セミナー
当社のバーチャルセミナーは、ゲストスピーカーとアイデアを交換する場であり、最新の動向や刺激的な研究テーマについてあなたを常にアップデートします。Secondmindの研究者が自分の研究を発表することもあります。
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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
過去のセミナー
Leveraging replication in active learning
Mickael Binois - INRIA Sophia Antipolis - Méditerranée
2024/06/24
Leveraging replication in active learning
Mickael Binois - INRIA Sophia Antipolis - Méditerranée
2024/06/24
Leveraging replication in active learning
Mickael Binois - INRIA Sophia Antipolis - Méditerranée
2024/06/24
Leveraging replication in active learning
Mickael Binois - INRIA Sophia Antipolis - Méditerranée
2024/06/24
From data to confident decisions
Ilija Bogunovic - University College London
2024/06/13
From data to confident decisions
Ilija Bogunovic - University College London
2024/06/13
From data to confident decisions
Ilija Bogunovic - University College London
2024/06/13
From data to confident decisions
Ilija Bogunovic - University College London
2024/06/13
Data-Centric Engineering for Coherent Risk Management
Domenic Di Francesco - The Alan Turing Institute
2023/10/26
Data-Centric Engineering for Coherent Risk Management
Domenic Di Francesco - The Alan Turing Institute
2023/10/26
Data-Centric Engineering for Coherent Risk Management
Domenic Di Francesco - The Alan Turing Institute
2023/10/26
Data-Centric Engineering for Coherent Risk Management
Domenic Di Francesco - The Alan Turing Institute
2023/10/26
Multi-fidelity Bayesian optimization in chemical engineering
Antonio Del Rio Chanona - Imperial College London
2023/07/06
Multi-fidelity Bayesian optimization in chemical engineering
Antonio Del Rio Chanona - Imperial College London
2023/07/06
Multi-fidelity Bayesian optimization in chemical engineering
Antonio Del Rio Chanona - Imperial College London
2023/07/06
Multi-fidelity Bayesian optimization in chemical engineering
Antonio Del Rio Chanona - Imperial College London
2023/07/06
Harnessing new information in Bayesian optimization
Luigi Nardi - Lund University, Stanford University, DBtune
2023/06/07
Harnessing new information in Bayesian optimization
Luigi Nardi - Lund University, Stanford University, DBtune
2023/06/07
Harnessing new information in Bayesian optimization
Luigi Nardi - Lund University, Stanford University, DBtune
2023/06/07
Harnessing new information in Bayesian optimization
Luigi Nardi - Lund University, Stanford University, DBtune
2023/06/07
Coin sampling: gradient-based Bayesian inference without learning rates
Christopher Nemeth - University of Lancaster
2023/02/23
Coin sampling: gradient-based Bayesian inference without learning rates
Christopher Nemeth - University of Lancaster
2023/02/23
Coin sampling: gradient-based Bayesian inference without learning rates
Christopher Nemeth - University of Lancaster
2023/02/23
Coin sampling: gradient-based Bayesian inference without learning rates
Christopher Nemeth - University of Lancaster
2023/02/23
A roadmap for ultra-scalable simulation and inference in stochastic PDEs
David K. Duvenaud - University of Toronto
2022/12/14
A roadmap for ultra-scalable simulation and inference in stochastic PDEs
David K. Duvenaud - University of Toronto
2022/12/14
A roadmap for ultra-scalable simulation and inference in stochastic PDEs
David K. Duvenaud - University of Toronto
2022/12/14
A roadmap for ultra-scalable simulation and inference in stochastic PDEs
David K. Duvenaud - University of Toronto
2022/12/14
Variational Gaussian processes for spatial modeling: the geoML project
Ítalo Gomes Gonçalves - Universidade Federal do Pampa
2022/11/23
Variational Gaussian processes for spatial modeling: the geoML project
Ítalo Gomes Gonçalves - Universidade Federal do Pampa
2022/11/23
Variational Gaussian processes for spatial modeling: the geoML project
Ítalo Gomes Gonçalves - Universidade Federal do Pampa
2022/11/23
Variational Gaussian processes for spatial modeling: the geoML project
Ítalo Gomes Gonçalves - Universidade Federal do Pampa
2022/11/23
Interacting ODEs with Gaussian processes
Barbara Rakitsch - Bosch Center for Artificial Intelligence
2022/10/06
Interacting ODEs with Gaussian processes
Barbara Rakitsch - Bosch Center for Artificial Intelligence
2022/10/06
Interacting ODEs with Gaussian processes
Barbara Rakitsch - Bosch Center for Artificial Intelligence
2022/10/06
Interacting ODEs with Gaussian processes
Barbara Rakitsch - Bosch Center for Artificial Intelligence
2022/10/06
Unbiased active learning and testing
Sebastian Farquhar - University of Oxford
2022/09/16
Unbiased active learning and testing
Sebastian Farquhar - University of Oxford
2022/09/16
Unbiased active learning and testing
Sebastian Farquhar - University of Oxford
2022/09/16
Unbiased active learning and testing
Sebastian Farquhar - University of Oxford
2022/09/16
Model recycling with Gaussian processes
Pablo Moreno-Muñoz - Technical University of Denmark
2022/06/23
Model recycling with Gaussian processes
Pablo Moreno-Muñoz - Technical University of Denmark
2022/06/23
Model recycling with Gaussian processes
Pablo Moreno-Muñoz - Technical University of Denmark
2022/06/23
Model recycling with Gaussian processes
Pablo Moreno-Muñoz - Technical University of Denmark
2022/06/23
Bayesian optimization over combinatorial structures
Aryan Deshwal - Washington State University
2022/05/26
Bayesian optimization over combinatorial structures
Aryan Deshwal - Washington State University
2022/05/26
Bayesian optimization over combinatorial structures
Aryan Deshwal - Washington State University
2022/05/26
Bayesian optimization over combinatorial structures
Aryan Deshwal - Washington State University
2022/05/26
Bayesian estimation of integrals: a multi-task approach
Francois-Xavier Briol - University College London
2022/01/06
Bayesian estimation of integrals: a multi-task approach
Francois-Xavier Briol - University College London
2022/01/06
Bayesian estimation of integrals: a multi-task approach
Francois-Xavier Briol - University College London
2022/01/06
Bayesian estimation of integrals: a multi-task approach
Francois-Xavier Briol - University College London
2022/01/06
Developments at the interface between kernel embeddings and Gaussian processes
Dino Sejdinovic - University of Oxford
2021/12/02
Developments at the interface between kernel embeddings and Gaussian processes
Dino Sejdinovic - University of Oxford
2021/12/02
Developments at the interface between kernel embeddings and Gaussian processes
Dino Sejdinovic - University of Oxford
2021/12/02
Developments at the interface between kernel embeddings and Gaussian processes
Dino Sejdinovic - University of Oxford
2021/12/02
Bayesian optimization on Riemannian manifolds for robot learning
Noémie Jaquier - Karlsruhe Institute of Technology
2021/11/25
Bayesian optimization on Riemannian manifolds for robot learning
Noémie Jaquier - Karlsruhe Institute of Technology
2021/11/25
Bayesian optimization on Riemannian manifolds for robot learning
Noémie Jaquier - Karlsruhe Institute of Technology
2021/11/25
Bayesian optimization on Riemannian manifolds for robot learning
Noémie Jaquier - Karlsruhe Institute of Technology
2021/11/25
Sequential construction and dimension reduction of GP under inequality constraints
François Bachoc - Toulouse Mathematics Institute
2021/11/25
Sequential construction and dimension reduction of GP under inequality constraints
François Bachoc - Toulouse Mathematics Institute
2021/11/25
Sequential construction and dimension reduction of GP under inequality constraints
François Bachoc - Toulouse Mathematics Institute
2021/11/25
Sequential construction and dimension reduction of GP under inequality constraints
François Bachoc - Toulouse Mathematics Institute
2021/11/25
Towards deep learning 2.0: going to the meta-level
Frank Hutter - University of Freiburg
2021/11/11
Towards deep learning 2.0: going to the meta-level
Frank Hutter - University of Freiburg
2021/11/11
Towards deep learning 2.0: going to the meta-level
Frank Hutter - University of Freiburg
2021/11/11
Towards deep learning 2.0: going to the meta-level
Frank Hutter - University of Freiburg
2021/11/11
Causal Bayesian optimisation
Javier González Hernández - Microsoft Research Cambridge
2021/02/21
Causal Bayesian optimisation
Javier González Hernández - Microsoft Research Cambridge
2021/02/21
Causal Bayesian optimisation
Javier González Hernández - Microsoft Research Cambridge
2021/02/21
Causal Bayesian optimisation
Javier González Hernández - Microsoft Research Cambridge
2021/02/21
A unifying view of optimism in episodic reinforcement learning
Ciara Pike-Burke - Imperial College London
2021/09/02
A unifying view of optimism in episodic reinforcement learning
Ciara Pike-Burke - Imperial College London
2021/09/02
A unifying view of optimism in episodic reinforcement learning
Ciara Pike-Burke - Imperial College London
2021/09/02
A unifying view of optimism in episodic reinforcement learning
Ciara Pike-Burke - Imperial College London
2021/09/02
Probabilistic methods for increased robustness in machine learning
José Miguel Hernández Lobato - University of Cambridge
2021/07/15
Probabilistic methods for increased robustness in machine learning
José Miguel Hernández Lobato - University of Cambridge
2021/07/15
Probabilistic methods for increased robustness in machine learning
José Miguel Hernández Lobato - University of Cambridge
2021/07/15
Probabilistic methods for increased robustness in machine learning
José Miguel Hernández Lobato - University of Cambridge
2021/07/15
Modulating surrogates for bayesian optimization
Carl Henrik Ek - University of Cambridge
2021/06/10
Modulating surrogates for bayesian optimization
Carl Henrik Ek - University of Cambridge
2021/06/10
Modulating surrogates for bayesian optimization
Carl Henrik Ek - University of Cambridge
2021/06/10
Modulating surrogates for bayesian optimization
Carl Henrik Ek - University of Cambridge
2021/06/10
Efficient robot skill learning
Peter Stone - University of Texas at Austin & Sony AI America
2020/05/13
Efficient robot skill learning
Peter Stone - University of Texas at Austin & Sony AI America
2020/05/13
Efficient robot skill learning
Peter Stone - University of Texas at Austin & Sony AI America
2020/05/13
Efficient robot skill learning
Peter Stone - University of Texas at Austin & Sony AI America
2020/05/13
How do we build models that learn and generalize?
Andrew G. Wilson - New York University
2021/01/21
How do we build models that learn and generalize?
Andrew G. Wilson - New York University
2021/01/21
How do we build models that learn and generalize?
Andrew G. Wilson - New York University
2021/01/21
How do we build models that learn and generalize?
Andrew G. Wilson - New York University
2021/01/21
Sparse methods for markovian GPs
Vincent Adam - Secondmind & Aalto University
2021/01/14
Sparse methods for markovian GPs
Vincent Adam - Secondmind & Aalto University
2021/01/14
Sparse methods for markovian GPs
Vincent Adam - Secondmind & Aalto University
2021/01/14
Sparse methods for markovian GPs
Vincent Adam - Secondmind & Aalto University
2021/01/14
Reinforcement learning in the real world: How to “cheat” and still feel good about it
Matthew E. Taylor - University of Alberta
2020/12/17
Reinforcement learning in the real world: How to “cheat” and still feel good about it
Matthew E. Taylor - University of Alberta
2020/12/17
Reinforcement learning in the real world: How to “cheat” and still feel good about it
Matthew E. Taylor - University of Alberta
2020/12/17
Reinforcement learning in the real world: How to “cheat” and still feel good about it
Matthew E. Taylor - University of Alberta
2020/12/17
Integration for and as Bayesian inference
Alexandra Gessner - University of Tuebingen
2020/11/12
Integration for and as Bayesian inference
Alexandra Gessner - University of Tuebingen
2020/11/12
Integration for and as Bayesian inference
Alexandra Gessner - University of Tuebingen
2020/11/12
Integration for and as Bayesian inference
Alexandra Gessner - University of Tuebingen
2020/11/12
Stationary activations for uncertainty calibration in deep learning
Arno Solin - Aalto University
2020/10/29
Stationary activations for uncertainty calibration in deep learning
Arno Solin - Aalto University
2020/10/29
Stationary activations for uncertainty calibration in deep learning
Arno Solin - Aalto University
2020/10/29
Stationary activations for uncertainty calibration in deep learning
Arno Solin - Aalto University
2020/10/29
Assisting human perception and control using theory of mind
Siddharth Reddy - University of California, Berkeley
2020/10/22
Assisting human perception and control using theory of mind
Siddharth Reddy - University of California, Berkeley
2020/10/22
Assisting human perception and control using theory of mind
Siddharth Reddy - University of California, Berkeley
2020/10/22
Assisting human perception and control using theory of mind
Siddharth Reddy - University of California, Berkeley
2020/10/22
Knowledge gradient methods for Bayesian optimization
Peter Frazier - Cornell University & Uber
2022/10/08
Knowledge gradient methods for Bayesian optimization
Peter Frazier - Cornell University & Uber
2022/10/08
Knowledge gradient methods for Bayesian optimization
Peter Frazier - Cornell University & Uber
2022/10/08
Knowledge gradient methods for Bayesian optimization
Peter Frazier - Cornell University & Uber
2022/10/08
Challenges of real-world RL: definition, implementation, analysis
Gabriel Dulac-Arnold - Google Research
2020/10/01
Challenges of real-world RL: definition, implementation, analysis
Gabriel Dulac-Arnold - Google Research
2020/10/01
Challenges of real-world RL: definition, implementation, analysis
Gabriel Dulac-Arnold - Google Research
2020/10/01
Challenges of real-world RL: definition, implementation, analysis
Gabriel Dulac-Arnold - Google Research
2020/10/01
Non-parametric modelling of gene expression in time and space
Magnus Rattray - University of Manchester
2020/09/10
Non-parametric modelling of gene expression in time and space
Magnus Rattray - University of Manchester
2020/09/10
Non-parametric modelling of gene expression in time and space
Magnus Rattray - University of Manchester
2020/09/10
Non-parametric modelling of gene expression in time and space
Magnus Rattray - University of Manchester
2020/09/10
Safe and efficient exploration in reinforcement learning
Andreas Krause - ETH Zurich
2020/08/27
Safe and efficient exploration in reinforcement learning
Andreas Krause - ETH Zurich
2020/08/27
Safe and efficient exploration in reinforcement learning
Andreas Krause - ETH Zurich
2020/08/27
Safe and efficient exploration in reinforcement learning
Andreas Krause - ETH Zurich
2020/08/27
MOReL: model-based offline reinforcement learning
Rahul Kidambi - Cornell University
2020/08/06
MOReL: model-based offline reinforcement learning
Rahul Kidambi - Cornell University
2020/08/06
MOReL: model-based offline reinforcement learning
Rahul Kidambi - Cornell University
2020/08/06
MOReL: model-based offline reinforcement learning
Rahul Kidambi - Cornell University
2020/08/06
A unified view of entropy-regularized Markov decision processes
Gergely Neu - Pompeu Fabra University
2020/05/21
A unified view of entropy-regularized Markov decision processes
Gergely Neu - Pompeu Fabra University
2020/05/21
A unified view of entropy-regularized Markov decision processes
Gergely Neu - Pompeu Fabra University
2020/05/21
