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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
Mickael Binois - Leveraging replication in active learning
We were recently joined by Mickael Binois, to talk about 'Leveraging replication in active learning'.
2024/06/24
Mickael Binois - Leveraging replication in active learning
We were recently joined by Mickael Binois, to talk about 'Leveraging replication in active learning'.
2024/06/24
Mickael Binois - Leveraging replication in active learning
We were recently joined by Mickael Binois, to talk about 'Leveraging replication in active learning'.
2024/06/24
Mickael Binois - Leveraging replication in active learning
We were recently joined by Mickael Binois, to talk about 'Leveraging replication in active learning'.
2024/06/24
Ilija Bogunovic - From Data to Confident Decisions
We were recently joined by Ilija Bogunovic, to talk about 'Robust and Efficient Algorithmic Decision Making'.
2024/06/13
Ilija Bogunovic - From Data to Confident Decisions
We were recently joined by Ilija Bogunovic, to talk about 'Robust and Efficient Algorithmic Decision Making'.
2024/06/13
Ilija Bogunovic - From Data to Confident Decisions
We were recently joined by Ilija Bogunovic, to talk about 'Robust and Efficient Algorithmic Decision Making'.
2024/06/13
Ilija Bogunovic - From Data to Confident Decisions
We were recently joined by Ilija Bogunovic, to talk about 'Robust and Efficient Algorithmic Decision Making'.
2024/06/13
Dario Azzimonti - Preference learning with Gaussian processes
We were recently joined by Dario Azzimonti, to talk about 'Preference learning with Gaussian processes'.
2024/05/23
Dario Azzimonti - Preference learning with Gaussian processes
We were recently joined by Dario Azzimonti, to talk about 'Preference learning with Gaussian processes'.
2024/05/23
Dario Azzimonti - Preference learning with Gaussian processes
We were recently joined by Dario Azzimonti, to talk about 'Preference learning with Gaussian processes'.
2024/05/23
Dario Azzimonti - Preference learning with Gaussian processes
We were recently joined by Dario Azzimonti, to talk about 'Preference learning with Gaussian processes'.
2024/05/23
Mojmír Mutný - Optimal Experiment Design in Markov Chains
We were recently joined by Mojmír Mutný (ETH Zurich), to talk about 'Optimal Experiment Design in Markov Chains'.
2024/03/28
Mojmír Mutný - Optimal Experiment Design in Markov Chains
We were recently joined by Mojmír Mutný (ETH Zurich), to talk about 'Optimal Experiment Design in Markov Chains'.
2024/03/28
Mojmír Mutný - Optimal Experiment Design in Markov Chains
We were recently joined by Mojmír Mutný (ETH Zurich), to talk about 'Optimal Experiment Design in Markov Chains'.
2024/03/28
Mojmír Mutný - Optimal Experiment Design in Markov Chains
We were recently joined by Mojmír Mutný (ETH Zurich), to talk about 'Optimal Experiment Design in Markov Chains'.
2024/03/28
Domenic Di Francesco - Data-Centric Engineering for Coherent Risk Management
We were recently joined by Domenic Di Francesco (The Alan Turing Institute), to talk about 'Data-Centric Engineering for Coherent Risk Management'.
2023/10/26
Domenic Di Francesco - Data-Centric Engineering for Coherent Risk Management
We were recently joined by Domenic Di Francesco (The Alan Turing Institute), to talk about 'Data-Centric Engineering for Coherent Risk Management'.
2023/10/26
Domenic Di Francesco - Data-Centric Engineering for Coherent Risk Management
We were recently joined by Domenic Di Francesco (The Alan Turing Institute), to talk about 'Data-Centric Engineering for Coherent Risk Management'.
2023/10/26
Domenic Di Francesco - Data-Centric Engineering for Coherent Risk Management
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2023/10/26
Antonio Del Rio Chanona - Multi-Fidelity Bayesian Optimization in Chemical Engineering
We were recently joined by Antonio Del Rio Chanona (Imperial College London), to talk about 'Multi-Fidelity Bayesian Optimization in Chemical Engineering'.
2023/07/06
Antonio Del Rio Chanona - Multi-Fidelity Bayesian Optimization in Chemical Engineering
We were recently joined by Antonio Del Rio Chanona (Imperial College London), to talk about 'Multi-Fidelity Bayesian Optimization in Chemical Engineering'.
2023/07/06
Antonio Del Rio Chanona - Multi-Fidelity Bayesian Optimization in Chemical Engineering
We were recently joined by Antonio Del Rio Chanona (Imperial College London), to talk about 'Multi-Fidelity Bayesian Optimization in Chemical Engineering'.
2023/07/06
Antonio Del Rio Chanona - Multi-Fidelity Bayesian Optimization in Chemical Engineering
We were recently joined by Antonio Del Rio Chanona (Imperial College London), to talk about 'Multi-Fidelity Bayesian Optimization in Chemical Engineering'.
2023/07/06
Luigi Nardi - Harnessing new information in Bayesian optimization
We were recently joined by Luigi Nardi (Lund University, Stanford University and DBtune), to talk about 'Harnessing new information in Bayesian optimization'.
2023/06/07
Luigi Nardi - Harnessing new information in Bayesian optimization
We were recently joined by Luigi Nardi (Lund University, Stanford University and DBtune), to talk about 'Harnessing new information in Bayesian optimization'.
2023/06/07
Luigi Nardi - Harnessing new information in Bayesian optimization
We were recently joined by Luigi Nardi (Lund University, Stanford University and DBtune), to talk about 'Harnessing new information in Bayesian optimization'.
2023/06/07
Luigi Nardi - Harnessing new information in Bayesian optimization
We were recently joined by Luigi Nardi (Lund University, Stanford University and DBtune), to talk about 'Harnessing new information in Bayesian optimization'.
2023/06/07
Christopher Nemeth - Coin Sampling: Gradient-Based Bayesian Inference without Learning Rates
We were recently joined by Christopher Nemeth (University of Lancaster), to talk about 'Gradient-Based Bayesian Inference without Learning Rates'.
2023/02/23
Christopher Nemeth - Coin Sampling: Gradient-Based Bayesian Inference without Learning Rates
We were recently joined by Christopher Nemeth (University of Lancaster), to talk about 'Gradient-Based Bayesian Inference without Learning Rates'.
2023/02/23
Christopher Nemeth - Coin Sampling: Gradient-Based Bayesian Inference without Learning Rates
We were recently joined by Christopher Nemeth (University of Lancaster), to talk about 'Gradient-Based Bayesian Inference without Learning Rates'.
2023/02/23
Christopher Nemeth - Coin Sampling: Gradient-Based Bayesian Inference without Learning Rates
We were recently joined by Christopher Nemeth (University of Lancaster), to talk about 'Gradient-Based Bayesian Inference without Learning Rates'.
2023/02/23
David K. Duvenaud - A farewell to GPs
We were recently joined by David Duvenaud (University of Toronto), to talk about 'A farewell to GPs'.
2022/12/14
David K. Duvenaud - A farewell to GPs
We were recently joined by David Duvenaud (University of Toronto), to talk about 'A farewell to GPs'.
2022/12/14
David K. Duvenaud - A farewell to GPs
We were recently joined by David Duvenaud (University of Toronto), to talk about 'A farewell to GPs'.
2022/12/14
David K. Duvenaud - A farewell to GPs
We were recently joined by David Duvenaud (University of Toronto), to talk about 'A farewell to GPs'.
2022/12/14
Ítalo Gomes Gonçalves - Variational Gaussian processes for spatial modeling: the geoML project
We were recently joined by Ítalo Gomes Gonçalves (Universidade Federal do Pampa), to talk about 'Variational Gaussian processes for spatial modeling: the geoML project'.
2022/11/23
Ítalo Gomes Gonçalves - Variational Gaussian processes for spatial modeling: the geoML project
We were recently joined by Ítalo Gomes Gonçalves (Universidade Federal do Pampa), to talk about 'Variational Gaussian processes for spatial modeling: the geoML project'.
2022/11/23
Ítalo Gomes Gonçalves - Variational Gaussian processes for spatial modeling: the geoML project
We were recently joined by Ítalo Gomes Gonçalves (Universidade Federal do Pampa), to talk about 'Variational Gaussian processes for spatial modeling: the geoML project'.
2022/11/23
Ítalo Gomes Gonçalves - Variational Gaussian processes for spatial modeling: the geoML project
We were recently joined by Ítalo Gomes Gonçalves (Universidade Federal do Pampa), to talk about 'Variational Gaussian processes for spatial modeling: the geoML project'.
2022/11/23
Martin Jørgensen - Bézier Gaussian Processes
We were recently joined by Martin Jørgensen (University of Oxford), to talk about 'Bézier Gaussian Processes'.
2022/11/10
Martin Jørgensen - Bézier Gaussian Processes
We were recently joined by Martin Jørgensen (University of Oxford), to talk about 'Bézier Gaussian Processes'.
2022/11/10
Martin Jørgensen - Bézier Gaussian Processes
We were recently joined by Martin Jørgensen (University of Oxford), to talk about 'Bézier Gaussian Processes'.
2022/11/10
Martin Jørgensen - Bézier Gaussian Processes
We were recently joined by Martin Jørgensen (University of Oxford), to talk about 'Bézier Gaussian Processes'.
2022/11/10
Barbara Rakitsch - Interacting ODEs with Gaussian Processes
We were recently joined by Barbara Rakitsch (Bosch Center for Artificial Intelligence), to talk about 'Interacting ODEs with Gaussian Processes'.
2022/10/06
Barbara Rakitsch - Interacting ODEs with Gaussian Processes
We were recently joined by Barbara Rakitsch (Bosch Center for Artificial Intelligence), to talk about 'Interacting ODEs with Gaussian Processes'.
2022/10/06
Barbara Rakitsch - Interacting ODEs with Gaussian Processes
We were recently joined by Barbara Rakitsch (Bosch Center for Artificial Intelligence), to talk about 'Interacting ODEs with Gaussian Processes'.
2022/10/06
Barbara Rakitsch - Interacting ODEs with Gaussian Processes
We were recently joined by Barbara Rakitsch (Bosch Center for Artificial Intelligence), to talk about 'Interacting ODEs with Gaussian Processes'.
2022/10/06
Sebastian Farquhar - Unbiased Active Learning and Testing
We were recently joined by Sebastian Farquhar (University of Oxofrd), to talk about 'Unbiased Active Learning and Testing'.
2022/09/16
Sebastian Farquhar - Unbiased Active Learning and Testing
We were recently joined by Sebastian Farquhar (University of Oxofrd), to talk about 'Unbiased Active Learning and Testing'.
2022/09/16
Sebastian Farquhar - Unbiased Active Learning and Testing
We were recently joined by Sebastian Farquhar (University of Oxofrd), to talk about 'Unbiased Active Learning and Testing'.
2022/09/16
Sebastian Farquhar - Unbiased Active Learning and Testing
We were recently joined by Sebastian Farquhar (University of Oxofrd), to talk about 'Unbiased Active Learning and Testing'.
2022/09/16
Pablo Moreno-Muñoz - Model Recycling with Gaussian Processes
We were recently joined by Pablo Moreno-Muñoz (Technical University of Denmark), to talk about 'Model Recycling with Gaussian Processes'.
2022/06/23
Pablo Moreno-Muñoz - Model Recycling with Gaussian Processes
We were recently joined by Pablo Moreno-Muñoz (Technical University of Denmark), to talk about 'Model Recycling with Gaussian Processes'.
2022/06/23
Pablo Moreno-Muñoz - Model Recycling with Gaussian Processes
We were recently joined by Pablo Moreno-Muñoz (Technical University of Denmark), to talk about 'Model Recycling with Gaussian Processes'.
2022/06/23
Pablo Moreno-Muñoz - Model Recycling with Gaussian Processes
We were recently joined by Pablo Moreno-Muñoz (Technical University of Denmark), to talk about 'Model Recycling with Gaussian Processes'.
2022/06/23
Aryan Deshwal - Bayesian Optimization over Combinatorial Structures
We were recently joined by Aryan Deshwal (Washington State University), to talk about 'Bayesian Optimization over Combinatorial Structures'.
2022/05/26
Aryan Deshwal - Bayesian Optimization over Combinatorial Structures
We were recently joined by Aryan Deshwal (Washington State University), to talk about 'Bayesian Optimization over Combinatorial Structures'.
2022/05/26
Aryan Deshwal - Bayesian Optimization over Combinatorial Structures
We were recently joined by Aryan Deshwal (Washington State University), to talk about 'Bayesian Optimization over Combinatorial Structures'.
2022/05/26
Aryan Deshwal - Bayesian Optimization over Combinatorial Structures
We were recently joined by Aryan Deshwal (Washington State University), to talk about 'Bayesian Optimization over Combinatorial Structures'.
2022/05/26
François-Xavier Briol - Bayesian Estimation of Integrals: A Multi-task Approach
We had the pleasure to host Francois-Xavier Briol for a virtual seminar. His work on Bayesian quadrature is very relevant to research and applications at Secondmind.ai
2022/01/06
François-Xavier Briol - Bayesian Estimation of Integrals: A Multi-task Approach
We had the pleasure to host Francois-Xavier Briol for a virtual seminar. His work on Bayesian quadrature is very relevant to research and applications at Secondmind.ai
2022/01/06
François-Xavier Briol - Bayesian Estimation of Integrals: A Multi-task Approach
We had the pleasure to host Francois-Xavier Briol for a virtual seminar. His work on Bayesian quadrature is very relevant to research and applications at Secondmind.ai
2022/01/06
François-Xavier Briol - Bayesian Estimation of Integrals: A Multi-task Approach
We had the pleasure to host Francois-Xavier Briol for a virtual seminar. His work on Bayesian quadrature is very relevant to research and applications at Secondmind.ai
2022/01/06
Dino Sejdinovic - Developments at the Interface Between Kernel Embeddings and Gaussian Processes
We were recently joined by Dino Sejdinovic (University of Oxford), to talk about 'Recent Developments at the Interface Between Kernel Embeddings and Gaussian Processes'.
2021/12/02
Dino Sejdinovic - Developments at the Interface Between Kernel Embeddings and Gaussian Processes
We were recently joined by Dino Sejdinovic (University of Oxford), to talk about 'Recent Developments at the Interface Between Kernel Embeddings and Gaussian Processes'.
2021/12/02
Dino Sejdinovic - Developments at the Interface Between Kernel Embeddings and Gaussian Processes
We were recently joined by Dino Sejdinovic (University of Oxford), to talk about 'Recent Developments at the Interface Between Kernel Embeddings and Gaussian Processes'.
2021/12/02
Dino Sejdinovic - Developments at the Interface Between Kernel Embeddings and Gaussian Processes
We were recently joined by Dino Sejdinovic (University of Oxford), to talk about 'Recent Developments at the Interface Between Kernel Embeddings and Gaussian Processes'.
2021/12/02
Noémie Jaquier - Bayesian optimization on Riemannian manifolds for robot learning
We were recently joined by Noémie Jaquier (Karlsruhe Institute of Technology), to talk about 'Bayesian optimization on Riemannian manifolds for robot learning'.
2021/11/25
Noémie Jaquier - Bayesian optimization on Riemannian manifolds for robot learning
We were recently joined by Noémie Jaquier (Karlsruhe Institute of Technology), to talk about 'Bayesian optimization on Riemannian manifolds for robot learning'.
2021/11/25
Noémie Jaquier - Bayesian optimization on Riemannian manifolds for robot learning
We were recently joined by Noémie Jaquier (Karlsruhe Institute of Technology), to talk about 'Bayesian optimization on Riemannian manifolds for robot learning'.
2021/11/25
Noémie Jaquier - Bayesian optimization on Riemannian manifolds for robot learning
We were recently joined by Noémie Jaquier (Karlsruhe Institute of Technology), to talk about 'Bayesian optimization on Riemannian manifolds for robot learning'.
2021/11/25
François Bachoc - Sequential construction and dimension reduction of GP under inequality constraints
We were recently joined by François Bachoc (Toulouse Mathematics Institute), to talk about 'Sequential construction and dimension reduction of Gaussian processes under inequality constraints'.
2021/11/25
François Bachoc - Sequential construction and dimension reduction of GP under inequality constraints
We were recently joined by François Bachoc (Toulouse Mathematics Institute), to talk about 'Sequential construction and dimension reduction of Gaussian processes under inequality constraints'.
2021/11/25
François Bachoc - Sequential construction and dimension reduction of GP under inequality constraints
We were recently joined by François Bachoc (Toulouse Mathematics Institute), to talk about 'Sequential construction and dimension reduction of Gaussian processes under inequality constraints'.
2021/11/25
François Bachoc - Sequential construction and dimension reduction of GP under inequality constraints
We were recently joined by François Bachoc (Toulouse Mathematics Institute), to talk about 'Sequential construction and dimension reduction of Gaussian processes under inequality constraints'.
2021/11/25
Frank Hutter - Towards Deep Learning 2.0: Going to the Meta-Level
We were recently joined by Frank Hutter (University of Freiburg), to talk about 'Towards Deep Learning 2.0: Going to the Meta-Level'.
2021/11/11
Frank Hutter - Towards Deep Learning 2.0: Going to the Meta-Level
We were recently joined by Frank Hutter (University of Freiburg), to talk about 'Towards Deep Learning 2.0: Going to the Meta-Level'.
2021/11/11
Frank Hutter - Towards Deep Learning 2.0: Going to the Meta-Level
We were recently joined by Frank Hutter (University of Freiburg), to talk about 'Towards Deep Learning 2.0: Going to the Meta-Level'.
2021/11/11
Frank Hutter - Towards Deep Learning 2.0: Going to the Meta-Level
We were recently joined by Frank Hutter (University of Freiburg), to talk about 'Towards Deep Learning 2.0: Going to the Meta-Level'.
2021/11/11
Javier González Hernández - Causal Bayesian Optimisation
We were recently joined by Javier González Hernández (Microsoft Research Cambridge), to talk about 'Causal Bayesian Optimisation: don’t do everything, just do the right thing'.
2021/02/21
Javier González Hernández - Causal Bayesian Optimisation
We were recently joined by Javier González Hernández (Microsoft Research Cambridge), to talk about 'Causal Bayesian Optimisation: don’t do everything, just do the right thing'.
2021/02/21
Javier González Hernández - Causal Bayesian Optimisation
We were recently joined by Javier González Hernández (Microsoft Research Cambridge), to talk about 'Causal Bayesian Optimisation: don’t do everything, just do the right thing'.
2021/02/21
Javier González Hernández - Causal Bayesian Optimisation
We were recently joined by Javier González Hernández (Microsoft Research Cambridge), to talk about 'Causal Bayesian Optimisation: don’t do everything, just do the right thing'.
2021/02/21
Emtiyaz Khan - Bayesian Principles for Learning-Machines
We were recently joined by Emtiyaz Khan (RIKEN), to talk about 'Bayesian Principles for Learning-Machines'.
2021/09/17
Emtiyaz Khan - Bayesian Principles for Learning-Machines
We were recently joined by Emtiyaz Khan (RIKEN), to talk about 'Bayesian Principles for Learning-Machines'.
2021/09/17
Emtiyaz Khan - Bayesian Principles for Learning-Machines
We were recently joined by Emtiyaz Khan (RIKEN), to talk about 'Bayesian Principles for Learning-Machines'.
2021/09/17
Emtiyaz Khan - Bayesian Principles for Learning-Machines
We were recently joined by Emtiyaz Khan (RIKEN), to talk about 'Bayesian Principles for Learning-Machines'.
2021/09/17
Ciara Pike-Burke - A unifying view of optimism in episodic reinforcement learning
Dr Ciara Pike-Burke (Imperial College London), gave a talk on 'A unifying view of optimism in episodic reinforcement learning'.
2021/09/02
Ciara Pike-Burke - A unifying view of optimism in episodic reinforcement learning
Dr Ciara Pike-Burke (Imperial College London), gave a talk on 'A unifying view of optimism in episodic reinforcement learning'.
2021/09/02
Ciara Pike-Burke - A unifying view of optimism in episodic reinforcement learning
Dr Ciara Pike-Burke (Imperial College London), gave a talk on 'A unifying view of optimism in episodic reinforcement learning'.
2021/09/02
Ciara Pike-Burke - A unifying view of optimism in episodic reinforcement learning
Dr Ciara Pike-Burke (Imperial College London), gave a talk on 'A unifying view of optimism in episodic reinforcement learning'.
2021/09/02
José Miguel Hernández Lobato - Probabilistic Methods for Increased Robustness in Machine Learning
We were recently joined by José Miguel Hernández Lobato (University of Cambridge), to talk about 'Probabilistic Methods for Increased Robustness in Machine Learning'.
2021/07/15
José Miguel Hernández Lobato - Probabilistic Methods for Increased Robustness in Machine Learning
We were recently joined by José Miguel Hernández Lobato (University of Cambridge), to talk about 'Probabilistic Methods for Increased Robustness in Machine Learning'.
2021/07/15
José Miguel Hernández Lobato - Probabilistic Methods for Increased Robustness in Machine Learning
We were recently joined by José Miguel Hernández Lobato (University of Cambridge), to talk about 'Probabilistic Methods for Increased Robustness in Machine Learning'.
2021/07/15
José Miguel Hernández Lobato - Probabilistic Methods for Increased Robustness in Machine Learning
We were recently joined by José Miguel Hernández Lobato (University of Cambridge), to talk about 'Probabilistic Methods for Increased Robustness in Machine Learning'.
2021/07/15
Carl Henrik Ek - Modulating surrogates for bayesian optimization
We were recently joined by Carl Henrik Ek to talk about 'Modulating surrogates for bayesian optimization'.
2021/06/10
Carl Henrik Ek - Modulating surrogates for bayesian optimization
We were recently joined by Carl Henrik Ek to talk about 'Modulating surrogates for bayesian optimization'.
2021/06/10
Carl Henrik Ek - Modulating surrogates for bayesian optimization
We were recently joined by Carl Henrik Ek to talk about 'Modulating surrogates for bayesian optimization'.
2021/06/10
Carl Henrik Ek - Modulating surrogates for bayesian optimization
We were recently joined by Carl Henrik Ek to talk about 'Modulating surrogates for bayesian optimization'.
2021/06/10
Peter Stone - Efficient Robot Skill Learning
We were recently joined by Peter Stone (University of Texas at Austin & Sony AI), to talk about 'Efficient Robot Skill Learning: Grounded Simulation Learning and Imitation Learning from Observation'.
2020/05/13
Peter Stone - Efficient Robot Skill Learning
We were recently joined by Peter Stone (University of Texas at Austin & Sony AI), to talk about 'Efficient Robot Skill Learning: Grounded Simulation Learning and Imitation Learning from Observation'.
2020/05/13
Peter Stone - Efficient Robot Skill Learning
We were recently joined by Peter Stone (University of Texas at Austin & Sony AI), to talk about 'Efficient Robot Skill Learning: Grounded Simulation Learning and Imitation Learning from Observation'.
2020/05/13
Peter Stone - Efficient Robot Skill Learning
We were recently joined by Peter Stone (University of Texas at Austin & Sony AI), to talk about 'Efficient Robot Skill Learning: Grounded Simulation Learning and Imitation Learning from Observation'.
2020/05/13
Laurence Aitchison - Deep Kernel Processes
We were recently joined by Laurence Aitchison (University of Bristol), to talk about 'Deep Kernel Processes?'.
2021/03/04
Laurence Aitchison - Deep Kernel Processes
We were recently joined by Laurence Aitchison (University of Bristol), to talk about 'Deep Kernel Processes?'.
2021/03/04
Laurence Aitchison - Deep Kernel Processes
We were recently joined by Laurence Aitchison (University of Bristol), to talk about 'Deep Kernel Processes?'.
2021/03/04
Laurence Aitchison - Deep Kernel Processes
We were recently joined by Laurence Aitchison (University of Bristol), to talk about 'Deep Kernel Processes?'.
2021/03/04
Andrew G. Wilson - How do we build models that learn and generalize?
We were recently joined by Andrew G. Wilson (New York University), to talk about 'How do we build models that learn and generalize?'.
2021/01/21
Andrew G. Wilson - How do we build models that learn and generalize?
We were recently joined by Andrew G. Wilson (New York University), to talk about 'How do we build models that learn and generalize?'.
2021/01/21
Andrew G. Wilson - How do we build models that learn and generalize?
We were recently joined by Andrew G. Wilson (New York University), to talk about 'How do we build models that learn and generalize?'.
2021/01/21
Andrew G. Wilson - How do we build models that learn and generalize?
We were recently joined by Andrew G. Wilson (New York University), to talk about 'How do we build models that learn and generalize?'.
2021/01/21
Vincent Adam - Sparse methods for markovian GPs
We were recently joined by Vincent Adam (Secondmind & Aalto University), to talk about 'Sparse methods for markovian GPs'.
2021/01/14
Vincent Adam - Sparse methods for markovian GPs
We were recently joined by Vincent Adam (Secondmind & Aalto University), to talk about 'Sparse methods for markovian GPs'.
2021/01/14
Vincent Adam - Sparse methods for markovian GPs
We were recently joined by Vincent Adam (Secondmind & Aalto University), to talk about 'Sparse methods for markovian GPs'.
2021/01/14
Vincent Adam - Sparse methods for markovian GPs
We were recently joined by Vincent Adam (Secondmind & Aalto University), to talk about 'Sparse methods for markovian GPs'.
2021/01/14
M. E. Taylor - Reinforcement Learning in the real world: How to “cheat” and still feel good about it
Matthew E. Taylor (University of Alberta) gave a talk on 'Reinforcement Learning in the Real-world: How to “cheat” and still feel good about it'.
2020/12/17
M. E. Taylor - Reinforcement Learning in the real world: How to “cheat” and still feel good about it
Matthew E. Taylor (University of Alberta) gave a talk on 'Reinforcement Learning in the Real-world: How to “cheat” and still feel good about it'.
2020/12/17
M. E. Taylor - Reinforcement Learning in the real world: How to “cheat” and still feel good about it
Matthew E. Taylor (University of Alberta) gave a talk on 'Reinforcement Learning in the Real-world: How to “cheat” and still feel good about it'.
2020/12/17
M. E. Taylor - Reinforcement Learning in the real world: How to “cheat” and still feel good about it
Matthew E. Taylor (University of Alberta) gave a talk on 'Reinforcement Learning in the Real-world: How to “cheat” and still feel good about it'.
2020/12/17
Arthur Guez - Value-driven Hindsight Modelling
We were recently joined by Arthur Guez (Google Deepmind), to talk about 'Value-driven Hindsight Modelling'.
2020/11/19
Arthur Guez - Value-driven Hindsight Modelling
We were recently joined by Arthur Guez (Google Deepmind), to talk about 'Value-driven Hindsight Modelling'.
2020/11/19
Arthur Guez - Value-driven Hindsight Modelling
We were recently joined by Arthur Guez (Google Deepmind), to talk about 'Value-driven Hindsight Modelling'.
2020/11/19
Arthur Guez - Value-driven Hindsight Modelling
We were recently joined by Arthur Guez (Google Deepmind), to talk about 'Value-driven Hindsight Modelling'.
2020/11/19
Alexandra Gessner - Integration for and as Bayesian inference
We were recently joined by Alexandra Gessner (University of Tuebingen), to talk about 'Integration for and as Bayesian inference'.
2020/11/12
Alexandra Gessner - Integration for and as Bayesian inference
We were recently joined by Alexandra Gessner (University of Tuebingen), to talk about 'Integration for and as Bayesian inference'.
2020/11/12
Alexandra Gessner - Integration for and as Bayesian inference
We were recently joined by Alexandra Gessner (University of Tuebingen), to talk about 'Integration for and as Bayesian inference'.
2020/11/12
Alexandra Gessner - Integration for and as Bayesian inference
We were recently joined by Alexandra Gessner (University of Tuebingen), to talk about 'Integration for and as Bayesian inference'.
2020/11/12
Arno Solin - Stationary Activations for Uncertainty Calibration in Deep Learning
We were recently joined by Dr Arno Solin (Aalto University), to talk about 'Stationary Activations for Uncertainty Calibration in Deep Learning'.
2020/10/29
Arno Solin - Stationary Activations for Uncertainty Calibration in Deep Learning
We were recently joined by Dr Arno Solin (Aalto University), to talk about 'Stationary Activations for Uncertainty Calibration in Deep Learning'.
2020/10/29
Arno Solin - Stationary Activations for Uncertainty Calibration in Deep Learning
We were recently joined by Dr Arno Solin (Aalto University), to talk about 'Stationary Activations for Uncertainty Calibration in Deep Learning'.
2020/10/29
Arno Solin - Stationary Activations for Uncertainty Calibration in Deep Learning
We were recently joined by Dr Arno Solin (Aalto University), to talk about 'Stationary Activations for Uncertainty Calibration in Deep Learning'.
2020/10/29
Siddharth Reddy - Assisting Human Perception and Control using Theory of Mind
Siddharth Reddy (University of California, Berkley), gave a talk on 'Assisting Human Perception and Control using Theory of Mind'.
2020/10/22
Siddharth Reddy - Assisting Human Perception and Control using Theory of Mind
Siddharth Reddy (University of California, Berkley), gave a talk on 'Assisting Human Perception and Control using Theory of Mind'.
2020/10/22
Siddharth Reddy - Assisting Human Perception and Control using Theory of Mind
Siddharth Reddy (University of California, Berkley), gave a talk on 'Assisting Human Perception and Control using Theory of Mind'.
2020/10/22
Siddharth Reddy - Assisting Human Perception and Control using Theory of Mind
Siddharth Reddy (University of California, Berkley), gave a talk on 'Assisting Human Perception and Control using Theory of Mind'.
2020/10/22
Peter Frazier - Knowledge Gradient Methods for Bayesian Optimization
We were recently joined by Peter Frazier (Cornell University & Uber), to talk about 'Knowledge-Gradient Methods for Grey-Box Bayesian Optimization'.
2022/10/08
Peter Frazier - Knowledge Gradient Methods for Bayesian Optimization
We were recently joined by Peter Frazier (Cornell University & Uber), to talk about 'Knowledge-Gradient Methods for Grey-Box Bayesian Optimization'.
2022/10/08
Peter Frazier - Knowledge Gradient Methods for Bayesian Optimization
We were recently joined by Peter Frazier (Cornell University & Uber), to talk about 'Knowledge-Gradient Methods for Grey-Box Bayesian Optimization'.
2022/10/08
Peter Frazier - Knowledge Gradient Methods for Bayesian Optimization
We were recently joined by Peter Frazier (Cornell University & Uber), to talk about 'Knowledge-Gradient Methods for Grey-Box Bayesian Optimization'.
2022/10/08
Gabriel Dulac-Arnold - Challenges of Real-world RL: Definition, Implementation, Analysis
Gabriel Dulac-Arnold (Google Research), gave a talk on 'Challenges of Real-world RL: Definition, Implementation, Analysis'.
2020/10/01
Gabriel Dulac-Arnold - Challenges of Real-world RL: Definition, Implementation, Analysis
Gabriel Dulac-Arnold (Google Research), gave a talk on 'Challenges of Real-world RL: Definition, Implementation, Analysis'.
2020/10/01
Gabriel Dulac-Arnold - Challenges of Real-world RL: Definition, Implementation, Analysis
Gabriel Dulac-Arnold (Google Research), gave a talk on 'Challenges of Real-world RL: Definition, Implementation, Analysis'.
2020/10/01
Gabriel Dulac-Arnold - Challenges of Real-world RL: Definition, Implementation, Analysis
Gabriel Dulac-Arnold (Google Research), gave a talk on 'Challenges of Real-world RL: Definition, Implementation, Analysis'.
2020/10/01
Philipp Hennig - Computation under Uncertainty
We were recently joined by Philipp Hennig (University of Tuebingen), to talk about 'Computation under Uncertainty'.
2020/09/24
Philipp Hennig - Computation under Uncertainty
We were recently joined by Philipp Hennig (University of Tuebingen), to talk about 'Computation under Uncertainty'.
2020/09/24
Philipp Hennig - Computation under Uncertainty
We were recently joined by Philipp Hennig (University of Tuebingen), to talk about 'Computation under Uncertainty'.
2020/09/24
Philipp Hennig - Computation under Uncertainty
We were recently joined by Philipp Hennig (University of Tuebingen), to talk about 'Computation under Uncertainty'.
2020/09/24
Magnus Rattray - Non-parametric modelling of gene expression in time and space
We were recently joined by Magnus Rattray (University of Manchester), to talk about 'Non-parametric modelling of gene expression in time and space'.
2020/09/10
Magnus Rattray - Non-parametric modelling of gene expression in time and space
We were recently joined by Magnus Rattray (University of Manchester), to talk about 'Non-parametric modelling of gene expression in time and space'.
2020/09/10
Magnus Rattray - Non-parametric modelling of gene expression in time and space
We were recently joined by Magnus Rattray (University of Manchester), to talk about 'Non-parametric modelling of gene expression in time and space'.
2020/09/10
Magnus Rattray - Non-parametric modelling of gene expression in time and space
We were recently joined by Magnus Rattray (University of Manchester), to talk about 'Non-parametric modelling of gene expression in time and space'.
2020/09/10
Andreas Krause - Safe and Efficient Exploration in Reinforcement Learning
We were recently joined by Andreas Krause (ETH Zurich), to talk about 'Safe and Efficient Exploration in Reinforcement Learning'.
2020/08/27
Andreas Krause - Safe and Efficient Exploration in Reinforcement Learning
We were recently joined by Andreas Krause (ETH Zurich), to talk about 'Safe and Efficient Exploration in Reinforcement Learning'.
2020/08/27
Andreas Krause - Safe and Efficient Exploration in Reinforcement Learning
We were recently joined by Andreas Krause (ETH Zurich), to talk about 'Safe and Efficient Exploration in Reinforcement Learning'.
2020/08/27
Andreas Krause - Safe and Efficient Exploration in Reinforcement Learning
We were recently joined by Andreas Krause (ETH Zurich), to talk about 'Safe and Efficient Exploration in Reinforcement Learning'.
2020/08/27
Rahul Kidambi - MOReL: Model-Based Offline Reinforcement Learning
We were recently joined by Rahul Kidambi (Cornell University), to talk about 'MOReL: Model-Based Offline Reinforcement Learning'.
2020/08/06
Rahul Kidambi - MOReL: Model-Based Offline Reinforcement Learning
We were recently joined by Rahul Kidambi (Cornell University), to talk about 'MOReL: Model-Based Offline Reinforcement Learning'.
2020/08/06
Rahul Kidambi - MOReL: Model-Based Offline Reinforcement Learning
We were recently joined by Rahul Kidambi (Cornell University), to talk about 'MOReL: Model-Based Offline Reinforcement Learning'.
2020/08/06
Rahul Kidambi - MOReL: Model-Based Offline Reinforcement Learning
We were recently joined by Rahul Kidambi (Cornell University), to talk about 'MOReL: Model-Based Offline Reinforcement Learning'.
2020/08/06
Arthur Gretton - Generalized Energy-Based Models
We were recently joined by Arthur Gretton (University College London), to talk about 'Generalized Energy-Based Models'.
2020/07/30
Arthur Gretton - Generalized Energy-Based Models
We were recently joined by Arthur Gretton (University College London), to talk about 'Generalized Energy-Based Models'.
2020/07/30
Arthur Gretton - Generalized Energy-Based Models
We were recently joined by Arthur Gretton (University College London), to talk about 'Generalized Energy-Based Models'.
2020/07/30
Arthur Gretton - Generalized Energy-Based Models
We were recently joined by Arthur Gretton (University College London), to talk about 'Generalized Energy-Based Models'.
2020/07/30
Gergely Neu - A unified view of entropy-regularized Markov decision processes
We were recently joined by Gergely Neu (Pompeu Fabra University), to talk about 'A unified view of entropy-regularized Markov decision processes'.
2020/05/21
Gergely Neu - A unified view of entropy-regularized Markov decision processes
We were recently joined by Gergely Neu (Pompeu Fabra University), to talk about 'A unified view of entropy-regularized Markov decision processes'.
2020/05/21
Gergely Neu - A unified view of entropy-regularized Markov decision processes
We were recently joined by Gergely Neu (Pompeu Fabra University), to talk about 'A unified view of entropy-regularized Markov decision processes'.
2020/05/21
