Athénaïs Honorine Gautier

Krikamol Muandet: Kernel Mean Embedding with Applications in Deconfounded Causal Learning

Carl Henrik Ek: Modulated Surrogates for Bayesian Optimisation

Mark van der Wilk: Approximations, Inductive Biases, and their Connections in Gaussian Processes

Dario Azzimonti: Skew Gaussian Processes for classification, preference and mixed problems

Andrew Gordon Wilson: How should we build scalable Gaussian processes?

José Miguel Hernández-Lobato: Molecule optimization with deep generative models

Johanna Ziegel: Kernel scores: A versatile class of proper scoring rules for evaluating probabilistic forecasts

George Wynne: A Spectral View of Kernel Stein Discrepancy: Unlocking Infinite Dimensions

Florence d'Alché-Buc: Learning to predict complex outputs: a kernel view

Soham Sarkar: Kernel methods: past, present, future

Dario Azzimonti & Cédric Travelletti: Sequential design of experiments with Gaussian Process models

Athénaïs Gautier & David Ginsbourger: Flexible, probabilistic function modelling with Gaussian Processes

Danica Sutherland: Better deep learning (sometimes) by learning kernel mean embeddings

Chris Oates: Robust Generalised Bayesian Inference for Intractable Likelihoods

Richard Wilkinson: Adjoint-aided inference of Gaussian process driven differential equations

Michael Gutmann: Accelerating Approximate Bayesian Computation with Kernels and Decision Making under Uncertainty

ST John: Gaussian processes for non-Gaussian likelihoods

Peter Frazier: Grey-Box Bayesian Optimization

Niklas Wahlström: Linearly and nonlinearly constrained Gaussian processes

Pierre-Cyril Aubin-Frankowski (INRIA Paris): Handling infinitely many inequality constraints in function optimization problems using kernel methods

Jean Carlo Guella (UNICAMP): Recent theoretical results on MMD, Energy distance, HSIC and its generalizations

Linda Chamakh (polytechnique): Explicit Mean-Embeddings for Financial Portfolio

Manuel Schürch (IDSIA/USI): Correlated Product of Experts for Sparse Gaussian Process Regression

Ziang Niu (University of Pennsylvania): Discrepancy-based Inference for Intractable Generative Models using Quasi-Monte Carlo

Iain Henderson (INSA Toulouse): Stochastic Processes Under Linear Differential Constraints : Application to Gaussian Process Regression for the 3 Dimensional Free Space Wave Equation

Zachary A. Cosenza (University of California): Design of Cell Culture Media with Multi-Information Source Bayesian Optimization

Luc Brogat-Motte (Télécom Paris): Reduced-rank Regression in Structured Prediction

Raul Astudillo (Cornell University): Bayesian Optimization of Function Networks