Research seminars
Our virtual seminar series is where we exchange ideas with guest speakers, keeping you up to date with the latest developments and inspiring research topics. Occasionally, Secondmind researchers present their own work as well.
Past seminars
Antonio Del Rio Chanona - Multi-Fidelity Bayesian Optimization in Chemical Engineering
July 6, 2023 - Antonio Del Rio Chanona - Imperial College London
Luigi Nardi - Harnessing new information in Bayesian optimization
June 7, 2023 - Luigi Nardi - Lund University, Stanford University and DBtune (www.dbtune.ai)
Christopher Nemeth - Coin Sampling: Gradient-Based Bayesian Inference without Learning Rates
February 23, 2023 - Christopher Nemeth - Lancaster University.
David K. Duvenaud - A farewell to GPs
December 14, 2022 - David K. Duvenaud - University of Toronto.
Ítalo Gomes Gonçalves - Variational Gaussian processes for spatial modeling: the geoML project
November 23, 2022 - Ítalo Gomes Gonçalves - Universidade Federal do Pampa
Martin Jørgensen - Bézier Gaussian Processes
November 10, 2022 - Martin Jørgensen - University of Oxford
Barbara Rakitsch - Interacting ODEs with Gaussian Processes
October 6, 2022 - Barbara Rakitsch - Bosch AI
Sebastian Farquhar - Unbiased Active Learning and Testing
September 16, 2022 - Sebastian Farquhar - University of Oxford
Pablo Moreno-Muñoz - Model Recycling with Gaussian Processes
June 23, 2022 - Pablo Moreno-Muñoz - Technical University of Denmark
Aryan Deshwal - Bayesian Optimization over Combinatorial Structures
May 26, 2022 - Aryan Deshwal - Washington State University.
François-Xavier Briol - Bayesian Estimation of Integrals: A Multi-task Approach
January 6, 2021 - François-Xavier Briol - Lecturer in the Department of Statistical Science at University College London, Group Leader at The Alan Turing Institute
Dino Sejdinovic - Developments at the Interface Between Kernel Embeddings and Gaussian Processes
December 2, 2021 - Dino Sejdinovic - Associate Professor at the Department of Statistics, University of Oxford, a Fellow of Mansfield College, Oxford, and a Turing Fellow of the Alan Turing Institute
Noémie Jaquier - Bayesian optimization on Riemannian manifolds for robot learning
November 25, 2021 - Noémie Jaquier - Postdoctoral researcher at Karlsruhe Institute of Technology
François Bachoc - Sequential construction and dimension reduction of GP under inequality constraints
November 25, 2021 - François Bachoc - Assistant professor in statistics at the Toulouse Mathematics Institute and at the University Paul Sabatier
Frank Hutter - Towards Deep Learning 2.0: Going to the Meta-Level
November 11, 2021 - Frank Hutter - Professor of Computer Science at the University of Freiburg
Javier González Hernández - Causal Bayesian Optimisation
October 21, 2021 - Javier González Hernández - Microsoft Research, Cambridge
Roberto Calandra - Bayesian Optimization for Robotics
October 14, 2021 - Roberto Calandra - Research Scientist at Facebook AI Research
Emtiyaz Khan - Bayesian Principles for Learning-Machines
September 17, 2021 - Emtiyaz Khan - Team leader at the RIKEN center for Advanced Intelligence Project (AIP) in Tokyo
Ciara Pike-Burke - A unifying view of optimism in episodic reinforcement learning
September 2, 2021 - Ciara Pike-Burke - Lecturer in Statistics at Imperial College London
José Miguel Hernández Lobato - Probabilistic Methods for Increased Robustness in Machine Learning
July 15, 2021 - José Miguel Hernández Lobato - University Lecturer in Machine Learning at the Department of Engineering in the University of Cambridge, UK
Carl Henrik Ek - Modulating surrogates for bayesian optimization
June 10, 2021 - Carl Henrik Ek - Senior Lecturer in the Computer Laboratory at the University of Cambridge, UK, and a Docent in Machine Learning at the Royal Institute of Technology, Sweden
Peter Stone - Efficient Robot Skill Learning
May 13, 2021 - Peter Stone - Professor, University of Texas, Austin; Executive Director, Sony AI America
Laurence Aitchison - Deep Kernel Processes
March 4, 2021 - Laurence Aitchison - Senior Lecturer, Computational Neuroscience Unit, University of Bristol
Andrew G. Wilson - How do we build models that learn and generalize?
January 21, 2021 - Andrew G. Wilson - Assistant Professor, Courant Institute of Mathematical Sciences and Center for Data Science, New York University
Vincent Adam - Sparse methods for markovian GPs
January 14, 2021 - Vincent Adam - Senior Machine Learning Researcher, Secondmind; Postdoctoral researcher, Aalto University
M. E. Taylor - Reinforcement Learning in the real world: How to “cheat” and still feel good about it
December 17, 2020 - Matthew E. Taylor - Associate Professor, Department of Computing Science, Director at The Intelligence Robot Learning Laboratory, University of Alberta
Arthur Guez - Value-driven Hindsight Modelling
November 19, 2020 - Arthur Guez - Google DeepMind
Alexandra Gessner - Integration for and as Bayesian inference
November 12, 2020 - Alexandra Gessner - Ph.D. candidate, Probabilistic Numerics group, The Max Planck Institute for Intelligent Systems in Tübingen
Arno Solin - Stationary Activations for Uncertainty Calibration in Deep Learning
October 29, 2020 - Arno Solin - Assistant Professor in Machine Learning, Department of Computer Science, Aalto University
Siddharth Reddy - Assisting Human Perception and Control using Theory of Mind -
October 22, 2020 - Siddharth Reddy - Ph.D. candidate, Berkeley Artificial Intelligence Research Lab, University of California, Berkeley
Peter Frazier - Knowledge Gradient Methods for Bayesian Optimization
October 8, 2020 - Peter Frazier - Associate Professor of Operations & Information Engineering, Cornell University; Staff Data Scientist, Uber
Gabriel Dulac-Arnold - Challenges of Real-world RL: Definition, Implementation, Analysis
October 1, 2020 - Gabriel Dulac-Arnold - Researcher at Google Research
Philipp Hennig - Computation under Uncertainty
September 24, 2020 - Philipp Hennig - Professor for the Methods of Machine Learning, University of Tuebingen; Adjunct scientist, Max Planck Institute for Intelligent Systems in Tuebingen
Magnus Rattray - Non-parametric modelling of gene expression in time and space
September 10, 2020 - Magnus Rattray - Professor of Computational & Systems Biology, University of Manchester
Andreas Krause - Safe and Efficient Exploration in Reinforcement Learning
August 27, 2020 - Andreas Krause - Professor of Computer Science, Director of Learning & Adaptive Systems Group, ETH Zurich
Rahul Kidambi - MOReL: Model-Based Offline Reinforcement Learning
August 6, 2020 - Rahul Kidambi - Post-doctoral researcher, Department of Computer Science, Cornell University
Arthur Gretton - Generalized Energy-Based Models
July 30, 2020 - Arthur Gretton - Professor, Gatsby Computational Neuroscience Unit, Director of the Centre for Computational Statistics and Machine Learning, University College London
Gergely Neu - A unified view of entropy-regularized Markov decision processes
May 21, 2020 - Gergely Neu - Research Assistant Professor, AI group, DTIC, Universitat Pompeu Fabra
Want to get involved?
If you have interesting research that you'd like to share, please get in touch.