LIKE22

David Ginsbourger, Universität Bern

2 November 2021, 19 videos, 359 views

Viewable by everyone.

January 11 #3, Niklas Wahlström: Linearly and nonlinearly constrained Gaussian processes

12 January 2022, 22 views

January 11 #4, Peter Frazier: Grey-Box Bayesian Optimization

12 January 2022, 17 views

January 10 #4, ST John: Gaussian processes for non-Gaussian likelihoods

12 January 2022, 35 views

January 12 #1, Michael Gutmann: Accelerating Approximate Bayesian Computation with Kernels and Decision Making under Uncertainty

12 January 2022, 14 views

January 12 #2, Richard Wilkinson: Adjoint-aided inference of Gaussian process driven differential equations

12 January 2022, 20 views

January 12 #3, Chris Oates: Robust Generalised Bayesian Inference for Intractable Likelihoods

12 January 2022, 28 views

January 12 #6, Danica Sutherland: Better deep learning (sometimes) by learning kernel mean embeddings

12 January 2022, 20 views

January 10 #1, Athénaïs Gautier & David Ginsbourger: Flexible, probabilistic function modelling with Gaussian Processes

12 January 2022, 39 views

January 10 #2, Dario Azzimonti & Cédric Travelletti: Sequential design of experiments with Gaussian Process models

12 January 2022, 12 views

January 10 #3, Soham Sarkar: Kernel methods: past, present, future

12 January 2022, 18 views

January 13 #1, Florence d'Alché-Buc: Learning to predict complex outputs: a kernel view

14 January 2022, 10 views

January 13 #2, George Wynne: A Spectral View of Kernel Stein Discrepancy: Unlocking Infinite Dimensions

14 January 2022, 17 views

January 13 #3, Johanna Ziegel: Kernel scores: A versatile class of proper scoring rules for evaluating probabilistic forecasts

14 January 2022, 28 views

January 13 #4, José Miguel Hernández-Lobato: Molecule optimization with deep generative models

14 January 2022, 10 views

January 13 #5, Andrew Gordon Wilson: How should we build scalable Gaussian processes?

14 January 2022, 17 views

January 14 #2, Dario Azzimonti: Skew Gaussian Processes for classification, preference and mixed problems

14 January 2022, 7 views

January 14 #1, Mark van der Wilk: Approximations, Inductive Biases, and their Connections in Gaussian Processes

14 January 2022, 12 views

January 14 #3, Carl Henrik Ek: Modulated Surrogates for Bayesian Optimisation

14 January 2022, 20 views

January 11 #1, Krikamol Muandet: Kernel Mean Embedding with Applications in Deconfounded Causal Learning

17 January 2022, 13 views