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.

Coming soon

Aryan Deshwal

May 26, 2022 - Aryan Deshwal - Washington State University.
Details not yet available

Pablo Moreno-Muñoz

June 23, 2022 - Pablo Moreno-Muñoz (Technical University of Denmark)
Details not available yet

David K. Duvenaud

September 8, 2022 - David K. Duvenaud - University of Toronto.
Details not yet available

Sebastian Farquhar

September 15, 2022 - Sebastian Farquhar - University of Oxford
Details not available

Past seminars

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
Details & Watch

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
Details & Watch

Noémie Jaquier - Bayesian optimization on Riemannian manifolds for robot learning

November 25, 2021 - Noémie Jaquier - Postdoctoral researcher at Karlsruhe Institute of Technology
Details & Watch

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
Details & Watch

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
Details & Watch

Javier González Hernández - Causal Bayesian Optimisation

October 21, 2021 - Javier González Hernández - Microsoft Research, Cambridge
Details

Roberto Calandra - Bayesian Optimization for Robotics

October 14, 2021 - Roberto Calandra - Research Scientist at Facebook AI Research
Details & Watch

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
Details & Watch

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
Details

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
Details & Watch

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
Details & Watch

Peter Stone - Efficient Robot Skill Learning

May 13, 2021 - Peter Stone - Professor, University of Texas, Austin; Executive Director, Sony AI America
Details & Watch

Laurence Aitchison - Deep Kernel Processes

March 4, 2021 - Laurence Aitchison - Senior Lecturer, Computational Neuroscience Unit, University of Bristol
Details & Watch

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
Details & Watch

Vincent Adam - Sparse methods for markovian GPs

January 14, 2021 - Vincent Adam - Senior Machine Learning Researcher, Secondmind; Postdoctoral researcher, Aalto University
Details & Watch

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
Details & Watch

Arthur Guez - Value-driven Hindsight Modelling

November 19, 2020 - Arthur Guez - Google DeepMind
Details

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
Details

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
Details & Watch

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
Details & Watch

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
Details & Watch

Gabriel Dulac-Arnold - Challenges of Real-world RL: Definition, Implementation, Analysis

October 1, 2020 - Gabriel Dulac-Arnold - Researcher at Google Research
Details & Watch

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
Details

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
Details

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
Details

Rahul Kidambi - MOReL: Model-Based Offline Reinforcement Learning

August 6, 2020 - Rahul Kidambi - Post-doctoral researcher, Department of Computer Science, Cornell University
Details

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
Details & Watch

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
Details

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