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Mohammad Emtiyaz Khan
Person information
- affiliation: RIKEN Center for Advanced Intelligence, Tokyo, Japan
- affiliation: EPFL, Lausanne, Switzerland
- affiliation: University of British Columbia, Department of Computer Science, Vancouver, BC, Canada
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2020 – today
- 2024
- [c44]Nico Daheim, Thomas Möllenhoff, Edoardo M. Ponti, Iryna Gurevych, Mohammad Emtiyaz Khan:
Model Merging by Uncertainty-Based Gradient Matching. ICLR 2024 - [c43]Etash Kumar Guha, Shlok Natarajan, Thomas Möllenhoff, Mohammad Emtiyaz Khan, Eugène Ndiaye:
Conformal Prediction via Regression-as-Classification. ICLR 2024 - [c42]Theodore Papamarkou, Maria Skoularidou, Konstantina Palla, Laurence Aitchison, Julyan Arbel, David B. Dunson, Maurizio Filippone, Vincent Fortuin, Philipp Hennig, José Miguel Hernández-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 Rügamer, Yee Whye Teh, Max Welling, Andrew Gordon Wilson, Ruqi Zhang:
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI. ICML 2024 - [c41]Yuesong Shen, Nico Daheim, Bai Cong, Peter Nickl, Gian Maria Marconi, Clement Bazan, Rio Yokota, Iryna Gurevych, Daniel Cremers, Mohammad Emtiyaz Khan, Thomas Möllenhoff:
Variational Learning is Effective for Large Deep Networks. ICML 2024 - [i43]Theodore Papamarkou, Maria Skoularidou, Konstantina Palla, Laurence Aitchison, Julyan Arbel, David B. Dunson, Maurizio Filippone, Vincent Fortuin, Philipp Hennig, José Miguel Hernández-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 Rügamer, Yee Whye Teh, Max Welling, Andrew Gordon Wilson, Ruqi Zhang:
Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI. CoRR abs/2402.00809 (2024) - [i42]Yuesong Shen, Nico Daheim, Bai Cong, Peter Nickl, Gian Maria Marconi, Clement Bazan, Rio Yokota, Iryna Gurevych, Daniel Cremers, Mohammad Emtiyaz Khan, Thomas Möllenhoff:
Variational Learning is Effective for Large Deep Networks. CoRR abs/2402.17641 (2024) - [i41]Etash Kumar Guha, Shlok Natarajan, Thomas Möllenhoff, Mohammad Emtiyaz Khan, Eugène Ndiaye:
Conformal Prediction via Regression-as-Classification. CoRR abs/2404.08168 (2024) - [i40]Claudia Clopath, Ruben De Winne, Mohammad Emtiyaz Khan, Jacopo Margutti:
AI for Social Good (Dagstuhl Seminar 24082). Dagstuhl Reports 14(2): 182-190 (2024) - 2023
- [j5]Mohammad Emtiyaz Khan, Håvard Rue:
The Bayesian Learning Rule. J. Mach. Learn. Res. 24: 281:1-281:46 (2023) - [j4]Erik A. Daxberger, Siddharth Swaroop, Kazuki Osawa, Rio Yokota, Richard E. Turner, José Miguel Hernández-Lobato, Mohammad Emtiyaz Khan:
Improving Continual Learning by Accurate Gradient Reconstructions of the Past. Trans. Mach. Learn. Res. 2023 (2023) - [j3]Alexandre Piché, Valentin Thomas, Joseph Marino, Rafael Pardinas, Gian Maria Marconi, Christopher Pal, Mohammad Emtiyaz Khan:
Bridging the Gap Between Target Networks and Functional Regularization. Trans. Mach. Learn. Res. 2023 (2023) - [c40]Eren Mehmet Kiral, Thomas Möllenhoff, Mohammad Emtiyaz Khan:
The Lie-Group Bayesian Learning Rule. AISTATS 2023: 3331-3352 - [c39]Thomas Möllenhoff, Mohammad Emtiyaz Khan:
SAM as an Optimal Relaxation of Bayes. ICLR 2023 - [c38]Paul Edmund Chang, Prakhar Verma
, S. T. John, Arno Solin, Mohammad Emtiyaz Khan:
Memory-Based Dual Gaussian Processes for Sequential Learning. ICML 2023: 4035-4054 - [c37]Wu Lin, Valentin Duruisseaux, Melvin Leok, Frank Nielsen, Mohammad Emtiyaz Khan, Mark Schmidt:
Simplifying Momentum-based Positive-definite Submanifold Optimization with Applications to Deep Learning. ICML 2023: 21026-21050 - [c36]Peter Nickl, Lu Xu, Dharmesh Tailor, Thomas Möllenhoff, Mohammad Emtiyaz Khan:
The Memory-Perturbation Equation: Understanding Model's Sensitivity to Data. NeurIPS 2023 - [c35]Dharmesh Tailor, Mohammad Emtiyaz Khan, Eric T. Nalisnick:
Exploiting Inferential Structure in Neural Processes. UAI 2023: 2089-2098 - [i39]Wu Lin, Valentin Duruisseaux, Melvin Leok, Frank Nielsen, Mohammad Emtiyaz Khan, Mark Schmidt:
Simplifying Momentum-based Riemannian Submanifold Optimization. CoRR abs/2302.09738 (2023) - [i38]Eren Mehmet Kiral, Thomas Möllenhoff, Mohammad Emtiyaz Khan:
The Lie-Group Bayesian Learning Rule. CoRR abs/2303.04397 (2023) - [i37]Mohammad Emtiyaz Khan:
Variational Bayes Made Easy. CoRR abs/2304.14251 (2023) - [i36]Paul E. Chang, Prakhar Verma, S. T. John, Arno Solin, Mohammad Emtiyaz Khan:
Memory-Based Dual Gaussian Processes for Sequential Learning. CoRR abs/2306.03566 (2023) - [i35]Dharmesh Tailor, Mohammad Emtiyaz Khan, Eric T. Nalisnick:
Exploiting Inferential Structure in Neural Processes. CoRR abs/2306.15169 (2023) - [i34]Nico Daheim, Thomas Möllenhoff, Edoardo Maria Ponti, Iryna Gurevych, Mohammad Emtiyaz Khan:
Model Merging by Uncertainty-Based Gradient Matching. CoRR abs/2310.12808 (2023) - [i33]Peter Nickl, Lu Xu, Dharmesh Tailor, Thomas Möllenhoff, Mohammad Emtiyaz Khan:
The Memory Perturbation Equation: Understanding Model's Sensitivity to Data. CoRR abs/2310.19273 (2023) - 2022
- [i32]Thomas Möllenhoff, Mohammad Emtiyaz Khan:
SAM as an Optimal Relaxation of Bayes. CoRR abs/2210.01620 (2022) - [i31]Ganesh Tata, Gautham Krishna Gudur, Gopinath Chennupati, Mohammad Emtiyaz Khan:
Can Calibration Improve Sample Prioritization? CoRR abs/2210.06592 (2022) - 2021
- [c34]Alexander Immer, Matthias Bauer, Vincent Fortuin, Gunnar Rätsch, Mohammad Emtiyaz Khan:
Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning. ICML 2021: 4563-4573 - [c33]Wu Lin, Frank Nielsen, Mohammad Emtiyaz Khan, Mark Schmidt:
Tractable structured natural-gradient descent using local parameterizations. ICML 2021: 6680-6691 - [c32]Vincent Adam, Paul E. Chang, Mohammad Emtiyaz Khan, Arno Solin:
Dual Parameterization of Sparse Variational Gaussian Processes. NeurIPS 2021: 11474-11486 - [c31]Mohammad Emtiyaz Khan, Siddharth Swaroop:
Knowledge-Adaptation Priors. NeurIPS 2021: 19757-19770 - [c30]Ayush Jain, P. K. Srijith, Mohammad Emtiyaz Khan:
Subset-of-data variational inference for deep Gaussian-processes regression. UAI 2021: 1362-1370 - [i30]Wu Lin, Frank Nielsen, Mohammad Emtiyaz Khan, Mark Schmidt:
Tractable structured natural gradient descent using local parameterizations. CoRR abs/2102.07405 (2021) - [i29]Alexander Immer, Matthias Bauer, Vincent Fortuin, Gunnar Rätsch, Mohammad Emtiyaz Khan:
Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning. CoRR abs/2104.04975 (2021) - [i28]Alexandre Piché, Joseph Marino, Gian Maria Marconi, Christopher Joseph Pal, Mohammad Emtiyaz Khan:
Beyond Target Networks: Improving Deep Q-learning with Functional Regularization. CoRR abs/2106.02613 (2021) - [i27]Mohammad Emtiyaz Khan, Siddharth Swaroop:
Knowledge-Adaptation Priors. CoRR abs/2106.08769 (2021) - [i26]Mohammad Emtiyaz Khan, Håvard Rue
The Bayesian Learning Rule. CoRR abs/2107.04562 (2021) - [i25]Ayush Jain, P. K. Srijith, Mohammad Emtiyaz Khan:
Subset-of-Data Variational Inference for Deep Gaussian-Processes Regression. CoRR abs/2107.08265 (2021) - [i24]Wu Lin, Frank Nielsen, Mohammad Emtiyaz Khan, Mark Schmidt:
Structured second-order methods via natural gradient descent. CoRR abs/2107.10884 (2021) - [i23]Vincent Adam, Paul E. Chang, Mohammad Emtiyaz Khan, Arno Solin:
Dual Parameterization of Sparse Variational Gaussian Processes. CoRR abs/2111.03412 (2021) - 2020
- [c29]Chao Li, Mohammad Emtiyaz Khan, Zhun Sun, Gang Niu, Bo Han, Shengli Xie, Qibin Zhao:
Beyond Unfolding: Exact Recovery of Latent Convex Tensor Decomposition Under Reshuffling. AAAI 2020: 4602-4609 - [c28]Wu Lin, Mark Schmidt, Mohammad Emtiyaz Khan:
Handling the Positive-Definite Constraint in the Bayesian Learning Rule. ICML 2020: 6116-6126 - [c27]Xiangming Meng, Roman Bachmann, Mohammad Emtiyaz Khan:
Training Binary Neural Networks using the Bayesian Learning Rule. ICML 2020: 6852-6861 - [c26]Voot Tangkaratt, Bo Han, Mohammad Emtiyaz Khan, Masashi Sugiyama:
Variational Imitation Learning with Diverse-quality Demonstrations. ICML 2020: 9407-9417 - [c25]Paul E. Chang, William J. Wilkinson, Mohammad Emtiyaz Khan, Arno Solin
Fast Variational Learning in State-Space Gaussian Process Models. MLSP 2020: 1-6 - [c24]Pingbo Pan, Siddharth Swaroop, Alexander Immer, Runa Eschenhagen, Richard E. Turner, Mohammad Emtiyaz Khan:
Continual Deep Learning by Functional Regularisation of Memorable Past. NeurIPS 2020 - [i22]Wu Lin, Mark Schmidt, Mohammad Emtiyaz Khan:
Handling the Positive-Definite Constraint in the Bayesian Learning Rule. CoRR abs/2002.10060 (2020) - [i21]Xiangming Meng, Roman Bachmann, Mohammad Emtiyaz Khan:
Training Binary Neural Networks using the Bayesian Learning Rule. CoRR abs/2002.10778 (2020) - [i20]Pingbo Pan, Siddharth Swaroop, Alexander Immer, Runa Eschenhagen, Richard E. Turner, Mohammad Emtiyaz Khan:
Continual Deep Learning by Functional Regularisation of Memorable Past. CoRR abs/2004.14070 (2020) - [i19]Paul E. Chang, William J. Wilkinson, Mohammad Emtiyaz Khan, Arno Solin:
Fast Variational Learning in State-Space Gaussian Process Models. CoRR abs/2007.04731 (2020)
2010 – 2019
- 2019
- [j2]Simone Parisi
TD-regularized actor-critic methods. Mach. Learn. 108(8-9): 1467-1501 (2019) - [c23]Badr-Eddine Chérief-Abdellatif, Pierre Alquier, Mohammad Emtiyaz Khan:
A Generalization Bound for Online Variational Inference. ACML 2019: 662-677 - [c22]Wu Lin, Mohammad Emtiyaz Khan, Mark Schmidt:
Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-family Approximations. ICML 2019: 3992-4002 - [c21]Jiaxin Shi, Mohammad Emtiyaz Khan, Jun Zhu:
Scalable Training of Inference Networks for Gaussian-Process Models. ICML 2019: 5758-5768 - [c20]Mohammad Emtiyaz Khan, Alexander Immer, Ehsan Abedi, Maciej Korzepa:
Approximate Inference Turns Deep Networks into Gaussian Processes. NeurIPS 2019: 3088-3098 - [c19]Kazuki Osawa, Siddharth Swaroop, Mohammad Emtiyaz Khan, Anirudh Jain, Runa Eschenhagen, Richard E. Turner, Rio Yokota:
Practical Deep Learning with Bayesian Principles. NeurIPS 2019: 4289-4301 - [i18]Badr-Eddine Chérief-Abdellatif, Pierre Alquier, Mohammad Emtiyaz Khan:
A Generalization Bound for Online Variational Inference. CoRR abs/1904.03920 (2019) - [i17]Jiaxin Shi, Mohammad Emtiyaz Khan, Jun Zhu:
Scalable Training of Inference Networks for Gaussian-Process Models. CoRR abs/1905.10969 (2019) - [i16]Mohammad Emtiyaz Khan, Alexander Immer, Ehsan Abedi
Approximate Inference Turns Deep Networks into Gaussian Processes. CoRR abs/1906.01930 (2019) - [i15]Kazuki Osawa, Siddharth Swaroop, Anirudh Jain, Runa Eschenhagen, Richard E. Turner, Rio Yokota, Mohammad Emtiyaz Khan:
Practical Deep Learning with Bayesian Principles. CoRR abs/1906.02506 (2019) - [i14]Wu Lin, Mohammad Emtiyaz Khan, Mark Schmidt:
Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-family Approximations. CoRR abs/1906.02914 (2019) - [i13]Voot Tangkaratt, Bo Han, Mohammad Emtiyaz Khan, Masashi Sugiyama:
VILD: Variational Imitation Learning with Diverse-quality Demonstrations. CoRR abs/1909.06769 (2019) - [i12]Wu Lin, Mohammad Emtiyaz Khan, Mark Schmidt:
Stein's Lemma for the Reparameterization Trick with Exponential Family Mixtures. CoRR abs/1910.13398 (2019) - [i11]Claudia Clopath, Ruben De Winne, Mohammad Emtiyaz Khan, Tom Schaul:
AI for the Social Good (Dagstuhl Seminar 19082). Dagstuhl Reports 9(2): 111-122 (2019) - 2018
- [c18]Hongyi Ding, Mohammad Emtiyaz Khan, Issei Sato, Masashi Sugiyama:
Bayesian Nonparametric Poisson-Process Allocation for Time-Sequence Modeling. AISTATS 2018: 1108-1116 - [c17]Wu Lin, Nicolas Hubacher, Mohammad Emtiyaz Khan:
Variational Message Passing with Structured Inference Networks. ICLR (Poster) 2018 - [c16]Mohammad Emtiyaz Khan, Didrik Nielsen, Voot Tangkaratt, Wu Lin, Yarin Gal, Akash Srivastava:
Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam. ICML 2018: 2616-2625 - [c15]Mohammad Emtiyaz Khan, Didrik Nielsen:
Fast yet Simple Natural-Gradient Descent for Variational Inference in Complex Models. ISITA 2018: 31-35 - [c14]Aaron Mishkin, Frederik Kunstner, Didrik Nielsen, Mark Schmidt, Mohammad Emtiyaz Khan:
SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient. NeurIPS 2018: 6248-6258 - [i10]Chao Li, Mohammad Emtiyaz Khan, Shengli Xie, Qibin Zhao:
Low-Rank Tensor Decomposition via Multiple Reshaping and Reordering Operations. CoRR abs/1805.08465 (2018) - [i9]Mohammad Emtiyaz Khan, Didrik Nielsen, Voot Tangkaratt, Wu Lin, Yarin Gal, Akash Srivastava:
Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam. CoRR abs/1806.04854 (2018) - [i8]Mohammad Emtiyaz Khan, Didrik Nielsen:
Fast yet Simple Natural-Gradient Descent for Variational Inference in Complex Models. CoRR abs/1807.04489 (2018) - [i7]Aaron Mishkin, Frederik Kunstner, Didrik Nielsen, Mark Schmidt, Mohammad Emtiyaz Khan:
SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient. CoRR abs/1811.04504 (2018) - [i6]Simone Parisi, Voot Tangkaratt, Jan Peters, Mohammad Emtiyaz Khan:
TD-Regularized Actor-Critic Methods. CoRR abs/1812.08288 (2018) - 2017
- [c13]Mohammad Emtiyaz Khan, Wu Lin:
Conjugate-Computation Variational Inference: Converting Variational Inference in Non-Conjugate Models to Inferences in Conjugate Models. AISTATS 2017: 878-887 - [c12]Katarzyna Olejnik, Italo Dacosta, Joana Soares Machado, Kévin Huguenin
SmarPer: Context-Aware and Automatic Runtime-Permissions for Mobile Devices. IEEE Symposium on Security and Privacy 2017: 1058-1076 - [i5]Mohammad Emtiyaz Khan, Wu Lin:
Conjugate-Computation Variational Inference : Converting Variational Inference in Non-Conjugate Models to Inferences in Conjugate Models. CoRR abs/1703.04265 (2017) - [i4]Mohammad Emtiyaz Khan, Wu Lin, Voot Tangkaratt, Zuozhu Liu, Didrik Nielsen:
Variational Adaptive-Newton Method for Explorative Learning. CoRR abs/1711.05560 (2017) - [i3]Mohammad Emtiyaz Khan, Zuozhu Liu, Voot Tangkaratt, Yarin Gal:
Vprop: Variational Inference using RMSprop. CoRR abs/1712.01038 (2017) - 2016
- [c11]Vincent Etter, Mohammad Emtiyaz Khan, Matthias Grossglauser, Patrick Thiran:
Online Collaborative Prediction of Regional Vote Results. DSAA 2016: 233-242 - [c10]Mohammad Emtiyaz Khan, Reza Babanezhad, Wu Lin, Mark Schmidt, Masashi Sugiyama:
Faster Stochastic Variational Inference using Proximal-Gradient Methods with General Divergence Functions. UAI 2016 - 2015
- [i2]Mattia Carpin, Stefano Rosati, Mohammad Emtiyaz Khan, Bixio Rimoldi:
UAVs using Bayesian Optimization to Locate WiFi Devices. CoRR abs/1510.03592 (2015) - [i1]Mohammad Emtiyaz Khan, Reza Babanezhad, Wu Lin, Mark Schmidt, Masashi Sugiyama:
Convergence of Proximal-Gradient Stochastic Variational Inference under Non-Decreasing Step-Size Sequence. CoRR abs/1511.00146 (2015) - 2014
- [c9]Young-Jun Ko, Mohammad Emtiyaz Khan:
Variational Gaussian Inference for Bilinear Models of Count Data. ACML 2014 - [c8]Mohammad Emtiyaz Khan, Young-Jun Ko, Matthias W. Seeger:
Scalable Collaborative Bayesian Preference Learning. AISTATS 2014: 475-483 - 2013
- [c7]Mohammad Emtiyaz Khan, Aleksandr Y. Aravkin, Michael P. Friedlander, Matthias W. Seeger:
Fast Dual Variational Inference for Non-Conjugate Latent Gaussian Models. ICML (3) 2013: 951-959 - 2012
- [c6]Mohammad Emtiyaz Khan, Shakir Mohamed, Kevin P. Murphy:
Fast Bayesian Inference for Non-Conjugate Gaussian Process Regression. NIPS 2012: 3149-3157 - [c5]Mohammad Emtiyaz Khan, Shakir Mohamed, Benjamin M. Marlin, Kevin P. Murphy:
A Stick-Breaking Likelihood for Categorical Data Analysis with Latent Gaussian Models. AISTATS 2012: 610-618 - 2011
- [c4]Benjamin M. Marlin, Mohammad Emtiyaz Khan, Kevin P. Murphy:
Piecewise Bounds for Estimating Bernoulli-Logistic Latent Gaussian Models. ICML 2011: 633-640 - 2010
- [c3]Mohammad Emtiyaz Khan, Benjamin M. Marlin, Guillaume Bouchard, Kevin P. Murphy:
Variational bounds for mixed-data factor analysis. NIPS 2010: 1108-1116
2000 – 2009
- 2009
- [c2]Baback Moghaddam, Benjamin M. Marlin, Mohammad Emtiyaz Khan, Kevin P. Murphy:
Accelerating Bayesian Structural Inference for Non-Decomposable Gaussian Graphical Models. NIPS 2009: 1285-1293 - 2007
- [j1]Mohammad Emtiyaz Khan, Deshpande Narayan Dutt:
An Expectation-Maximization Algorithm Based Kalman Smoother Approach for Event-Related Desynchronization (ERD) Estimation from EEG. IEEE Trans. Biomed. Eng. 54(7): 1191-1198 (2007) - 2004
- [c1]Mohammad Emtiyaz Khan, D. Narayana Dutt:
Expectation-maximization (EM) algorithm for instantaneous frequency estimation with Kalman smoother. EUSIPCO 2004: 1797-1800
Coauthor Index
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