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06/27/2023
Exploiting Inferential Structure in Neural Processes
Neural Processes (NPs) are appealing due to their ability to perform fas...
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06/06/2023
Memory-Based Dual Gaussian Processes for Sequential Learning
Sequential learning with Gaussian processes (GPs) is challenging when ac...
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04/27/2023
Variational Bayes Made Easy
Variational Bayes is a popular method for approximate inference but its ...
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03/08/2023
The Lie-Group Bayesian Learning Rule
The Bayesian Learning Rule provides a framework for generic algorithm de...
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02/20/2023
Simplifying Momentum-based Riemannian Submanifold Optimization
Riemannian submanifold optimization with momentum is computationally cha...
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10/21/2022
Bridging the Gap Between Target Networks and Functional Regularization
Bootstrapping is behind much of the successes of Deep Reinforcement Lear...
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10/12/2022
Can Calibration Improve Sample Prioritization?
Calibration can reduce overconfident predictions of deep neural networks...
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10/04/2022
SAM as an Optimal Relaxation of Bayes
Sharpness-aware minimization (SAM) and related adversarial deep-learning...
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11/05/2021
Dual Parameterization of Sparse Variational Gaussian Processes
Sparse variational Gaussian process (SVGP) methods are a common choice f...
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07/22/2021
Structured second-order methods via natural gradient descent
In this paper, we propose new structured second-order methods and struct...
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07/09/2021
The Bayesian Learning Rule
We show that many machine-learning algorithms are specific instances of ...
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06/16/2021
Knowledge-Adaptation Priors
Humans and animals have a natural ability to quickly adapt to their surr...
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06/04/2021
Beyond Target Networks: Improving Deep Q-learning with Functional Regularization
Target networks are at the core of recent success in Reinforcement Learn...
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04/11/2021
Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning
Marginal-likelihood based model-selection, even though promising, is rar...
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02/15/2021
Tractable structured natural gradient descent using local parameterizations
Natural-gradient descent on structured parameter spaces (e.g., low-rank ...
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07/09/2020
Fast Variational Learning in State-Space Gaussian Process Models
Gaussian process (GP) regression with 1D inputs can often be performed i...
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04/29/2020
Continual Deep Learning by Functional Regularisation of Memorable Past
Continually learning new skills is important for intelligent systems, ye...
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02/25/2020
Training Binary Neural Networks using the Bayesian Learning Rule
Neural networks with binary weights are computation-efficient and hardwa...
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02/24/2020
Handling the Positive-Definite Constraint in the Bayesian Learning Rule
Bayesian learning rule is a recently proposed variational inference meth...
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10/29/2019
Stein's Lemma for the Reparameterization Trick with Exponential Family Mixtures
Stein's method (Stein, 1973; 1981) is a powerful tool for statistical ap...
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09/15/2019
VILD: Variational Imitation Learning with Diverse-quality Demonstrations
The goal of imitation learning (IL) is to learn a good policy from high-...
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06/07/2019
Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-family Approximations
Natural-gradient methods enable fast and simple algorithms for variation...
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06/06/2019
Practical Deep Learning with Bayesian Principles
Bayesian methods promise to fix many shortcomings of deep learning, but ...
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06/05/2019
Approximate Inference Turns Deep Networks into Gaussian Processes
Deep neural networks (DNN) and Gaussian processes (GP) are two powerful ...
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05/27/2019
Scalable Training of Inference Networks for Gaussian-Process Models
Inference in Gaussian process (GP) models is computationally challenging...
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04/08/2019
A Generalization Bound for Online Variational Inference
Bayesian inference provides an attractive online-learning framework to a...
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12/19/2018
TD-Regularized Actor-Critic Methods
Actor-critic methods can achieve incredible performance on difficult rei...
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11/11/2018
SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient
Uncertainty estimation in large deep-learning models is a computationall...
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07/12/2018
Fast yet Simple Natural-Gradient Descent for Variational Inference in Complex Models
Bayesian inference plays an important role in advancing machine learning...
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06/13/2018
Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam
Uncertainty computation in deep learning is essential to design robust a...
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05/22/2018
Low-Rank Tensor Decomposition via Multiple Reshaping and Reordering Operations
Tensor decomposition has been widely applied to find low-rank representa...
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03/15/2018
Variational Message Passing with Structured Inference Networks
Recent efforts on combining deep models with probabilistic graphical mod...
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12/04/2017
Vprop: Variational Inference using RMSprop
Many computationally-efficient methods for Bayesian deep learning rely o...
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11/15/2017
Variational Adaptive-Newton Method for Explorative Learning
We present the Variational Adaptive Newton (VAN) method which is a black...
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10/31/2015
Faster Stochastic Variational Inference using Proximal-Gradient Methods with General Divergence Functions
Several recent works have explored stochastic gradient methods for varia...
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10/13/2015
UAVs using Bayesian Optimization to Locate WiFi Devices
We address the problem of localizing non-collaborative WiFi devices in a...
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06/05/2013