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03/02/2021
Fast Adaptation with Linearized Neural Networks
The inductive biases of trained neural networks are difficult to underst...
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06/30/2020
Tomographic Auto-Encoder: Unsupervised Bayesian Recovery of Corrupted Data
We propose a new probabilistic method for unsupervised recovery of corru...
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04/27/2020
Empirical Bayes Transductive Meta-Learning with Synthetic Gradients
We propose a meta-learning approach that learns from multiple tasks in a...
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04/07/2020
Online Constrained Model-based Reinforcement Learning
Applying reinforcement learning to robotic systems poses a number of cha...
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11/24/2019
ORL: Reinforcement Learning Benchmarks for Online Stochastic Optimization Problems
Reinforcement Learning (RL) has achieved state-of-the-art results in dom...
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04/11/2019
Variational Information Distillation for Knowledge Transfer
Transferring knowledge from a teacher neural network pretrained on the s...
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03/18/2019
Deep Gaussian Processes for Multi-fidelity Modeling
Multi-fidelity methods are prominently used when cheaply-obtained, but p...
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12/03/2018
Transferring Knowledge across Learning Processes
In complex transfer learning scenarios new tasks might not be tightly li...
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06/05/2018
Deep Gaussian Processes with Convolutional Kernels
Deep Gaussian processes (DGPs) provide a Bayesian non-parametric alterna...
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03/12/2018
Leveraging Crowdsourcing Data For Deep Active Learning - An Application: Learning Intents in Alexa
This paper presents a generic Bayesian framework that enables any deep l...
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06/12/2017
DAC-h3: A Proactive Robot Cognitive Architecture to Acquire and Express Knowledge About the World and the Self
This paper introduces a cognitive architecture for a humanoid robot to e...
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04/12/2017
Preferential Bayesian Optimization
Bayesian optimization (BO) has emerged during the last few years as an e...
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01/12/2017
Manifold Alignment Determination: finding correspondences across different data views
We present Manifold Alignment Determination (MAD), an algorithm for lear...
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04/17/2016
Multi-view Learning as a Nonparametric Nonlinear Inter-Battery Factor Analysis
Factor analysis aims to determine latent factors, or traits, which summa...
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12/26/2015
Inverse Reinforcement Learning via Deep Gaussian Process
We propose a new approach to inverse reinforcement learning (IRL) based ...
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11/20/2015
Recurrent Gaussian Processes
We define Recurrent Gaussian Processes (RGP) models, a general family of...
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11/19/2015
Variational Auto-encoded Deep Gaussian Processes
We develop a scalable deep non-parametric generative model by augmenting...
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09/03/2015
Semi-described and semi-supervised learning with Gaussian processes
Propagating input uncertainty through non-linear Gaussian process (GP) m...
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10/18/2014
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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01/15/2013
Factorized Topic Models
In this paper we present a modification to a latent topic model, which m...
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06/18/2012