Mohammad Emtiyaz Khan

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

Achievements

+15 more ↓

🐣 Hot Topic Early Bird 🗺️ Taxonomy Completionist (13) 🌉 Interdisciplinary Bridge 🧭 Keyword Pioneer 🌍 Conference Polyglot (8)

🌍 Conference Polyglot (8) 🗺️ Taxonomy Completionist (13) 🐣 Hot Topic Early Bird 🌟 Keyword Trendsetter Combo (3) 👥 Mega-Team (25) 🏆 Grand Slam 🧬 Topic Evolution 🔬 Deep Specialist (18) 👑 Triple Crown 🏆 Keyword Champion 📈 Trend Setter 💎 Century Club (34) 🗃️ Keyword Collector (53) 🚀 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 Möllenhoff (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