Mohammad Emtiyaz Khan

Jacopo Margutti

Dagstuhl Reports, 2024

How to Weight Multitask Finetuning? Fast Previews via Bayesian Model-Merging.

[BibT_eX]

[DOI]

Hugo Monzón Maldonado

Nico Daheim

Iryna Gurevych

CoRR, 2024

Variational Low-Rank Adaptation Using IVON.

[BibT_eX]

[DOI]

CoRR, 2024

Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI.

[BibT_eX]

[DOI]

CoRR, 2024

Variational Learning is Effective for Large Deep Networks.

[BibT_eX]

[DOI]

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI.

[BibT_eX]

[DOI]

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Conformal Prediction via Regression-as-Classification.

[BibT_eX]

[DOI]

Etash Kumar Guha

Shlok Natarajan

Eugène Ndiaye

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Model Merging by Uncertainty-Based Gradient Matching.

[BibT_eX]

[DOI]

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Bridging the Gap Between Target Networks and Functional Regularization.

[BibT_eX]

[DOI]

José Miguel Hernández-Lobato

Trans. Mach. Learn. Res., 2023

Improving Continual Learning by Accurate Gradient Reconstructions of the Past.

[BibT_eX]

[DOI]

Trans. Mach. Learn. Res., 2023

The Bayesian Learning Rule.

[BibT_eX]

[DOI]

Håvard Rue

J. Mach. Learn. Res., 2023

Variational Bayes Made Easy.

[BibT_eX]

[DOI]

CoRR, 2023

Simplifying Momentum-based Riemannian Submanifold Optimization.

[BibT_eX]

[DOI]

CoRR, 2023

Exploiting Inferential Structure in Neural Processes.

[BibT_eX]

[DOI]

Dharmesh Tailor

Eric T. Nalisnick

Proceedings of the Uncertainty in Artificial Intelligence, 2023

The Memory-Perturbation Equation: Understanding Model's Sensitivity to Data.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Simplifying Momentum-based Positive-definite Submanifold Optimization with Applications to Deep Learning.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2023

Memory-Based Dual Gaussian Processes for Sequential Learning.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2023

SAM as an Optimal Relaxation of Bayes.

[BibT_eX]

[DOI]

Proceedings of the Eleventh International Conference on Learning Representations, 2023

The Lie-Group Bayesian Learning Rule.

[BibT_eX]

[DOI]

Eren Mehmet Kiral

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Can Calibration Improve Sample Prioritization?

[BibT_eX]

[DOI]

Ganesh Tata

Gautham Krishna Gudur

Gopinath Chennupati

CoRR, 2022

2021

Structured second-order methods via natural gradient descent.

[BibT_eX]

[DOI]

Frank Nielsen

CoRR, 2021

Beyond Target Networks: Improving Deep Q-learning with Functional Regularization.

[BibT_eX]

[DOI]

Alexandre Piché

Joseph Marino

Gian Maria Marconi

Christopher Joseph Pal

CoRR, 2021

Subset-of-data variational inference for deep Gaussian-processes regression.

[BibT_eX]

[DOI]

Ayush Jain

P. K. Srijith

Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

Knowledge-Adaptation Priors.

[BibT_eX]

[DOI]

Siddharth Swaroop

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Dual Parameterization of Sparse Variational Gaussian Processes.

[BibT_eX]

[DOI]

Vincent Adam

Paul E. Chang

Arno Solin

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Tractable structured natural-gradient descent using local parameterizations.

[BibT_eX]

[DOI]

Frank Nielsen

Proceedings of the 38th International Conference on Machine Learning, 2021

Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning.

[BibT_eX]

[DOI]

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Continual Deep Learning by Functional Regularisation of Memorable Past.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Fast Variational Learning in State-Space Gaussian Process Models.

[BibT_eX]

[DOI]

Paul E. Chang

William J. Wilkinson

Arno Solin

Proceedings of the 30th IEEE International Workshop on Machine Learning for Signal Processing, 2020

Variational Imitation Learning with Diverse-quality Demonstrations.

[BibT_eX]

[DOI]

Bo Han

Masashi Sugiyama

Proceedings of the 37th International Conference on Machine Learning, 2020

Training Binary Neural Networks using the Bayesian Learning Rule.

[BibT_eX]

[DOI]

Xiangming Meng

Roman Bachmann

Proceedings of the 37th International Conference on Machine Learning, 2020

Handling the Positive-Definite Constraint in the Bayesian Learning Rule.

[BibT_eX]

[DOI]

Proceedings of the 37th International Conference on Machine Learning, 2020

Beyond Unfolding: Exact Recovery of Latent Convex Tensor Decomposition Under Reshuffling.

[BibT_eX]

[DOI]

Chao Li

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

TD-regularized actor-critic methods.

[BibT_eX]

[DOI]

Simone Parisi

Jan Peters

Mach. Learn., 2019

AI for the Social Good (Dagstuhl Seminar 19082).

[BibT_eX]

[DOI]

Claudia Clopath

Ruben De Winne

Tom Schaul

Dagstuhl Reports, 2019

Stein's Lemma for the Reparameterization Trick with Exponential Family Mixtures.

[BibT_eX]

[DOI]

CoRR, 2019

VILD: Variational Imitation Learning with Diverse-quality Demonstrations.

[BibT_eX]

[DOI]

Bo Han

Masashi Sugiyama

CoRR, 2019

Practical Deep Learning with Bayesian Principles.

[BibT_eX]

[DOI]

Kazuki Osawa

Siddharth Swaroop

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Approximate Inference Turns Deep Networks into Gaussian Processes.

[BibT_eX]

[DOI]

Alexander Immer

Ehsan Abedi

Maciej Korzepa

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Scalable Training of Inference Networks for Gaussian-Process Models.

[BibT_eX]

[DOI]

Jiaxin Shi

Jun Zhu

Proceedings of the 36th International Conference on Machine Learning, 2019

Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-family Approximations.

[BibT_eX]

[DOI]

Badr-Eddine Chérief-Abdellatif

Proceedings of the 36th International Conference on Machine Learning, 2019

A Generalization Bound for Online Variational Inference.

[BibT_eX]

[DOI]

Pierre Alquier

Proceedings of The 11th Asian Conference on Machine Learning, 2019

2018

Low-Rank Tensor Decomposition via Multiple Reshaping and Reordering Operations.

[BibT_eX]

[DOI]

Chao Li

Shengli Xie

Qibin Zhao

CoRR, 2018

SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Fast yet Simple Natural-Gradient Descent for Variational Inference in Complex Models.

[BibT_eX]

[DOI]

Didrik Nielsen

Proceedings of the International Symposium on Information Theory and Its Applications, 2018

Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam.

[BibT_eX]

[DOI]

Proceedings of the 35th International Conference on Machine Learning, 2018

Variational Message Passing with Structured Inference Networks.

[BibT_eX]

[DOI]

Nicolas Hubacher

Proceedings of the 6th International Conference on Learning Representations, 2018

Bayesian Nonparametric Poisson-Process Allocation for Time-Sequence Modeling.

[BibT_eX]

[DOI]

Hongyi Ding

Issei Sato

Masashi Sugiyama

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017

Vprop: Variational Inference using RMSprop.

[BibT_eX]

[DOI]

Zuozhu Liu

Yarin Gal

CoRR, 2017

Variational Adaptive-Newton Method for Explorative Learning.

[BibT_eX]

[DOI]

CoRR, 2017

SmarPer: Context-Aware and Automatic Runtime-Permissions for Mobile Devices.

[BibT_eX]

[DOI]

Jean-Pierre Hubaux

Proceedings of the 2017 IEEE Symposium on Security and Privacy, 2017

Conjugate-Computation Variational Inference: Converting Variational Inference in Non-Conjugate Models to Inferences in Conjugate Models.

[BibT_eX]

[DOI]

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016

Faster Stochastic Variational Inference using Proximal-Gradient Methods with General Divergence Functions.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, 2016

Online Collaborative Prediction of Regional Vote Results.

[BibT_eX]

[DOI]

Vincent Etter

Matthias Grossglauser

Patrick Thiran

Proceedings of the 2016 IEEE International Conference on Data Science and Advanced Analytics, 2016

2015

Convergence of Proximal-Gradient Stochastic Variational Inference under Non-Decreasing Step-Size Sequence.

[BibT_eX]

[DOI]

CoRR, 2015

UAVs using Bayesian Optimization to Locate WiFi Devices.

[BibT_eX]

[DOI]

Mattia Carpin

Stefano Rosati

Bixio Rimoldi

CoRR, 2015

2014

Scalable Collaborative Bayesian Preference Learning.

[BibT_eX]

[DOI]

Young-Jun Ko

Matthias W. Seeger

Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

Variational Gaussian Inference for Bilinear Models of Count Data.

[BibT_eX]

[DOI]

Young-Jun Ko

Proceedings of the Sixth Asian Conference on Machine Learning, 2014

2013

Fast Dual Variational Inference for Non-Conjugate Latent Gaussian Models.

[BibT_eX]

[DOI]

Aleksandr Y. Aravkin

Michael P. Friedlander

Matthias W. Seeger

Proceedings of the 30th International Conference on Machine Learning, 2013

2012

A Stick-Breaking Likelihood for Categorical Data Analysis with Latent Gaussian Models.

[BibT_eX]

[DOI]

Shakir Mohamed

Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

Fast Bayesian Inference for Non-Conjugate Gaussian Process Regression.

[BibT_eX]

[DOI]

Shakir Mohamed

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

2011

Piecewise Bounds for Estimating Bernoulli-Logistic Latent Gaussian Models.

[BibT_eX]

[DOI]

Proceedings of the 28th International Conference on Machine Learning, 2011

2010

Variational bounds for mixed-data factor analysis.

[BibT_eX]

[DOI]

Guillaume Bouchard

Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

2009

Accelerating Bayesian Structural Inference for Non-Decomposable Gaussian Graphical Models.

[BibT_eX]

[DOI]

Baback Moghaddam

Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

2007

An Expectation-Maximization Algorithm Based Kalman Smoother Approach for Event-Related Desynchronization (ERD) Estimation from EEG.

[BibT_eX]

[DOI]

Deshpande Narayan Dutt

IEEE Trans. Biomed. Eng., 2007

2004

Expectation-maximization (EM) algorithm for instantaneous frequency estimation with Kalman smoother.

[BibT_eX]

[DOI]