research
∙
02/06/2023
A Strong Baseline for Batch Imitation Learning
Imitation of expert behaviour is a highly desirable and safe approach to...
share
research
∙
01/16/2023
Intrinsic Gaussian Process on Unknown Manifolds with Probabilistic Metrics
This article presents a novel approach to construct Intrinsic Gaussian P...
research
∙
05/27/2021
Model Selection for Production System via Automated Online Experiments
A challenge that machine learning practitioners in the industry face is ...
research
∙
04/21/2021
Making Differentiable Architecture Search less local
Neural architecture search (NAS) is a recent methodology for automating ...
research
∙
10/28/2020
The Evidence Lower Bound of Variational Autoencoders Converges to a Sum of Three Entropies
The central objective function of a variational autoencoder (VAE) is its...
research
∙
10/26/2020
Black-box density function estimation using recursive partitioning
We present a novel approach to Bayesian inference and general Bayesian c...
research
∙
05/30/2019
Meta-Surrogate Benchmarking for Hyperparameter Optimization
Despite the recent progress in hyperparameter optimization (HPO), availa...
research
∙
04/11/2019
Variational Information Distillation for Knowledge Transfer
Transferring knowledge from a teacher neural network pretrained on the s...
research
∙
01/03/2018
Intrinsic Gaussian processes on complex constrained domains
We propose a class of intrinsic Gaussian processes (in-GPs) for interpol...
research
∙
12/21/2017
Truncated Variational Sampling for "Black Box" Optimization of Generative Models
We investigate the optimization of two generative models with binary hid...
research
∙
10/24/2017
Auto-Differentiating Linear Algebra
Development systems for deep learning, such as Theano, Torch, TensorFlow...
research
∙
05/27/2017
Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes
Often in machine learning, data are collected as a combination of multip...
research
∙
04/12/2017
Preferential Bayesian Optimization
Bayesian optimization (BO) has emerged during the last few years as an e...
research
∙
10/17/2016
Spatio-temporal Gaussian processes modeling of dynamical systems in systems biology
Quantitative modeling of post-transcriptional regulation process is a ch...
research
∙
06/30/2016
Unsupervised Learning with Imbalanced Data via Structure Consolidation Latent Variable Model
Unsupervised learning on imbalanced data is challenging because, when gi...
research
∙
11/20/2015
Recurrent Gaussian Processes
We define Recurrent Gaussian Processes (RGP) models, a general family of...
research
∙
11/19/2015
Variational Auto-encoded Deep Gaussian Processes
We develop a scalable deep non-parametric generative model by augmenting...
research
∙
05/29/2015
Batch Bayesian Optimization via Local Penalization
The popularity of Bayesian optimization methods for efficient exploratio...
research
∙
05/10/2015
Spike and Slab Gaussian Process Latent Variable Models
The Gaussian process latent variable model (GP-LVM) is a popular approac...
research
∙
12/10/2014
GP-select: Accelerating EM using adaptive subspace preselection
We propose a nonparametric procedure to achieve fast inference in genera...
research
∙
10/18/2014
Gaussian Process Models with Parallelization and GPU acceleration
In this work, we present an extension of Gaussian process (GP) models wi...
research
∙
01/12/2012