Mohammad Emtiyaz Khan
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
32
Venues
9
Active years
2011–2025
Best venue rank
A*
Where they publish
Papers
32 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2025 | ICLR | Connecting Federated ADMM to Bayes. | Siddharth Swaroop, Mohammad Emtiyaz Khan, Finale Doshi-Velez |
| 2024 | ICLR | Model Merging by Uncertainty-Based Gradient Matching. | Nico Daheim, Thomas Mllenhoff, Edoardo M. Ponti, Iryna Gurevych, Mohammad Emtiyaz Khan |
| 2024 | ICLR | Conformal Prediction via Regression-as-Classification. | Etash Kumar Guha, Shlok Natarajan, Thomas Mllenhoff, Mohammad Emtiyaz Khan, Eugne Ndiaye |
| 2024 | ICML | Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI. | Theodore Papamarkou, Maria Skoularidou, Konstantina Palla, Laurence Aitchison, Julyan Arbel, David B. Dunson, Maurizio Filippone, Vincent Fortuin, Philipp Hennig, Jos Miguel Hernndez-Lobato, Aliaksandr Hubin, Alexander Immer, Theofanis Karaletsos, Mohammad Emtiyaz Khan, Agustinus Kristiadi, Yingzhen Li, Stephan Mandt, Christopher Nemeth, Michael A. Osborne, Tim G. J. Rudner, David Rgamer, Yee Whye Teh, Max Welling, Andrew Gordon Wilson, Ruqi Zhang |
| 2024 | ICML | Variational Learning is Effective for Large Deep Networks. | Yuesong Shen, Nico Daheim, Bai Cong, Peter Nickl, Gian Maria Marconi, Clement Bazan, Rio Yokota, Iryna Gurevych, Daniel Cremers, Mohammad Emtiyaz Khan, Thomas Mllenhoff |
| 2023 | AISTATS | The Lie-Group Bayesian Learning Rule. | Eren Mehmet Kiral, Thomas Mllenhoff, Mohammad Emtiyaz Khan |
| 2023 | ICLR | SAM as an Optimal Relaxation of Bayes. | Thomas Mllenhoff, Mohammad Emtiyaz Khan |
| 2023 | ICML | Memory-Based Dual Gaussian Processes for Sequential Learning. | Paul Edmund Chang, Prakhar Verma, S. T. John, Arno Solin, Mohammad Emtiyaz Khan |
| 2023 | ICML | Simplifying Momentum-based Positive-definite Submanifold Optimization with Applications to Deep Learning. | Wu Lin, Valentin Duruisseaux, Melvin Leok, Frank Nielsen, Mohammad Emtiyaz Khan, Mark Schmidt |
| 2023 | UAI | Exploiting Inferential Structure in Neural Processes. | Dharmesh Tailor, Mohammad Emtiyaz Khan, Eric T. Nalisnick |
| 2021 | ICML | Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning. | Alexander Immer, Matthias Bauer, Vincent Fortuin, Gunnar Rtsch, Mohammad Emtiyaz Khan |
| 2021 | ICML | Tractable structured natural-gradient descent using local parameterizations. | Wu Lin, Frank Nielsen, Mohammad Emtiyaz Khan, Mark Schmidt |
| 2021 | UAI | Subset-of-data variational inference for deep Gaussian-processes regression. | Ayush Jain, P. K. Srijith, Mohammad Emtiyaz Khan |
| 2020 | AAAI | Beyond Unfolding: Exact Recovery of Latent Convex Tensor Decomposition Under Reshuffling. | Chao Li, Mohammad Emtiyaz Khan, Zhun Sun, Gang Niu, Bo Han, Shengli Xie, Qibin Zhao |
| 2020 | ICML | Handling the Positive-Definite Constraint in the Bayesian Learning Rule. | Wu Lin, Mark Schmidt, Mohammad Emtiyaz Khan |
| 2020 | ICML | Training Binary Neural Networks using the Bayesian Learning Rule. | Xiangming Meng, Roman Bachmann, Mohammad Emtiyaz Khan |
| 2020 | ICML | Variational Imitation Learning with Diverse-quality Demonstrations. | Voot Tangkaratt, Bo Han, Mohammad Emtiyaz Khan, Masashi Sugiyama |
| 2019 | ACML | A Generalization Bound for Online Variational Inference. | Badr-Eddine Chrief-Abdellatif, Pierre Alquier, Mohammad Emtiyaz Khan |
| 2019 | ICML | Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-family Approximations. | Wu Lin, Mohammad Emtiyaz Khan, Mark Schmidt |
| 2019 | ICML | Scalable Training of Inference Networks for Gaussian-Process Models. | Jiaxin Shi, Mohammad Emtiyaz Khan, Jun Zhu |
| 2018 | AISTATS | Bayesian Nonparametric Poisson-Process Allocation for Time-Sequence Modeling. | Hongyi Ding, Mohammad Emtiyaz Khan, Issei Sato, Masashi Sugiyama |
| 2018 | ICLR | Variational Message Passing with Structured Inference Networks. | Wu Lin, Nicolas Hubacher, Mohammad Emtiyaz Khan |
| 2018 | ICML | Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam. | Mohammad Emtiyaz Khan, Didrik Nielsen, Voot Tangkaratt, Wu Lin, Yarin Gal, Akash Srivastava |
| 2018 | ISITA | Fast yet Simple Natural-Gradient Descent for Variational Inference in Complex Models. | Mohammad Emtiyaz Khan, Didrik Nielsen |
| 2017 | AISTATS | Conjugate-Computation Variational Inference: Converting Variational Inference in Non-Conjugate Models to Inferences in Conjugate Models. | Mohammad Emtiyaz Khan, Wu Lin |
| 2017 | SP | SmarPer: Context-Aware and Automatic Runtime-Permissions for Mobile Devices. | Katarzyna Olejnik, Italo Dacosta, Joana Soares Machado, Kvin Huguenin, Mohammad Emtiyaz Khan, Jean-Pierre Hubaux |
| 2016 | DSAA | Online Collaborative Prediction of Regional Vote Results. | Vincent Etter, Mohammad Emtiyaz Khan, Matthias Grossglauser, Patrick Thiran |
| 2016 | UAI | Faster Stochastic Variational Inference using Proximal-Gradient Methods with General Divergence Functions. | Mohammad Emtiyaz Khan, Reza Babanezhad, Wu Lin, Mark Schmidt, Masashi Sugiyama |
| 2014 | ACML | Variational Gaussian Inference for Bilinear Models of Count Data. | Young-Jun Ko, Mohammad Emtiyaz Khan |
| 2014 | AISTATS | Scalable Collaborative Bayesian Preference Learning. | Mohammad Emtiyaz Khan, Young-Jun Ko, Matthias W. Seeger |
| 2013 | ICML | Fast Dual Variational Inference for Non-Conjugate Latent Gaussian Models. | Mohammad Emtiyaz Khan, Aleksandr Y. Aravkin, Michael P. Friedlander, Matthias W. Seeger |
| 2011 | ICML | Piecewise Bounds for Estimating Bernoulli-Logistic Latent Gaussian Models. | Benjamin M. Marlin, Mohammad Emtiyaz Khan, Kevin P. Murphy |