
Making Differentiable Architecture Search less local
Neural architecture search (NAS) is a recent methodology for automating ...
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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...
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Blackbox density function estimation using recursive partitioning
We present a novel approach to Bayesian inference and general Bayesian c...
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MetaSurrogate Benchmarking for Hyperparameter Optimization
Despite the recent progress in hyperparameter optimization (HPO), availa...
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Variational Information Distillation for Knowledge Transfer
Transferring knowledge from a teacher neural network pretrained on the s...
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Intrinsic Gaussian processes on complex constrained domains
We propose a class of intrinsic Gaussian processes (inGPs) for interpol...
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Truncated Variational Sampling for "Black Box" Optimization of Generative Models
We investigate the optimization of two generative models with binary hid...
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AutoDifferentiating Linear Algebra
Development systems for deep learning, such as Theano, Torch, TensorFlow...
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Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes
Often in machine learning, data are collected as a combination of multip...
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Preferential Bayesian Optimization
Bayesian optimization (BO) has emerged during the last few years as an e...
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Spatiotemporal Gaussian processes modeling of dynamical systems in systems biology
Quantitative modeling of posttranscriptional regulation process is a ch...
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Unsupervised Learning with Imbalanced Data via Structure Consolidation Latent Variable Model
Unsupervised learning on imbalanced data is challenging because, when gi...
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Recurrent Gaussian Processes
We define Recurrent Gaussian Processes (RGP) models, a general family of...
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Variational Autoencoded Deep Gaussian Processes
We develop a scalable deep nonparametric generative model by augmenting...
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Batch Bayesian Optimization via Local Penalization
The popularity of Bayesian optimization methods for efficient exploratio...
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Spike and Slab Gaussian Process Latent Variable Models
The Gaussian process latent variable model (GPLVM) is a popular approac...
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GPselect: Accelerating EM using adaptive subspace preselection
We propose a nonparametric procedure to achieve fast inference in genera...
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Gaussian Process Models with Parallelization and GPU acceleration
In this work, we present an extension of Gaussian process (GP) models wi...
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Autonomous Cleaning of Corrupted Scanned Documents  A Generative Modeling Approach
We study the task of cleaning scanned text documents that are strongly c...
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