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.
HIGHLIGHT
HIGHLIGHT
HIGHLIGHT
HIGHLIGHT
Seminar
Seminar
Seminar
Seminar
March 28th, 2024
Optimal Experiment Design in Markov Chains
Optimal Experiment Design in Markov Chains
Optimal Experiment Design in Markov Chains
Mojmír Mutný
Mojmír Mutný
Mojmír Mutný
Postdoctoral researcher at ETH Zurich
Postdoctoral researcher at ETH Zurich
Postdoctoral researcher at ETH Zurich
Past seminars
Leveraging replication in active learning
Mickael Binois - INRIA Sophia Antipolis - Méditerranée
Jun 24, 2024
Leveraging replication in active learning
Mickael Binois - INRIA Sophia Antipolis - Méditerranée
Jun 24, 2024
Leveraging replication in active learning
Mickael Binois - INRIA Sophia Antipolis - Méditerranée
Jun 24, 2024
Leveraging replication in active learning
Mickael Binois - INRIA Sophia Antipolis - Méditerranée
Jun 24, 2024
From data to confident decisions
Ilija Bogunovic - University College London
Jun 13, 2024
From data to confident decisions
Ilija Bogunovic - University College London
Jun 13, 2024
From data to confident decisions
Ilija Bogunovic - University College London
Jun 13, 2024
From data to confident decisions
Ilija Bogunovic - University College London
Jun 13, 2024
Data-Centric Engineering for Coherent Risk Management
Domenic Di Francesco - The Alan Turing Institute
Oct 26, 2023
Data-Centric Engineering for Coherent Risk Management
Domenic Di Francesco - The Alan Turing Institute
Oct 26, 2023
Data-Centric Engineering for Coherent Risk Management
Domenic Di Francesco - The Alan Turing Institute
Oct 26, 2023
Data-Centric Engineering for Coherent Risk Management
Domenic Di Francesco - The Alan Turing Institute
Oct 26, 2023
Multi-fidelity Bayesian optimization in chemical engineering
Antonio Del Rio Chanona - Imperial College London
Jul 6, 2023
Multi-fidelity Bayesian optimization in chemical engineering
Antonio Del Rio Chanona - Imperial College London
Jul 6, 2023
Multi-fidelity Bayesian optimization in chemical engineering
Antonio Del Rio Chanona - Imperial College London
Jul 6, 2023
Multi-fidelity Bayesian optimization in chemical engineering
Antonio Del Rio Chanona - Imperial College London
Jul 6, 2023
Harnessing new information in Bayesian optimization
Luigi Nardi - Lund University, Stanford University, DBtune
Jun 7, 2023
Harnessing new information in Bayesian optimization
Luigi Nardi - Lund University, Stanford University, DBtune
Jun 7, 2023
Harnessing new information in Bayesian optimization
Luigi Nardi - Lund University, Stanford University, DBtune
Jun 7, 2023
Harnessing new information in Bayesian optimization
Luigi Nardi - Lund University, Stanford University, DBtune
Jun 7, 2023
Coin sampling: gradient-based Bayesian inference without learning rates
Christopher Nemeth - University of Lancaster
Feb 23, 2023
Coin sampling: gradient-based Bayesian inference without learning rates
Christopher Nemeth - University of Lancaster
Feb 23, 2023
Coin sampling: gradient-based Bayesian inference without learning rates
Christopher Nemeth - University of Lancaster
Feb 23, 2023
Coin sampling: gradient-based Bayesian inference without learning rates
Christopher Nemeth - University of Lancaster
Feb 23, 2023
A roadmap for ultra-scalable simulation and inference in stochastic PDEs
David K. Duvenaud - University of Toronto
Dec 14, 2022
A roadmap for ultra-scalable simulation and inference in stochastic PDEs
David K. Duvenaud - University of Toronto
Dec 14, 2022
A roadmap for ultra-scalable simulation and inference in stochastic PDEs
David K. Duvenaud - University of Toronto
Dec 14, 2022
A roadmap for ultra-scalable simulation and inference in stochastic PDEs
David K. Duvenaud - University of Toronto
Dec 14, 2022
Variational Gaussian processes for spatial modeling: the geoML project
Ítalo Gomes Gonçalves - Universidade Federal do Pampa
Nov 23, 2022
Variational Gaussian processes for spatial modeling: the geoML project
Ítalo Gomes Gonçalves - Universidade Federal do Pampa
Nov 23, 2022
Variational Gaussian processes for spatial modeling: the geoML project
Ítalo Gomes Gonçalves - Universidade Federal do Pampa
Nov 23, 2022
Variational Gaussian processes for spatial modeling: the geoML project
Ítalo Gomes Gonçalves - Universidade Federal do Pampa
Nov 23, 2022
Interacting ODEs with Gaussian processes
Barbara Rakitsch - Bosch Center for Artificial Intelligence
Oct 6, 2022
Interacting ODEs with Gaussian processes
Barbara Rakitsch - Bosch Center for Artificial Intelligence
Oct 6, 2022
Interacting ODEs with Gaussian processes
Barbara Rakitsch - Bosch Center for Artificial Intelligence
Oct 6, 2022
Interacting ODEs with Gaussian processes
Barbara Rakitsch - Bosch Center for Artificial Intelligence
Oct 6, 2022
Unbiased active learning and testing
Sebastian Farquhar - University of Oxford
Sep 16, 2022
Unbiased active learning and testing
Sebastian Farquhar - University of Oxford
Sep 16, 2022
Unbiased active learning and testing
Sebastian Farquhar - University of Oxford
Sep 16, 2022
Unbiased active learning and testing
Sebastian Farquhar - University of Oxford
Sep 16, 2022
Model recycling with Gaussian processes
Pablo Moreno-Muñoz - Technical University of Denmark
Jun 23, 2022
Model recycling with Gaussian processes
Pablo Moreno-Muñoz - Technical University of Denmark
Jun 23, 2022
Model recycling with Gaussian processes
Pablo Moreno-Muñoz - Technical University of Denmark
Jun 23, 2022
Model recycling with Gaussian processes
Pablo Moreno-Muñoz - Technical University of Denmark
Jun 23, 2022
Bayesian optimization over combinatorial structures
Aryan Deshwal - Washington State University
May 26, 2022
Bayesian optimization over combinatorial structures
Aryan Deshwal - Washington State University
May 26, 2022
Bayesian optimization over combinatorial structures
Aryan Deshwal - Washington State University
May 26, 2022
Bayesian optimization over combinatorial structures
Aryan Deshwal - Washington State University
May 26, 2022
Bayesian estimation of integrals: a multi-task approach
Francois-Xavier Briol - University College London
Jan 6, 2022
Bayesian estimation of integrals: a multi-task approach
Francois-Xavier Briol - University College London
Jan 6, 2022
Bayesian estimation of integrals: a multi-task approach
Francois-Xavier Briol - University College London
Jan 6, 2022
Bayesian estimation of integrals: a multi-task approach
Francois-Xavier Briol - University College London
Jan 6, 2022
Developments at the interface between kernel embeddings and Gaussian processes
Dino Sejdinovic - University of Oxford
Dec 2, 2021
Developments at the interface between kernel embeddings and Gaussian processes
Dino Sejdinovic - University of Oxford
Dec 2, 2021
Developments at the interface between kernel embeddings and Gaussian processes
Dino Sejdinovic - University of Oxford
Dec 2, 2021
Developments at the interface between kernel embeddings and Gaussian processes
Dino Sejdinovic - University of Oxford
Dec 2, 2021
Bayesian optimization on Riemannian manifolds for robot learning
Noémie Jaquier - Karlsruhe Institute of Technology
Nov 25, 2021
Bayesian optimization on Riemannian manifolds for robot learning
Noémie Jaquier - Karlsruhe Institute of Technology
Nov 25, 2021
Bayesian optimization on Riemannian manifolds for robot learning
Noémie Jaquier - Karlsruhe Institute of Technology
Nov 25, 2021
Bayesian optimization on Riemannian manifolds for robot learning
Noémie Jaquier - Karlsruhe Institute of Technology
Nov 25, 2021
Sequential construction and dimension reduction of GP under inequality constraints
François Bachoc - Toulouse Mathematics Institute
Nov 25, 2021
Sequential construction and dimension reduction of GP under inequality constraints
François Bachoc - Toulouse Mathematics Institute
Nov 25, 2021
Sequential construction and dimension reduction of GP under inequality constraints
François Bachoc - Toulouse Mathematics Institute
Nov 25, 2021
Sequential construction and dimension reduction of GP under inequality constraints
François Bachoc - Toulouse Mathematics Institute
Nov 25, 2021
Towards deep learning 2.0: going to the meta-level
Frank Hutter - University of Freiburg
Nov 11, 2021
Towards deep learning 2.0: going to the meta-level
Frank Hutter - University of Freiburg
Nov 11, 2021
Towards deep learning 2.0: going to the meta-level
Frank Hutter - University of Freiburg
Nov 11, 2021
Towards deep learning 2.0: going to the meta-level
Frank Hutter - University of Freiburg
Nov 11, 2021
Causal Bayesian optimisation
Javier González Hernández - Microsoft Research Cambridge
Feb 21, 2021
Causal Bayesian optimisation
Javier González Hernández - Microsoft Research Cambridge
Feb 21, 2021
Causal Bayesian optimisation
Javier González Hernández - Microsoft Research Cambridge
Feb 21, 2021
Causal Bayesian optimisation
Javier González Hernández - Microsoft Research Cambridge
Feb 21, 2021
A unifying view of optimism in episodic reinforcement learning
Ciara Pike-Burke - Imperial College London
Sep 2, 2021
A unifying view of optimism in episodic reinforcement learning
Ciara Pike-Burke - Imperial College London
Sep 2, 2021
A unifying view of optimism in episodic reinforcement learning
Ciara Pike-Burke - Imperial College London
Sep 2, 2021
A unifying view of optimism in episodic reinforcement learning
Ciara Pike-Burke - Imperial College London
Sep 2, 2021
Probabilistic methods for increased robustness in machine learning
José Miguel Hernández Lobato - University of Cambridge
Jul 15, 2021
Probabilistic methods for increased robustness in machine learning
José Miguel Hernández Lobato - University of Cambridge
Jul 15, 2021
Probabilistic methods for increased robustness in machine learning
José Miguel Hernández Lobato - University of Cambridge
Jul 15, 2021
Probabilistic methods for increased robustness in machine learning
José Miguel Hernández Lobato - University of Cambridge
Jul 15, 2021
Modulating surrogates for bayesian optimization
Carl Henrik Ek - University of Cambridge
Jun 10, 2021
Modulating surrogates for bayesian optimization
Carl Henrik Ek - University of Cambridge
Jun 10, 2021
Modulating surrogates for bayesian optimization
Carl Henrik Ek - University of Cambridge
Jun 10, 2021
Modulating surrogates for bayesian optimization
Carl Henrik Ek - University of Cambridge
Jun 10, 2021
Efficient robot skill learning
Peter Stone - University of Texas at Austin & Sony AI America
May 13, 2020
Efficient robot skill learning
Peter Stone - University of Texas at Austin & Sony AI America
May 13, 2020
Efficient robot skill learning
Peter Stone - University of Texas at Austin & Sony AI America
May 13, 2020
Efficient robot skill learning
Peter Stone - University of Texas at Austin & Sony AI America
May 13, 2020
How do we build models that learn and generalize?
Andrew G. Wilson - New York University
Jan 21, 2021
How do we build models that learn and generalize?
Andrew G. Wilson - New York University
Jan 21, 2021
How do we build models that learn and generalize?
Andrew G. Wilson - New York University
Jan 21, 2021
How do we build models that learn and generalize?
Andrew G. Wilson - New York University
Jan 21, 2021
Sparse methods for markovian GPs
Vincent Adam - Secondmind & Aalto University
Jan 14, 2021
Sparse methods for markovian GPs
Vincent Adam - Secondmind & Aalto University
Jan 14, 2021
Sparse methods for markovian GPs
Vincent Adam - Secondmind & Aalto University
Jan 14, 2021
Sparse methods for markovian GPs
Vincent Adam - Secondmind & Aalto University
Jan 14, 2021
Reinforcement learning in the real world: How to “cheat” and still feel good about it
Matthew E. Taylor - University of Alberta
Dec 17, 2020
Reinforcement learning in the real world: How to “cheat” and still feel good about it
Matthew E. Taylor - University of Alberta
Dec 17, 2020
Reinforcement learning in the real world: How to “cheat” and still feel good about it
Matthew E. Taylor - University of Alberta
Dec 17, 2020
Reinforcement learning in the real world: How to “cheat” and still feel good about it
Matthew E. Taylor - University of Alberta
Dec 17, 2020
Integration for and as Bayesian inference
Alexandra Gessner - University of Tuebingen
Nov 12, 2020
Integration for and as Bayesian inference
Alexandra Gessner - University of Tuebingen
Nov 12, 2020
Integration for and as Bayesian inference
Alexandra Gessner - University of Tuebingen
Nov 12, 2020
Integration for and as Bayesian inference
Alexandra Gessner - University of Tuebingen
Nov 12, 2020
Stationary activations for uncertainty calibration in deep learning
Arno Solin - Aalto University
Oct 29, 2020
Stationary activations for uncertainty calibration in deep learning
Arno Solin - Aalto University
Oct 29, 2020
Stationary activations for uncertainty calibration in deep learning
Arno Solin - Aalto University
Oct 29, 2020
Stationary activations for uncertainty calibration in deep learning
Arno Solin - Aalto University
Oct 29, 2020
Assisting human perception and control using theory of mind
Siddharth Reddy - University of California, Berkeley
Oct 22, 2020
Assisting human perception and control using theory of mind
Siddharth Reddy - University of California, Berkeley
Oct 22, 2020
Assisting human perception and control using theory of mind
Siddharth Reddy - University of California, Berkeley
Oct 22, 2020
Assisting human perception and control using theory of mind
Siddharth Reddy - University of California, Berkeley
Oct 22, 2020
Knowledge gradient methods for Bayesian optimization
Peter Frazier - Cornell University & Uber
Oct 8, 2022
Knowledge gradient methods for Bayesian optimization
Peter Frazier - Cornell University & Uber
Oct 8, 2022
Knowledge gradient methods for Bayesian optimization
Peter Frazier - Cornell University & Uber
Oct 8, 2022
Knowledge gradient methods for Bayesian optimization
Peter Frazier - Cornell University & Uber
Oct 8, 2022
Challenges of real-world RL: definition, implementation, analysis
Gabriel Dulac-Arnold - Google Research
Oct 1, 2020
Challenges of real-world RL: definition, implementation, analysis
Gabriel Dulac-Arnold - Google Research
Oct 1, 2020
Challenges of real-world RL: definition, implementation, analysis
Gabriel Dulac-Arnold - Google Research
Oct 1, 2020
Challenges of real-world RL: definition, implementation, analysis
Gabriel Dulac-Arnold - Google Research
Oct 1, 2020
Non-parametric modelling of gene expression in time and space
Magnus Rattray - University of Manchester
Sep 10, 2020
Non-parametric modelling of gene expression in time and space
Magnus Rattray - University of Manchester
Sep 10, 2020
Non-parametric modelling of gene expression in time and space
Magnus Rattray - University of Manchester
Sep 10, 2020
Non-parametric modelling of gene expression in time and space
Magnus Rattray - University of Manchester
Sep 10, 2020
Safe and efficient exploration in reinforcement learning
Andreas Krause - ETH Zurich
Aug 27, 2020
Safe and efficient exploration in reinforcement learning
Andreas Krause - ETH Zurich
Aug 27, 2020
Safe and efficient exploration in reinforcement learning
Andreas Krause - ETH Zurich
Aug 27, 2020
Safe and efficient exploration in reinforcement learning
Andreas Krause - ETH Zurich
Aug 27, 2020
MOReL: model-based offline reinforcement learning
Rahul Kidambi - Cornell University
Aug 6, 2020
MOReL: model-based offline reinforcement learning
Rahul Kidambi - Cornell University
Aug 6, 2020
MOReL: model-based offline reinforcement learning
Rahul Kidambi - Cornell University
Aug 6, 2020
MOReL: model-based offline reinforcement learning
Rahul Kidambi - Cornell University
Aug 6, 2020
Generalized energy-based models
Arthur Gretton - University College London
Jul 30, 2020
Generalized energy-based models
Arthur Gretton - University College London
Jul 30, 2020
Generalized energy-based models
Arthur Gretton - University College London
Jul 30, 2020
Generalized energy-based models
Arthur Gretton - University College London
Jul 30, 2020
A unified view of entropy-regularized Markov decision processes
Gergely Neu - Pompeu Fabra University
May 21, 2020
A unified view of entropy-regularized Markov decision processes
Gergely Neu - Pompeu Fabra University
May 21, 2020
A unified view of entropy-regularized Markov decision processes
Gergely Neu - Pompeu Fabra University
May 21, 2020
A unified view of entropy-regularized Markov decision processes
Gergely Neu - Pompeu Fabra University
May 21, 2020
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© Secondmind 2025
© Secondmind 2025
© Secondmind 2025