Mohammad Emtiyaz Khan

research

∙ 06/27/2023

Exploiting Inferential Structure in Neural Processes

Neural Processes (NPs) are appealing due to their ability to perform fas...

0 Dharmesh Tailor, et al. ∙

research

∙ 06/06/2023

Memory-Based Dual Gaussian Processes for Sequential Learning

Sequential learning with Gaussian processes (GPs) is challenging when ac...

0 Paul E. Chang, et al. ∙

research

∙ 04/27/2023

Variational Bayes Made Easy

Variational Bayes is a popular method for approximate inference but its ...

0 Mohammad Emtiyaz Khan, et al. ∙

research

∙ 03/08/2023

The Lie-Group Bayesian Learning Rule

The Bayesian Learning Rule provides a framework for generic algorithm de...

0 Eren Mehmet Kıral, et al. ∙

research

∙ 02/20/2023

Simplifying Momentum-based Riemannian Submanifold Optimization

Riemannian submanifold optimization with momentum is computationally cha...

0 Wu Lin, et al. ∙

research

∙ 10/21/2022

Bridging the Gap Between Target Networks and Functional Regularization

Bootstrapping is behind much of the successes of Deep Reinforcement Lear...

0 Alexandre Piché, et al. ∙

research

∙ 10/12/2022

Can Calibration Improve Sample Prioritization?

Calibration can reduce overconfident predictions of deep neural networks...

5 Ganesh Tata, et al. ∙

research

∙ 10/04/2022

SAM as an Optimal Relaxation of Bayes

Sharpness-aware minimization (SAM) and related adversarial deep-learning...

0 Thomas Möllenhoff, et al. ∙

research

∙ 11/05/2021

Dual Parameterization of Sparse Variational Gaussian Processes

Sparse variational Gaussian process (SVGP) methods are a common choice f...

0 Vincent Adam, et al. ∙

research

∙ 07/22/2021

Structured second-order methods via natural gradient descent

In this paper, we propose new structured second-order methods and struct...

0 Wu Lin, et al. ∙

research

∙ 07/09/2021

The Bayesian Learning Rule

We show that many machine-learning algorithms are specific instances of ...

0 Mohammad Emtiyaz Khan, et al. ∙

research

∙ 06/16/2021

Knowledge-Adaptation Priors

Humans and animals have a natural ability to quickly adapt to their surr...

0 Mohammad Emtiyaz Khan, et al. ∙

research

∙ 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...

0 Alexandre Piché, et al. ∙

research

∙ 04/11/2021

Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning

Marginal-likelihood based model-selection, even though promising, is rar...

0 Alexander Immer, et al. ∙

research

∙ 02/15/2021

Tractable structured natural gradient descent using local parameterizations

Natural-gradient descent on structured parameter spaces (e.g., low-rank ...

0 Wu Lin, et al. ∙

research

∙ 07/09/2020

Fast Variational Learning in State-Space Gaussian Process Models

Gaussian process (GP) regression with 1D inputs can often be performed i...

0 Paul E. Chang, et al. ∙

research

∙ 04/29/2020

Continual Deep Learning by Functional Regularisation of Memorable Past

Continually learning new skills is important for intelligent systems, ye...

17 Pingbo Pan, et al. ∙

research

∙ 02/25/2020

Training Binary Neural Networks using the Bayesian Learning Rule

Neural networks with binary weights are computation-efficient and hardwa...

14 Xiangming Meng, et al. ∙

research

∙ 02/24/2020

Handling the Positive-Definite Constraint in the Bayesian Learning Rule

Bayesian learning rule is a recently proposed variational inference meth...

0 Wu Lin, et al. ∙

research

∙ 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...

0 Wu Lin, et al. ∙

research

∙ 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-...

0 Voot Tangkaratt, et al. ∙

research

∙ 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...

0 Wu Lin, et al. ∙

research

∙ 06/06/2019

Practical Deep Learning with Bayesian Principles

Bayesian methods promise to fix many shortcomings of deep learning, but ...

23 Kazuki Osawa, et al. ∙

research

∙ 06/05/2019

Approximate Inference Turns Deep Networks into Gaussian Processes

Deep neural networks (DNN) and Gaussian processes (GP) are two powerful ...

0 Mohammad Emtiyaz Khan, et al. ∙

research

∙ 05/27/2019

Scalable Training of Inference Networks for Gaussian-Process Models

Inference in Gaussian process (GP) models is computationally challenging...

0 Jiaxin Shi, et al. ∙

research

∙ 04/08/2019

A Generalization Bound for Online Variational Inference

Bayesian inference provides an attractive online-learning framework to a...

0 Badr-Eddine Chérief-Abdellatif, et al. ∙

research

∙ 12/19/2018

TD-Regularized Actor-Critic Methods

Actor-critic methods can achieve incredible performance on difficult rei...

4 Simone Parisi, et al. ∙

research

∙ 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...

0 Aaron Mishkin, et al. ∙

research

∙ 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...

0 Mohammad Emtiyaz Khan, et al. ∙

research

∙ 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...

0 Mohammad Emtiyaz Khan, et al. ∙

research

∙ 05/22/2018

Low-Rank Tensor Decomposition via Multiple Reshaping and Reordering Operations

Tensor decomposition has been widely applied to find low-rank representa...

0 Chao Li, et al. ∙

research

∙ 03/15/2018

Variational Message Passing with Structured Inference Networks

Recent efforts on combining deep models with probabilistic graphical mod...

0 Wu Lin, et al. ∙

research

∙ 12/04/2017

Vprop: Variational Inference using RMSprop

Many computationally-efficient methods for Bayesian deep learning rely o...

0 Mohammad Emtiyaz Khan, et al. ∙

research

∙ 11/15/2017

Variational Adaptive-Newton Method for Explorative Learning

We present the Variational Adaptive Newton (VAN) method which is a black...

0 Mohammad Emtiyaz Khan, et al. ∙

research

∙ 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...

0 Mohammad Emtiyaz Khan, et al. ∙

research

∙ 10/13/2015

UAVs using Bayesian Optimization to Locate WiFi Devices

We address the problem of localizing non-collaborative WiFi devices in a...

0 Mattia Carpin, et al. ∙

research

∙ 06/05/2013

Fast Dual Variational Inference for Non-Conjugate LGMs

Latent Gaussian models (LGMs) are widely used in statistics and machine ...

0 Mohammad Emtiyaz Khan, et al. ∙

Mohammad Emtiyaz Khan

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