 Jan 2019 13:00-14:00 Jean-Baptiste Alayrac (DeepMind): Weakly Supervised Learning from Videos Zoom
Fri, 18 Jan 2019 13:00-14:00 Sesh Kumar (Imperial College): Differentially Private Empirical Risk Minimization with Sparsity-Inducing Norms Zoom
Fri, 25 Jan 2019 13:00-14:00 Dino Sejdinovic (University of Oxford): Learning on Aggregate Outputs with Kernels Zoom
Fri, 01 Feb 2019 13:00-14:00 Ozan Öktem (Cambridge University/KTH): Deep learning for Bayesian inverse problems from tomography Zoom
Fri, 22 Feb 2019 13:00-14:00 Tengyao Wang (UCL): Sparse PCA: statistical and computational trade-offs Zoom
Fri, 01 Mar 2019 13:00-14:00 Sander Dieleman (DeepMind): Generating music in the raw audio domain Zoom
Fri, 08 Mar 2019 13:00-14:00 Sanjeevan Ahilan (UCL, Gatsby): Feudal Multi-Agent Hierarchies for Cooperative Reinforcement Learning Zoom
Fri, 26 Apr 2019 13:00-14:00 Nicolas Anastassacos (UCL): Investigating the Emergence of Cooperative Behaviour for Artificial Societies with RL Zoom
Thu, 09 May 2019 13:00-14:00 Umut Şimşekli (Télécom ParisTech): Nonparametric Generative Modeling via Optimal Transport and Diffusions with Provable Guarantees Zoom
Thu, 30 May 2019 13:00-14:00 Quentin Berthet (University of Cambridge): Optimal transport methods in statistics and machine learning: theory and applications Zoom
Fri, 07 Jun 2019 13:00-14:00 Victor Prisacariu (University of Oxford): (Deep-ish) SLAM for Next Generation AR Zoom
Fri, 21 Jun 2019 13:00-14:00 Marc Deisenroth (Imperial College): Faster Learning and Richer Models for the Next AI Challenges Zoom
Fri, 28 Jun 2019 13:00-14:00 Benjamin Guedj (UCL - INRIA): A primer on PAC-Bayesian learning with applications to deep neural networks Zoom
Fri, 11 Oct 2019 13:00-14:00 Arthur Mensch (Ecole Normale Superieure (ENS) Paris): Geometric Losses for Distributional Learning Zoom
Fri, 15 Nov 2019 13:00-14:00 Varun Kanade (University of Oxford): Implicit Regularization for Optimal Sparse Recovery Zoom
Thu, 28 Nov 2019 13:00-15:00 Claire Vernade (DeepMind): NeurIPS Previews 2019 Zoom
Fri, 10 Jan 2020 13:00-14:00 Catalina Cangea (University of Cambridge): Question Answering in Realistic Visual Environments: Challenges and Approaches Zoom
Thu, 27 Feb 2020 13:00-14:00 Aude Genevay (MIT): Learning with entropy-regularized optimal transport Zoom
Thu, 09 Apr 2020 13:00-14:00 Maurice Weiler (University of Amsterdam): Equivariant Neural Networks Zoom
Fri, 27 Nov 2020 14:00-15:00 Alexey Dosovitskiy (Google Brain): Non-convolutional architectures for recognition and generation Zoom
Fri, 04 Dec 2020 14:00-15:00 Jonathan Frankle (MIT): The Lottery Ticket Hypothesis: On Sparse, Trainable Neural Networks Zoom
Fri, 18 Dec 2020 14:00-15:00 Luigi Gresele & Giancarlo Fissore (MPI for Intelligent Systems & Inria Paris-Saclay): Relative gradient optimization of the Jacobian term in unsupervised deep learning Zoom
Fri, 08 Jan 2021 17:00-18:00 Sergey Levine (UC Berkeley): Data-Driven Reinforcement Learning: Deriving Common Sense from Past Experience Zoom
Fri, 15 Jan 2021 16:00-17:00 Jakob Foerster (Facebook): Zero-Shot (Human-AI) Coordination (in Hanabi) and Ridge Rider Zoom
Fri, 22 Jan 2021 14:00-15:00 Marta Garnelo (DeepMind): Meta-Learning and Neural Processes Zoom
Fri, 29 Jan 2021 14:00-15:00 Guido Montufar (Max Planck Institute for Mathematics in the Sciences): Implicit bias of gradient descent for mean squared error regression with wide neural networks Zoom
Fri, 05 Feb 2021 14:00-15:00 Mihaela van der Schaar (University of Cambridge): Why medicine is creating exciting new frontiers for machine learning Zoom
Fri, 12 Feb 2021 14:00-15:00 David Duvenaud (University of Toronto): Latent Stochastic Differential Equations: An Unexplored Model Class. Zoom
Fri, 19 Feb 2021 17:00-18:00 Chelsea Finn (Stanford University): Principles for Tackling Distribution Shift: Pessimism, Adaptation, and Anticipation Zoom
Fri, 26 Feb 2021 17:00-18:00 Greg Yang (Microsoft Research): Feature Learning in Infinite-Width Neural Networks Zoom
Fri, 12 Nov 2021 16:00-17:00 Mark Herbster (University College London): Online Multitask Learning with Long-Term Memory Zoom
Fri, 03 Dec 2021 15:00-16:00 Ting Chen (Google Brain): Contrastive Self-Supervised Learning and Potential Limitations Zoom
Fri, 17 Dec 2021 10:00-11:00 Makoto Yamada (Kyoto University and RIKEN AIP center): Selective inference with Kernels Zoom
Fri, 14 Jan 2022 14:00-15:00 Richard Samworth (Cambridge University): Optimal Subgroup Selection Zoom
Fri, 04 Feb 2022 14:00-15:00 Stéphanie Allassonnière (Paris Descartes University): Data Augmentation in High Dimensional Low Sample Size Setting Using a Geometry-Based Variational Autoencoder Zoom
Fri, 25 Feb 2022 17:00-18:00 Hossein Mobahi (Google): Sharpness-Aware Minimization (SAM) Current Method and Future Directions Zoom
Fri, 11 Mar 2022 10:00-11:00 Emtiyaz Khan (Tokyo RIKEN center for Advanced Intelligence Project (AIP)): The Bayesian Learning Rule for Adaptive AI Zoom
Fri, 18 Mar 2022 14:00-15:00 Alexandre Gramfort (Inria Parietal Team): Machine Learning without human supervision on neuroscience signals Zoom
Fri, 25 Mar 2022 16:00-17:00 Stefano Ermon (Stanford University): Utilitarian Information Theory Zoom
Fri, 01 Apr 2022 14:00-15:00 Julien Mairal (Inria Grenoble, Thoth team): Lucas-Kanade Reloaded End-to-End Super-Resolution from Raw Image Bursts Zoom
Fri, 08 Apr 2022 17:00-18:00 Manfred Warmuth (Google Brain (formerly University of California at Santa Cruz)): The blessing and the curse of the multiplicative updates - discusses connections between in evolution and the multiplicative updates of online learning Zoom
Fri, 29 Apr 2022 12:00-13:00 Siu Lun (Alan) Chau (Oxford University): Explaining Kernel Methods with RKHS-SHAP Ground Floor Lecture Theatre, UCL Gatsby Computational Neuroscience Unit, 25 Howland St, London W1T 4JG
Fri, 06 May 2022 12:00-13:00 Alexander Terenin (Cambridge University): Non-Euclidean Matérn Gaussian Processes Function Space, UCL Centre for Artificial Intelligence, 1st Floor, 90 High Holborn, London WC1V 6BH
Fri, 13 May 2022 12:00-13:00 Harita Dellaporta (University of Warwick): Robust Bayesian Inference for Simulator-based Models via the MMD Posterior Bootstrap G08 Sir David Davies LT, UCL Roberts Building, London WC1E 7JE
Fri, 27 May 2022 12:00-13:00 Daniel Paulin (University of Edinburgh): Efficient MCMC Sampling with Dimension-Free Convergence Rate using ADMM-type Splitting Function Space, UCL Centre for Artificial Intelligence, 1st Floor, 90 High Holborn, London WC1V 6BH
Fri, 10 Jun 2022 12:00-13:00 Badr-Eddine Chérief-Abdellatif (University of Oxford): Robust Estimation via Maximum Mean Discrepancy Function Space, UCL Centre for Artificial Intelligence, 1st Floor, 90 High Holborn, London WC1V 6BH
Fri, 17 Jun 2022 12:00-13:00 Maurice Weiler (University of Amsterdam): Equivariant and coordinate independent convolutional networks Function Space, UCL Centre for Artificial Intelligence, 1st Floor, 90 High Holborn, London WC1V 6BH
Fri, 24 Jun 2022 12:00-13:00 Rebecca Lewis (Imperial College London): Inference in high-dimensional logistic regression models with separated data Function Space, UCL Centre for Artificial Intelligence, 1st Floor, 90 High Holborn, London WC1V 6BH
Fri, 26 Aug 2022 12:00-13:00 Tom Everitt (DeepMind): Causal Foundations for Safe AI Function Space, UCL Centre for Artificial Intelligence, 1st Floor, 90 High Holborn, London WC1V 6BH
Fri, 07 Oct 2022 12:00-13:00 Joshua David Robinson (MIT): Sign and Basis Invariant Networks for Spectral Graph Representation Learning Function Space, UCL Centre for Artificial Intelligence, 1st Floor, 90 High Holborn, London WC1V 6BH
Fri, 14 Oct 2022 12:00-13:00 Wenkai Xu (University of Oxford): AgraSSt Approximate Graph Stein Statistics for Interpretable Assessment of Implicit Graph Generators Function Space, UCL Centre for Artificial Intelligence, 1st Floor, 90 High Holborn, London WC1V 6BH
Fri, 21 Oct 2022 12:00-13:00 Samory Kpotufe (Columbia University): Adaptivity in Domain Adaptation and Friends Function Space, UCL Centre for Artificial Intelligence, 1st Floor, 90 High Holborn, London WC1V 6BH
Fri, 28 Oct 2022 12:00-13:00 Nick Pawlowski & Wenbo Gong (Microsoft Research): Rhino Deep Causal Temporal Relationship Learning with history-dependent noise Function Space, UCL Centre for Artificial Intelligence, 1st Floor, 90 High Holborn, London WC1V 6BH
Fri, 04 Nov 2022 12:00-13:00 Siyuan Guo (University of Cambridge & Max Planck Institute for Intelligent Systems): Causal de Finetti: On the Identification of Invariant Causal Structure in Exchangeable Data Function Space, UCL Centre for Artificial Intelligence, 1st Floor, 90 High Holborn, London WC1V 6BH
Fri, 11 Nov 2022 12:00-13:00 Ilija Bogunovic (UCL): Robust Design Discovery and Exploration in Bayesian Optimization Function Space, UCL Centre for Artificial Intelligence, 1st Floor, 90 High Holborn, London WC1V 6BH
Fri, 18 Nov 2022 12:00-13:00 Eugenio Clerico (University of Oxford): A PAC-Bayesian bound for deterministic classifiers Function Space, UCL Centre for Artificial Intelligence, 1st Floor, 90 High Holborn, London WC1V 6BH
Fri, 25 Nov 2022 12:00-13:00 Xiaowen Dong (University of Oxford): On the stability of spectral graph filters and beyond Function Space, UCL Centre for Artificial Intelligence, 1st Floor, 90 High Holborn, London WC1V 6BH
Fri, 09 Dec 2022 12:00-13:00 Lionel Riou-Durand (University of Warwick): Adaptive Tuning for Metropolis Adjusted Langevin Trajectories Function Space, UCL Centre for Artificial Intelligence, 1st Floor, 90 High Holborn, London WC1V 6BH
Fri, 20 Jan 2023 12:00-13:00 Ricardo Silva (University College London): Stochastic Causal Programming for Bounding Treatment Effects Function Space, UCL Centre for Artificial Intelligence, 1st Floor, 90 High Holborn, London WC1V 6BH
Fri, 20 Jan 2023 12:00-13:00 Aldo Pacchiano (Microsoft Research NYC): Learning Systems in Adaptive Environments. Theory, Algorithms and Design Function Space, UCL Centre for Artificial Intelligence, 1st Floor, 90 High Holborn, London WC1V 6BH
Fri, 03 Feb 2023 12:00-13:00 Jong Chul Ye (Graduate School of Artificial Intelligence, KAIST, Daejeon, Korea): Manifold-constrained diffusion models for inverse problems in imaging Function Space, UCL Centre for Artificial Intelligence, 1st Floor, 90 High Holborn, London WC1V 6BH
Fri, 17 Feb 2023 12:00-13:00 Sam Power (University of Bristol): Explicit convergence bounds for Metropolis Markov chains Function Space, UCL Centre for Artificial Intelligence, 1st Floor, 90 High Holborn, London WC1V 6BH
Fri, 03 Mar 2023 12:00-13:00 Robin Evans (University of Oxford): Parameterizing and Simulating from Causal Models Function Space, UCL Centre for Artificial Intelligence, 1st Floor, 90 High Holborn, London WC1V 6BH
Fri, 24 Mar 2023 12:00-13:00 Tom Rainforth (University of Oxford): Modern Bayesian Experimental Design Function Space, UCL Centre for Artificial Intelligence, 1st Floor, 90 High Holborn, London WC1V 6BH
Fri, 31 Mar 2023 12:00-13:00 Pierre Alquier (ESSEC Business School Singapore): New deviation inequalities for Markov chains, with applications to stochastic optimization and empirical risk minimization Function Space, UCL Centre for Artificial Intelligence, 1st Floor, 90 High Holborn, London WC1V 6BH
Fri, 21 Apr 2023 12:00-13:00 James A. Landay (Stanford University): 'AI For Good' Isn’t Good Enough: A Call for Human-Centered AI (joint UCLIC Seminar) Function Space, UCL Centre for Artificial Intelligence, 1st Floor, 90 High Holborn, London WC1V 6BH
Fri, 28 Apr 2023 12:00-13:00 Seth Flaxman (University of Oxford): Deep generative modelling with πVAE and PriorVAE to enable scalable MCMC inference on stochastic processes Function Space, UCL Centre for Artificial Intelligence, 1st Floor, 90 High Holborn, London WC1V 6BH
Fri, 05 May 2023 12:00-13:00 Mike Walmsley (University of Manchester): Practical Deep Learning at Galaxy Zoo Function Space, UCL Centre for Artificial Intelligence, 1st Floor, 90 High Holborn, London WC1V 6BH
Fri, 12 May 2023 12:00-13:00 Alexander Terenin (University of Cambridge): Physically Structured Neural Networks for Smooth and Contact Dynamics Function Space, UCL Centre for Artificial Intelligence, 1st Floor, 90 High Holborn, London WC1V 6BH
Fri, 26 May 2023 12:00-13:00 Daniel Mannion (University College London): Dendritic Computation: The What, The Why & The How? Function Space, UCL Centre for Artificial Intelligence, 1st Floor, 90 High Holborn, London WC1V 6BH
Fri, 02 Jun 2023 12:00-13:00 Mark van der Wilk (Imperial College London): Bivariate Causal Discovery using Bayesian Model Selection Function Space, UCL Centre for Artificial Intelligence, 1st Floor, 90 High Holborn, London WC1V 6BH
Fri, 09 Jun 2023 12:00-13:00 Fabio De Sousa Ribeiro (Imperial College London): High Fidelity Image Counterfactuals with Probabilistic Causal Models Function Space, UCL Centre for Artificial Intelligence, 1st Floor, 90 High Holborn, London WC1V 6BH
Fri, 16 Jun 2023 12:00-13:00 Abhin Shah (MIT): On counterfactual inference with unobserved confounding Function Space, UCL Centre for Artificial Intelligence, 1st Floor, 90 High Holborn, London WC1V 6BH
Fri, 23 Jun 2023 12:00-13:00 Sattar Vakili (MediaTek Research): Kernel-based Reinforcement Learning Function Space, UCL Centre for Artificial Intelligence, 1st Floor, 90 High Holborn, London WC1V 6BH
Fri, 29 Sep 2023 12:00-13:00 Marina Riabiz (King's College London): Optimal Thinning of MCMC Output Function Space, UCL Centre for Artificial Intelligence, 1st Floor, 90 High Holborn, London WC1V 6BH
Fri, 06 Oct 2023 12:00-13:00 Peter Orbanz (Gatsby Computational Neuroscience Unit UCL): Learning functions with crystallographic symmetries Function Space, UCL Centre for Artificial Intelligence, 1st Floor, 90 High Holborn, London WC1V 6BH
Fri, 13 Oct 2023 12:00-13:00 Anastasia Mantziou (Alan Turing Institute): Bayesian model-based clustering for populations of network data Function Space, UCL Centre for Artificial Intelligence, 1st Floor, 90 High Holborn, London WC1V 6BH
Fri, 20 Oct 2023 12:00-13:00 Petros Dellaportas (University College London): Can independent Metropolis samplers beat Monte Carlo? Function Space, UCL Centre for Artificial Intelligence, 1st Floor, 90 High Holborn, London WC1V 6BH
Fri, 27 Oct 2023 12:00-13:00 Yu Luo (King's College London): Bayesian estimation using loss functions Function Space, UCL Centre for Artificial Intelligence, 1st Floor, 90 High Holborn, London WC1V 6BH
Fri, 03 Nov 2023 12:00-13:00 José Miguel Hernández Lobato (University of Cambridge): Normalizing Flows for Molecular Modeling Function Space, UCL Centre for Artificial Intelligence, 1st Floor, 90 High Holborn, London WC1V 6BH
Fri, 10 Nov 2023 12:00-13:00 Jonas Peters (ETH Zurich): Instrumental Time Series and Effect-Invariance for Policy Generalization Function Space, UCL Centre for Artificial Intelligence, 1st Floor, 90 High Holborn, London WC1V 6BH
Fri, 17 Nov 2023 12:00-13:00 Rajen Shah (University of Cambridge): Rank-transformed subsampling: Inference for multiple data splitting and exchangeable p-values Function Space, UCL Centre for Artificial Intelligence, 1st Floor, 90 High Holborn, London WC1V 6BH
Fri, 24 Nov 2023 12:00-13:00 Alberto Caron (Alan Turing Institute): Bayesian Structure Learning with Random Neighbourhood Samplers Function Space, UCL Centre for Artificial Intelligence, 1st Floor, 90 High Holborn, London WC1V 6BH
Fri, 08 Mar 2024 12:00-13:00 Qingyuan Zhao (University of Cambridge): Design: The Missing Concept in AI Function Space, UCL Centre for Artificial Intelligence, 1st Floor, 90 High Holborn, London WC1V 6BH
Fri, 15 Mar 2024 12:00-13:00 Francesco Quinzan (University of Oxford): Trustworthy AI: Exploring Causality and Generative Models for Better-Informed Predictions Function Space, UCL Centre for Artificial Intelligence, 1st Floor, 90 High Holborn, London WC1V 6BH
Fri, 22 Mar 2024 12:00-13:00 Aleksandar Botev (Google DeepMind): Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models Function Space, UCL Centre for Artificial Intelligence, 1st Floor, 90 High Holborn, London WC1V 6BH
Fri, 03 May 2024 12:00-13:00 Kostas Margellos (University of Oxford): Optimization under the lens of compression learning: Trading feasibility to performance Function Space, UCL Centre for Artificial Intelligence, 1st Floor, 90 High Holborn, London WC1V 6BH

Past Events

Date Description
Fri, 03 May 2024 Kostas Margellos (University of Oxford): Optimization under the lens of compression learning: Trading feasibility to performance
Fri, 22 Mar 2024 Aleksandar Botev (Google DeepMind): Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models
Fri, 15 Mar 2024 Francesco Quinzan (University of Oxford): Trustworthy AI: Exploring Causality and Generative Models for Better-Informed Predictions
Fri, 08 Mar 2024 Qingyuan Zhao (University of Cambridge): Design: The Missing Concept in AI
Fri, 24 Nov 2023 Alberto Caron (Alan Turing Institute): Bayesian Structure Learning with Random Neighbourhood Samplers
Fri, 17 Nov 2023 Rajen Shah (University of Cambridge): Rank-transformed subsampling: Inference for multiple data splitting and exchangeable p-values
Fri, 10 Nov 2023 Jonas Peters (ETH Zurich): Instrumental Time Series and Effect-Invariance for Policy Generalization
Fri, 03 Nov 2023 José Miguel Hernández Lobato (University of Cambridge): Normalizing Flows for Molecular Modeling
Fri, 27 Oct 2023 Yu Luo (King's College London): Bayesian estimation using loss functions
Fri, 20 Oct 2023 Petros Dellaportas (University College London): Can independent Metropolis samplers beat Monte Carlo?
Fri, 13 Oct 2023 Anastasia Mantziou (Alan Turing Institute): Bayesian model-based clustering for populations of network data
Fri, 06 Oct 2023 Peter Orbanz (Gatsby Computational Neuroscience Unit UCL): Learning functions with crystallographic symmetries
Fri, 29 Sep 2023 Marina Riabiz (King's College London): Optimal Thinning of MCMC Output
Fri, 23 Jun 2023 Sattar Vakili (MediaTek Research): Kernel-based Reinforcement Learning
Fri, 16 Jun 2023 Abhin Shah (MIT): On counterfactual inference with unobserved confounding
Fri, 09 Jun 2023 Fabio De Sousa Ribeiro (Imperial College London): High Fidelity Image Counterfactuals with Probabilistic Causal Models
Fri, 02 Jun 2023 Mark van der Wilk (Imperial College London): Bivariate Causal Discovery using Bayesian Model Selection
Fri, 26 May 2023 Daniel Mannion (University College London): Dendritic Computation: The What, The Why & The How?
Fri, 12 May 2023 Alexander Terenin (University of Cambridge): Physically Structured Neural Networks for Smooth and Contact Dynamics
Fri, 05 May 2023 Mike Walmsley (University of Manchester): Practical Deep Learning at Galaxy Zoo
Fri, 28 Apr 2023 Seth Flaxman (University of Oxford): Deep generative modelling with πVAE and PriorVAE to enable scalable MCMC inference on stochastic processes
Fri, 21 Apr 2023 James A. Landay (Stanford University): 'AI For Good' Isn’t Good Enough: A Call for Human-Centered AI (joint UCLIC Seminar)
Fri, 31 Mar 2023 Pierre Alquier (ESSEC Business School Singapore): New deviation inequalities for Markov chains, with applications to stochastic optimization and empirical risk minimization
Fri, 24 Mar 2023 Tom Rainforth (University of Oxford): Modern Bayesian Experimental Design
Fri, 03 Mar 2023 Robin Evans (University of Oxford): Parameterizing and Simulating from Causal Models
Fri, 17 Feb 2023 Sam Power (University of Bristol): Explicit convergence bounds for Metropolis Markov chains
Fri, 03 Feb 2023 Jong Chul Ye (Graduate School of Artificial Intelligence, KAIST, Daejeon, Korea): Manifold-constrained diffusion models for inverse problems in imaging
Fri, 20 Jan 2023 Aldo Pacchiano (Microsoft Research NYC): Learning Systems in Adaptive Environments. Theory, Algorithms and Design
Fri, 20 Jan 2023 Ricardo Silva (University College London): Stochastic Causal Programming for Bounding Treatment Effects
Fri, 09 Dec 2022 Lionel Riou-Durand (University of Warwick): Adaptive Tuning for Metropolis Adjusted Langevin Trajectories
Fri, 25 Nov 2022 Xiaowen Dong (University of Oxford): On the stability of spectral graph filters and beyond
Fri, 18 Nov 2022 Eugenio Clerico (University of Oxford): A PAC-Bayesian bound for deterministic classifiers
Fri, 11 Nov 2022 Ilija Bogunovic (UCL): Robust Design Discovery and Exploration in Bayesian Optimization
Fri, 04 Nov 2022 Siyuan Guo (University of Cambridge & Max Planck Institute for Intelligent Systems): Causal de Finetti: On the Identification of Invariant Causal Structure in Exchangeable Data
Fri, 28 Oct 2022 Nick Pawlowski & Wenbo Gong (Microsoft Research): Rhino Deep Causal Temporal Relationship Learning with history-dependent noise
Fri, 21 Oct 2022 Samory Kpotufe (Columbia University): Adaptivity in Domain Adaptation and Friends
Fri, 14 Oct 2022 Wenkai Xu (University of Oxford): AgraSSt Approximate Graph Stein Statistics for Interpretable Assessment of Implicit Graph Generators
Fri, 07 Oct 2022 Joshua David Robinson (MIT): Sign and Basis Invariant Networks for Spectral Graph Representation Learning
Fri, 26 Aug 2022 Tom Everitt (DeepMind): Causal Foundations for Safe AI
Fri, 24 Jun 2022 Rebecca Lewis (Imperial College London): Inference in high-dimensional logistic regression models with separated data
Fri, 17 Jun 2022 Maurice Weiler (University of Amsterdam): Equivariant and coordinate independent convolutional networks
Fri, 10 Jun 2022 Badr-Eddine Chérief-Abdellatif (University of Oxford): Robust Estimation via Maximum Mean Discrepancy
Fri, 27 May 2022 Daniel Paulin (University of Edinburgh): Efficient MCMC Sampling with Dimension-Free Convergence Rate using ADMM-type Splitting
Fri, 13 May 2022 Harita Dellaporta (University of Warwick): Robust Bayesian Inference for Simulator-based Models via the MMD Posterior Bootstrap
Fri, 06 May 2022 Alexander Terenin (Cambridge University): Non-Euclidean Matérn Gaussian Processes
Fri, 29 Apr 2022 Siu Lun (Alan) Chau (Oxford University): Explaining Kernel Methods with RKHS-SHAP
Fri, 08 Apr 2022 Manfred Warmuth (Google Brain (formerly University of California at Santa Cruz)): The blessing and the curse of the multiplicative updates - discusses connections between in evolution and the multiplicative updates of online learning
Fri, 01 Apr 2022 Julien Mairal (Inria Grenoble, Thoth team): Lucas-Kanade Reloaded End-to-End Super-Resolution from Raw Image Bursts
Fri, 25 Mar 2022 Stefano Ermon (Stanford University): Utilitarian Information Theory
Fri, 18 Mar 2022 Alexandre Gramfort (Inria Parietal Team): Machine Learning without human supervision on neuroscience signals
Fri, 11 Mar 2022 Emtiyaz Khan (Tokyo RIKEN center for Advanced Intelligence Project (AIP)): The Bayesian Learning Rule for Adaptive AI
Fri, 25 Feb 2022 Hossein Mobahi (Google): Sharpness-Aware Minimization (SAM) Current Method and Future Directions
Fri, 04 Feb 2022 Stéphanie Allassonnière (Paris Descartes University): Data Augmentation in High Dimensional Low Sample Size Setting Using a Geometry-Based Variational Autoencoder
Fri, 14 Jan 2022 Richard Samworth (Cambridge University): Optimal Subgroup Selection
Fri, 17 Dec 2021 Makoto Yamada (Kyoto University and RIKEN AIP center): Selective inference with Kernels
Fri, 03 Dec 2021 Ting Chen (Google Brain): Contrastive Self-Supervised Learning and Potential Limitations
Fri, 12 Nov 2021 Mark Herbster (University College London): Online Multitask Learning with Long-Term Memory
Fri, 26 Feb 2021 Greg Yang (Microsoft Research): Feature Learning in Infinite-Width Neural Networks
Fri, 19 Feb 2021 Chelsea Finn (Stanford University): Principles for Tackling Distribution Shift: Pessimism, Adaptation, and Anticipation
Fri, 12 Feb 2021 David Duvenaud (University of Toronto): Latent Stochastic Differential Equations: An Unexplored Model Class.
Fri, 05 Feb 2021 Mihaela van der Schaar (University of Cambridge): Why medicine is creating exciting new frontiers for machine learning
Fri, 29 Jan 2021 Guido Montufar (Max Planck Institute for Mathematics in the Sciences): Implicit bias of gradient descent for mean squared error regression with wide neural networks
Fri, 22 Jan 2021 Marta Garnelo (DeepMind): Meta-Learning and Neural Processes
Fri, 15 Jan 2021 Jakob Foerster (Facebook): Zero-Shot (Human-AI) Coordination (in Hanabi) and Ridge Rider
Fri, 08 Jan 2021 Sergey Levine (UC Berkeley): Data-Driven Reinforcement Learning: Deriving Common Sense from Past Experience
Fri, 18 Dec 2020 Luigi Gresele & Giancarlo Fissore (MPI for Intelligent Systems & Inria Paris-Saclay): Relative gradient optimization of the Jacobian term in unsupervised deep learning
Fri, 04 Dec 2020 Jonathan Frankle (MIT): The Lottery Ticket Hypothesis: On Sparse, Trainable Neural Networks
Fri, 27 Nov 2020 Alexey Dosovitskiy (Google Brain): Non-convolutional architectures for recognition and generation
Thu, 09 Apr 2020 Maurice Weiler (University of Amsterdam): Equivariant Neural Networks
Thu, 27 Feb 2020 Aude Genevay (MIT): Learning with entropy-regularized optimal transport
Fri, 10 Jan 2020 Catalina Cangea (University of Cambridge): Question Answering in Realistic Visual Environments: Challenges and Approaches
Thu, 28 Nov 2019 Claire Vernade (DeepMind): NeurIPS Previews 2019
Fri, 15 Nov 2019 Varun Kanade (University of Oxford): Implicit Regularization for Optimal Sparse Recovery
Fri, 11 Oct 2019 Arthur Mensch (Ecole Normale Superieure (ENS) Paris): Geometric Losses for Distributional Learning
Fri, 28 Jun 2019 Benjamin Guedj (UCL - INRIA): A primer on PAC-Bayesian learning with applications to deep neural networks
Fri, 21 Jun 2019 Marc Deisenroth (Imperial College): Faster Learning and Richer Models for the Next AI Challenges
Fri, 07 Jun 2019 Victor Prisacariu (University of Oxford): (Deep-ish) SLAM for Next Generation AR
Thu, 30 May 2019 Quentin Berthet (University of Cambridge): Optimal transport methods in statistics and machine learning: theory and applications
Thu, 09 May 2019 Umut Şimşekli (Télécom ParisTech): Nonparametric Generative Modeling via Optimal Transport and Diffusions with Provable Guarantees
Fri, 26 Apr 2019 Nicolas Anastassacos (UCL): Investigating the Emergence of Cooperative Behaviour for Artificial Societies with RL
Fri, 08 Mar 2019 Sanjeevan Ahilan (UCL, Gatsby): Feudal Multi-Agent Hierarchies for Cooperative Reinforcement Learning
Fri, 01 Mar 2019 Sander Dieleman (DeepMind): Generating music in the raw audio domain
Fri, 22 Feb 2019 Tengyao Wang (UCL): Sparse PCA: statistical and computational trade-offs
Fri, 01 Feb 2019 Ozan Öktem (Cambridge University/KTH): Deep learning for Bayesian inverse problems from tomography
Fri, 25 Jan 2019 Dino Sejdinovic (University of Oxford): Learning on Aggregate Outputs with Kernels
Fri, 18 Jan 2019 Sesh Kumar (Imperial College): Differentially Private Empirical Risk Minimization with Sparsity-Inducing Norms
Fri, 11 Jan 2019 Jean-Baptiste Alayrac (DeepMind): Weakly Supervised Learning from Videos
Fri, 14 Dec 2018 Ricardo Pio Monti (UCL (Gatsby)): Causal discovery with general non-linear relationships using non-linear ICA
Fri, 30 Nov 2018 Artur Garcez (City University): Logic Tensor Networks: A System for Deep Learning with Symbolic Reasoning
Fri, 23 Nov 2018 Various (UCL, DeepMind, Imperial): NIPS Previews
Fri, 16 Nov 2018 Kayvan Sadeghi (UCL): Probabilistic Independence, Graphs, and Random Networks
Fri, 19 Oct 2018 Kai Arulkumaran (Imperial College): Tutorial on Deep RL
Fri, 08 Jun 2018 Edward Grefenstette (DeepMind): Learning to follow grounded language instructions in the "real" world
Fri, 01 Jun 2018 Piotr Mirowski (DeepMind): Learning to Navigate
Fri, 25 May 2018 Marco Cuturi (CREST-ENSAE/Université Paris-Saclay): Regularization for Optimal Transport and Dynamic Time Warping Distances
Fri, 27 Apr 2018 Stefanos Zafeiriou (Imperial College London): Discovering correlations in the modern era: robust and deep learning approaches
Fri, 20 Apr 2018 Lucas Theis (Twitter): Evaluating generative models
Fri, 13 Apr 2018 Seth Flaxman (Imperial College): Predictor Variable Prioritization in Nonlinear Models: A Genetic Association Case Study
Fri, 09 Mar 2018 Sam Livingstone (UCL Statistical Science): What we talk about when we talk about non-reversible MCMC
Fri, 23 Feb 2018 Ricardo Silva (UCL Statistical Science): Some Machine Learning Tools to Aid Causal Inference
Fri, 16 Feb 2018 Zbigniew Wojna (UCL): Architectures for big scale 2D imagery
Fri, 09 Feb 2018 Sebastian Nowozin (Microsoft Research): Opportunities and Challenges in Generative Adversarial Networks – Looking beyond the Hype
Fri, 02 Feb 2018 Maria Lomeli (University of Cambridge): Kernel Monte Carlo estimators for partial rankings
Fri, 26 Jan 2018 Edouard Oyallon (CentraleSupelec): Invariance & invertibility in CNNs
Fri, 12 Jan 2018 Tamara Fernandez (UCL Gatsby): A Gaussian process model for survival analysis
Fri, 15 Dec 2017 Dougal Sutherland (UCL, Gatsby): Efficient score estimation with infinite-dimensional exponential families
Fri, 08 Dec 2017 Relja Arandjelovic (DeepMind): Look, Listen and Learn
Fri, 01 Dec 2017 Yingzhen Li (University of Cambridge): Wild approximate inference: why and how
Fri, 24 Nov 2017 Various (UCL): NIPS Accepted Papers
Fri, 17 Nov 2017 Mark van der Wilk (University of Cambridge): Convolutional Gaussian processes
Fri, 03 Nov 2017 Aapo Hyvarinen (UCL, Gatsby): Nonlinear ICA using temporal structure: a principled framework for unsupervised deep learning
Fri, 20 Oct 2017 Hugh Salimbeni (Imperial College London): Doubly Stochastic Variational Inference for Deep Gaussian Processes
Fri, 27 Jan 2017 Mijung Park (Amsterdam Machine Learning Lab): Variational Bayes In Private Settings (VIPS)
Fri, 02 Dec 2016 Various (UCL): NIPS Previews
Fri, 25 Nov 2016 Theo Trouillon (Xerox Research, Univ. Grenoble Alpes): Complex-Valued Embeddings for Knowledge Base Completion
Fri, 18 Nov 2016 Ryota Tomioka (Microsoft Research Cambridge): f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization
Fri, 11 Nov 2016 Daniel Tarlow (Microsoft Research Cambridge): Learning to Code: Machine Learning for Program Induction
Fri, 04 Nov 2016 Remi Munos (Google DeepMind): Safe and efficient off-policy reinforcement learning
Fri, 28 Oct 2016 Shakir Mohamed (Google DeepMind): Building Machines that Imagine and Reason: Principles and Applications of Deep Generative Models
Wed, 26 Oct 2016 Hrishi Aradhye (Google Research/ Google Play): Personalized app/games recommendations on Google Play using machine learning
Wed, 19 Oct 2016 Iasonas Kokkinos (UCL): Deeplab to UberNet: from task-specific to task-agnostic deep learning in computer vision
Wed, 29 Jun 2016 Wolfgang Gatterbauer (CMU): Approximate lifted inference with probabilistic databases
Fri, 10 Jun 2016 Andrew Fitzgibbon (Microsoft Research Cambridge): Lifting, VarPro, ICP, and all that.
Wed, 01 Jun 2016 Yee-Whye Teh (University of Oxford): Distributed Bayesian Learning
Fri, 06 May 2016 Ted Meeds (University of Amsterdam): Likelihood-free Inference by Controlling Simulator Noise
Fri, 22 Apr 2016 Ingmar Schuster (Université Paris-Dauphine): Kernel Sequential Monte Carlo
Fri, 15 Apr 2016 Chris Oates (University of Technology Sydney): Stein Operators on Hilbert Spaces
Thu, 24 Mar 2016 David Silver (Google DeepMind, University College London): AlphaGO: Mastering the game of Go with deep neural networks and tree search
Fri, 18 Mar 2016 Emtiyaz Khan (EPFL): Approximate Bayesian Inference: Bringing Statistics, Optimization, and Machine Learning Together.
Fri, 11 Mar 2016 Francois-Xavier Briol (University of Warwick): Probabilistic Numerics Approaches to Integration
Fri, 04 Mar 2016 James Hensman (Lancaster University): Variational Inference in Gaussian Process Models
Fri, 19 Feb 2016 Thore Graepel (DeepMind, University College London): DeepMind's Quest for Artificial General Intelligence: From Atari to AlphaGo and beyond
Fri, 04 Dec 2015 Stephen Pasteris, Wittawat Jitkrittum, Ricardo Silver (UCL): NIPS previews
Fri, 27 Nov 2015 Tom Schaul (Google Deepmind): Universal Value Function Approximators
Fri, 20 Nov 2015 Shakir Mohamed (Google Deepmind): Memory-based Bayesian Reasoning with Deep Learning
Fri, 13 Nov 2015 Yarin Gal (University of Cambridge): Modern Deep Learning through Bayesian Eyes
Fri, 16 Oct 2015 Roberto Calandra (TU Darmstadt): Bayesian Modeling for Optimization and Control in Robotics
Fri, 28 Aug 2015 Elad Hazan (Princeton University): Classification with Low Rank and Missing Data
Fri, 24 Jul 2015 Ribana Roscher (Freie Universität Berlin): Discriminative and Reconstructive Methods for Classification of Remote Sensing Images
Mon, 29 Jun 2015 Manik Varma (Microsoft Research India): Extreme Classification: A New Paradigm for Ranking & Recommendation
Fri, 29 May 2015 Javier Gonzalez (University of Sheffield): Batch Bayesian Optimization via Local Penalization
Fri, 15 May 2015 Zhenwen Dai (University of Sheffield): Variational Hierarchical Community of Experts
Fri, 08 May 2015 Patrick Conrad (University of Warwick): Probability Measures on Numerical Solutions of ODEs and PDEs for Uncertainty Quant. and Inference
Fri, 17 Apr 2015 Csaba Szepesvari (University of Alberta, Canada): Optimistic Algorithms for Online Learning in Structured Decision Problems
Fri, 10 Apr 2015 Seppo Virtanen (University of Warwick): Non-parametric Bayes to infer playing strategies adopted in a population of mobile gamers
Fri, 27 Mar 2015 Adrian Weller (University of Cambridge): Recent results on the Bethe approximation
Fri, 20 Mar 2015 Iain Murray (Edinburgh University): Flexible and deep models for density estimation
Fri, 13 Mar 2015 Peter Sollich (King's College, University Of London): Gaussian process regression on graphs
Fri, 06 Mar 2015 Heiko Strathmann (covering co-author Mark Girolami who cannot make it) (Gatsby Unit, UCL): Unbiased Bayes for Big Data: Paths of Partial Posteriors
Wed, 04 Mar 2015 Jason Weston (Facebook, New York): Memory Networks
Fri, 27 Feb 2015 Matt Hoffmann (University Of Cambridge): Predictive Entropy Search
Fri, 06 Feb 2015 Peter Tino (University of Birmingham): Learning from Temporal Data Using Dynamical Feature Space
Fri, 23 Jan 2015 Vladimir Vovk (Royal Holloway University of London): Probabilistic prediction in machine learning
Fri, 16 Jan 2015 Kamil Ciosek (UCL, Computer Science): Combining state abstraction and temporal abstraction in MDP solving
Fri, 05 Dec 2014 Andrew McDonald, Kacper Chwialkowski, Balaji Lakshminarayanan (UCL/Gatsby): NIPS Previews
Fri, 28 Nov 2014 Stephen Roberts (Oxford University): Planets, Pulsars, People and Petabytes: Explorations of Machine Learning in Astronomy
Fri, 21 Nov 2014 Amos Storkey (Edinburgh University): Series Expansion Methods for Approximate Learning, Filtering and Smoothing in Diffusions
Fri, 14 Nov 2014 Ata Kaban (University of Birmingham): Learning with random projections
Fri, 07 Nov 2014 Chris Williams (Edinburgh University): Switching Linear Dynamical Systems for Condition Monitoring in the Intensive Care Unit
Fri, 17 Oct 2014 Peter Flach (University of Bristol): Comparing apples and oranges -- reinterpreting common evaluation metrics in classification
Fri, 12 Sep 2014 Remi Munos (INRIA Lille): Two generic principles in modern bandits: the optimistic principle and Thompson sampling
Thu, 19 Jun 2014 Gerhard Neumann (TU Darmstadt): Learning Modular Control Policies in Robotics
Fri, 13 Jun 2014 David Barber, Kacper Chwiałkowski, Dino Sejdinovic (UCL): ICML preview talks
Fri, 23 May 2014 Remi Bardenet (Deptartment of Statistics, Oxford): Scaling up MCMC: a subsampling approach
Fri, 16 May 2014 Chris Watkins (Royal Holloway University of London): Evolution as a standard Monte-Carlo algorithm
Fri, 09 May 2014 Demis Hassabis (Google DeepMind): General Artificial Intelligence
Fri, 02 May 2014 Zoltan Szabo (UCL, Gatsby): Distribution Regression - the Set Kernel Heuristic is Consistent
Fri, 25 Apr 2014 Alex Graves (Google Deepmind): Generating Sequences with Recurrent Neural Networks
Fri, 11 Apr 2014 Dimitrios Athanasakis (UCL): Principled Non-Linear Feature Selection (with applications in representation learning)
Fri, 04 Apr 2014 Sarah Chisholm (UCL, Computer Science): Statistical Methods for Analysing Time Series Data of Animal Movement
Fri, 21 Mar 2014 Lloyd Elliott (UCL, Gatsby): Bayesian nonparametric dynamic-clustering and genetic imputation
Fri, 14 Mar 2014 Srini Turaga (Gatsby Unit & Wolfson Institute for Biomedical Research): Using ConvNets, MALIS and crowd-sourcing to map the retinal connectome.
Fri, 07 Mar 2014 Robert Stojnic (Cambridge Systems Biology Centre): Bayesian Molecular LEGO
Fri, 28 Feb 2014 Trevor Cohn (University of Sheffield): Language processing using Gaussian Processes
Fri, 21 Feb 2014 Marc Deisenroth (Imperial College, London): Statistical Machine Learning for Autonomous Systems
Fri, 14 Feb 2014 Peter Forbes (Oxford, Department of Statistics): Quantifying Fingerprint Evidence using Bayesian Alignment
Fri, 24 Jan 2014 Ioanna Manolopoulou (UCL, Statistics): Diffusion modelling of motion trajectories under the influence of covariates
Fri, 10 Jan 2014 Stephen Pasteris (UCL, Computer Science): Online Similarity Prediction of Networked Data
Fri, 29 Nov 2013 Alfredo Kalaitzis, Bernadino Romero Paredes, Dino Sejdinovic (UCL): NIPS preview talks
Fri, 22 Nov 2013 Mario Marchand (Universite Laval): Risk Bounds and Learning Algorithms for the Regression Approach to Structured Output Prediction
Fri, 15 Nov 2013 Thore Graepel (Microsoft Research Cambridge and Chair of Machine Learning, Department of Computer Science, UCL): Private traits and attributes are predictable from digital records of human behavior
Fri, 01 Nov 2013 Sam Livingstone (UCL, Statistics): Diffusions with position-dependent volatility and the Metropolis-adjusted Langevin algorithm
Fri, 10 May 2013 Isadora Antoniano-Villalobos (Department of Decision Sciences, Bocconi University, Italy): Bayesian inference for nonparametric mixture models with intractable normalizing constants
Fri, 26 Apr 2013 Chris Bracegirdle (UCL (CS)): Probabilistic Inference for Changepoints and Cointegration
Fri, 05 Apr 2013 Robert Jenssen (University of Tromso, Norway): Entropy-Relevant Dimensions in Kernel Feature Space
Fri, 22 Mar 2013 Vladimir Krylov (UCL): Extraction of geometrical objects from images with MCMC methods
Fri, 08 Mar 2013 David Silver (UCL): Reinforcement Learning and Simulation-Based Search
Fri, 01 Mar 2013 Tamara Broderick (University of California, Berkeley): Feature allocations, probability functions, and paintboxes
Fri, 22 Feb 2013 Matthew Higgs (UCL): A Population Approach to Ubicomp System Design (APAUSD)
Fri, 08 Feb 2013 Gary Macindoe (UCL): A hybrid Cholesky decomposition algorithm for multicore CPUs with GPU accelerators
Fri, 25 Jan 2013 Andriy Mnih (UCL): A fast and simple algorithm for training neural probabilistic language models
Fri, 11 Jan 2013 Ed Challis (UCL): Variational approximate inference in linear latent variable models
Thu, 29 Nov 2012 Juan Carlos Martinez-Ovando (Banco de México): Non- and semi-parametric construction of stationary dependent models
Fri, 23 Nov 2012 Ben Calderhead and Simon Byrne (UCL): The use of geometry in MCMC
Fri, 09 Nov 2012 Steffen Grunewalder (UCL): Conditional Expectation Estimates for Discrete Control
Fri, 26 Oct 2012 Dino Sejdinovic (UCL): Equivalence of distance-based and RKHS-based statistics in hypothesis testing
Fri, 12 Oct 2012 Jan Gasthaus (UCL): Hierarchical Bayesian Nonparametric Models for Sequences
Fri, 28 Sep 2012 Janaina Mourao-Miranda, Jane Maryam Rondina, Maria Joao Rosa (UCL): Machine learning approaches for clinical neuroimaging data
Fri, 13 Jul 2012 Vinayak Rao (UCL): Efficient MCMC for Continuous Time Discrete State Systems
Mon, 02 Jul 2012 Yuan (Alan) Qi (Purdue University): Bayesian learning with big data: virtual vector machines and Gaussian processes with sparse eigenval
Fri, 22 Jun 2012 Adam Sykulski (UCL): Statistical modelling and estimation of physical phenomena in ocean surface trajectories
Tue, 12 Jun 2012 Shivani Lamba, (Founder/CEO of Chechako) and Marshall Levine, (Wise Counsel for Chechako Ltd) (Multiple): Startup Pitch
Mon, 11 Jun 2012 Larry Wasserman (Carnegie Mellon University): Discussion
Fri, 25 May 2012 Gabi Teodoru (UCL): Spectral Learning of Latent Variable Models and its Interpretation as an Optimization Problem
Fri, 11 May 2012 Tom Furmston (UCL): Gradient-based algorithms for policy search
Fri, 27 Apr 2012 Mark Girolami (UCL): Riemann Manifold Langevin and Hamiltonian Monte Carlo Methods