Archive of past Machine Learning Coffee Seminar talks

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Jonas Arruda: Diffusion Models in Simulation-Based Inference: Post-Hoc Guidance and Composition
Jun 4
Jonas Arruda: Diffusion Models in Simulation-Based Inference: Post-Hoc Guidance and Composition

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Umberto Picchini: Scalable Bayesian inference in stochastic nonlinear mixed-effects models using semi-amortised likelihood and posterior approximations
Mar 31
Umberto Picchini: Scalable Bayesian inference in stochastic nonlinear mixed-effects models using semi-amortised likelihood and posterior approximations

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Harita Dellaporta: Model-based Distributionally Robust Optimisation: Bayesian Ambiguity Sets and Model Misspecification
Mar 4
Harita Dellaporta: Model-based Distributionally Robust Optimisation: Bayesian Ambiguity Sets and Model Misspecification

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Daniel Schmidt: Fundamental limitations of foundational time series forecasting models: The need for multimodality and rigorous evaluation
Jun 2
Daniel Schmidt: Fundamental limitations of foundational time series forecasting models: The need for multimodality and rigorous evaluation

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Klaus Nordhausen: Identifiable Variational Autoencoders for Modelling Multivariate Spatio-Temporal Data
May 26
Klaus Nordhausen: Identifiable Variational Autoencoders for Modelling Multivariate Spatio-Temporal Data

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François-Xavier Briol: Robust and Conjugate Gaussian Processes Regression
May 8
François-Xavier Briol: Robust and Conjugate Gaussian Processes Regression

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Ossi Räisä: Noise-Aware Differentially Private Regression via Meta-Learning
Apr 14
Ossi Räisä: Noise-Aware Differentially Private Regression via Meta-Learning

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Melih Kandemir: Embodied Estimators for Full Autonomy
Feb 20
Melih Kandemir: Embodied Estimators for Full Autonomy

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Linda Mannila: AI in the classroom – designing AI literacy for K-12 education
Dec 9
Linda Mannila: AI in the classroom – designing AI literacy for K-12 education

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Antti Oulasvirta: Better AI with Better Human Models
Dec 2
Antti Oulasvirta: Better AI with Better Human Models

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Grigorios Tsoumakas: Neural Abstractive Summarization
Nov 29
Grigorios Tsoumakas: Neural Abstractive Summarization

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Andrew Cropper: The Automatic Computer Scientist
Nov 11
Andrew Cropper: The Automatic Computer Scientist

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Toni Karvonen: Probabilistic Richardson extrapolation
Sep 30
Toni Karvonen: Probabilistic Richardson extrapolation

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Maximilian Naegele: Tackling Decision Processes with Non-Cumulative Objectives using Reinforcement Learning
Jun 17
Maximilian Naegele: Tackling Decision Processes with Non-Cumulative Objectives using Reinforcement Learning

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Mine Dogucu: Updating Beliefs on Bayesian Education: Reflections on Research and Teaching Projects
Jun 10
Mine Dogucu: Updating Beliefs on Bayesian Education: Reflections on Research and Teaching Projects

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Mikko Koivisto: Bayesian inference of causal graphs: where we are and where we should go
May 20
Mikko Koivisto: Bayesian inference of causal graphs: where we are and where we should go

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Matias Turkulainen: NeRFs and 3D Gaussian Splatting: Survey and Applications in 3D Computer Vision
May 6
Matias Turkulainen: NeRFs and 3D Gaussian Splatting: Survey and Applications in 3D Computer Vision

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Michael Mathioudakis: Algorithms for Efficient Machine Learning (ML)
Apr 22
Michael Mathioudakis: Algorithms for Efficient Machine Learning (ML)

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Mikko Mäkelä: Bayesian optimization for design of experiments
Apr 8
Mikko Mäkelä: Bayesian optimization for design of experiments

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David Leslie: Reinforcement learning in bandits and games
Mar 28
David Leslie: Reinforcement learning in bandits and games

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Chen He: VMS: Interactive Visualization to Support the Sensemaking and Selection of Predictive Models
Mar 25
Chen He: VMS: Interactive Visualization to Support the Sensemaking and Selection of Predictive Models

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Mashrura Musharraf: Navigating Tomorrow: Putting People at the Helm of Maritime Digitalization and Automation
Mar 11
Mashrura Musharraf: Navigating Tomorrow: Putting People at the Helm of Maritime Digitalization and Automation

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Daniel Schmidt: Fast and Accurate Probabilistic Time Series Classification
Feb 26
Daniel Schmidt: Fast and Accurate Probabilistic Time Series Classification

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Martin Trapp: Flexible and Efficient Probabilistic Modeling with Tractable Circuits
Feb 12
Martin Trapp: Flexible and Efficient Probabilistic Modeling with Tractable Circuits

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Ayush Bharti: Robust and sample-efficient simulation-based inference
Jan 29
Ayush Bharti: Robust and sample-efficient simulation-based inference

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Carl Henrik Ek: Modulated Surrogates for Bayesian Optimisation
Dec 14
Carl Henrik Ek: Modulated Surrogates for Bayesian Optimisation

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Jörg Tiedemann: Releasing the MAMMOTH - a framework for large-scale modular multilingual NLP models
Nov 27
Jörg Tiedemann: Releasing the MAMMOTH - a framework for large-scale modular multilingual NLP models

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Justin Domke: Monte Carlo Variational Inference
Nov 2
Justin Domke: Monte Carlo Variational Inference

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Aaro Järvinen: The role of machine learning in facilitating fusion energy research
Oct 30
Aaro Järvinen: The role of machine learning in facilitating fusion energy research

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Bo Zhao: Scalable and Flexible Distributed Reinforcement Learning Systems
Oct 16
Bo Zhao: Scalable and Flexible Distributed Reinforcement Learning Systems

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