← Authors

Mohammad Emtiyaz Khan

34 papers · 2009–2025 · 8 conferences · across top CS/AI conferences

Achievements

🧭 Keyword Pioneer 🌈 Renaissance Researcher (7) 🐝 Cross-Pollinator (12) πŸŒ‰ Interdisciplinary Bridge πŸƒ Academic Marathon (16) 🌍 Conference Polyglot (8) πŸ—ΊοΈ Taxonomy Completionist (13) 🐣 Hot Topic Early Bird 🌟 Keyword Trendsetter Combo (3) πŸ‘₯ Mega-Team (25) πŸ† Grand Slam 🧬 Topic Evolution πŸ”¬ Deep Specialist (18) πŸ‘‘ Triple Crown πŸ† Keyword Champion πŸ“ˆ Trend Setter πŸ’Ž Century Club (34) πŸ—ƒοΈ Keyword Collector (53) πŸš€ Conference Pioneer ⚑ Prolific Year (5)

Conferences

NIPS (12) ICML (10) ICLR (5) AISTATS (2) UAI (2) AAAI (1) ACML (1) JMLR (1)

Top co-authors

Thomas MΓΆllenhoff (5) Siddharth Swaroop (4) Mark Schmidt (4) Wu Lin (4) Kevin P. Murphy (3) Alexander Immer (3) Bo Han (2) Dharmesh Tailor (2) Arno Solin (2) Benjamin M. Marlin (2)

Keywords

variational inference (16) bayesian inference (12) gaussian process (7) natural gradient (5) continual learning (4) convex optimization (3) bayesian learning rule (3) non-conjugate model (2) bayesian deep learning (2) gaussian processes (2) latent variable (2) uncertainty estimation (2) non-conjugate likelihood (2) stochastic process (2) latent variable model (2) matrix factorization (2) exponential family (2) graphical model (2) imitation learning (1) learning theory (1)

Papers

Connecting Federated ADMM to Bayes ICLR 2025
Model Merging by Uncertainty-Based Gradient Matching ICLR 2024
Variational Learning is Effective for Large Deep Networks ICML 2024
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI ICML 2024
Conformal Prediction via Regression-as-Classification ICLR 2024
Exploiting Inferential Structure in Neural Processes UAI 2023
The Memory-Perturbation Equation: Understanding Model's Sensitivity to Data NIPS 2023
The Lie-Group Bayesian Learning Rule AISTATS 2023
SAM as an Optimal Relaxation of Bayes ICLR 2023
Memory-Based Dual Gaussian Processes for Sequential Learning ICML 2023
Simplifying Momentum-based Positive-definite Submanifold Optimization with Applications to Deep Learning ICML 2023
The Bayesian Learning Rule JMLR 2023
Dual Parameterization of Sparse Variational Gaussian Processes NIPS 2021
Subset-of-data variational inference for deep Gaussian-processes regression UAI 2021
Knowledge-Adaptation Priors NIPS 2021
Beyond Unfolding: Exact Recovery of Latent Convex Tensor Decomposition Under Reshuffling AAAI 2020
Handling the Positive-Definite Constraint in the Bayesian Learning Rule ICML 2020
Training Binary Neural Networks using the Bayesian Learning Rule ICML 2020
Variational Imitation Learning with Diverse-quality Demonstrations ICML 2020
Continual Deep Learning by Functional Regularisation of Memorable Past NIPS 2020
Approximate Inference Turns Deep Networks into Gaussian Processes NIPS 2019
Practical Deep Learning with Bayesian Principles NIPS 2019
Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-family Approximations ICML 2019
Scalable Training of Inference Networks for Gaussian-Process Models ICML 2019
A Generalization Bound for Online Variational Inference ACML 2019
SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient NIPS 2018
Variational Message Passing with Structured Inference Networks ICLR 2018
Kullback-Leibler Proximal Variational Inference NIPS 2015
Decoupled Variational Gaussian Inference NIPS 2014
Scalable Collaborative Bayesian Preference Learning AISTATS 2014
Fast Dual Variational Inference for Non-Conjugate Latent Gaussian Models ICML 2013
Fast Bayesian Inference for Non-Conjugate Gaussian Process Regression NIPS 2012
Variational bounds for mixed-data factor analysis NIPS 2010
Accelerating Bayesian Structural Inference for Non-Decomposable Gaussian Graphical Models NIPS 2009