Athénaïs Honorine Gautier

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Postdoc - 01, Pierre-Cyril Aubin-Frankowski (INRIA Paris): Handling infinitely many inequality constraints in function optimization problems using kernel methods

Postdoc - 03, Jean Carlo Guella (UNICAMP): Recent theoretical results on MMD, Energy distance, HSIC and its generalizations

Postdoc - 02, Linda Chamakh (polytechnique): Explicit Mean-Embeddings for Financial Portfolio

Graduate Student - 06, Manuel Schürch (IDSIA/USI): Correlated Product of Experts for Sparse Gaussian Process Regression

Graduate Student - 05, Ziang Niu (University of Pennsylvania): Discrepancy-based Inference for Intractable Generative Models using Quasi-Monte Carlo

Graduate Student - 04, Iain Henderson (INSA Toulouse): Stochastic Processes Under Linear Differential Constraints : Application to Gaussian Process Regression for the 3 Dimensional Free Space Wave Equation

Graduate Student - 03, Zachary A. Cosenza (University of California): Design of Cell Culture Media with Multi-Information Source Bayesian Optimization

Graduate Student - 02, Luc Brogat-Motte (Télécom Paris): Reduced-rank Regression in Structured Prediction

Graduate Student - 01, Raul Astudillo (Cornell University): Bayesian Optimization of Function Networks