LIKE22

David Ginsbourger, Universität Bern

2 November 2021, 19 videos, 228 views

Viewable by everyone.

1:34:57

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

12 January, 27 views

1:32:45

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

12 January, 7 views

1:36:25

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

12 January, 11 views

1:22:45

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

12 January, 9 views

1:39:14

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

17 January, 3 views

45:23

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

12 January, 19 views

1:19:50

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

12 January, 11 views

1:25:18

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

12 January, 8 views

44:51

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

12 January, 18 views

45:43

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

12 January, 21 views

46:50

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

12 January, 12 views

1:24:08

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

14 January, 5 views

42:32

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

14 January, 14 views

43:01

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

14 January, 6 views

1:06:30

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

14 January, 7 views

1:11:06

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

14 January, 16 views

1:14:58

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

14 January, 9 views

43:09

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

14 January, 6 views

46:58

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

14 January, 19 views