List of Publications

Early Drafts/Preprints

• User-friendly introduction to PAC-Bayes bounds,
  (Preprint) P. Alquier, [ arXiv ]
• The Bayesian Learning Rule,
  (Preprint) M.E. Khan, H. Rue [ arXiv ]
• Beyond Target Networks: Improving Deep Q-learning with Functional Regularization,
  (Preprint) A. Piché, J. Marino, G. M. Marconi, C. Pal, M. E. Khan [arXiv]
• Deviation inequalities for stochastic approximation by averaging,
  (Preprint) X. Fan, P. Alquier, P. Doukhan [arXiv]
• Universal Robust Regression via Maximum Mean Discrepancy,
  (Preprint) P. Alquier, M. Gerber [arXiv]

2022

• Understanding the Population Structure Correction Regression,
  (ICSTA 2022) T. T. Mai, P. Alquier [to appear] [arXiv]
• Tight Risk Bound for High Dimensional Time Series Completion,
  (EJS) P. Alquier, N. Marie, A. Rosier [Published version] [arXiv]
• Estimation of Copulas via Maximum Mean Discrepancy,
  (JASA) P. Alquier, B.-E. Chérief-Abdellatif, A. Derumigny, J.-D. Fermanian [Published version] [arXiv]
• Finite sample properties of parametric MMD estimation: robustness to misspecification and dependence,
  (Bernoulli) B.E. Chérief-Abdellatif, P. Alquier [Published version] [arXiv]

2021

• Knowledge-Adaptation Priors,
  (NeurIPS 2021) M.E. Khan, S. Swaroop [Published version] [arXiv] [Slides] [Tweet] [SlidesLive Video] [Code]
• Dual Parameterization of Sparse Variational Gaussian Processes,
  (NeurIPS 2021) P. Chang, V. Adam, M.E. Khan, A. Solin [Published version] [arXiv] [Code]
• Meta-strategy for Learning Tuning Parameters with Guarantees,
  (Entropy) D. Meunier, P. Alquier [Published version] [arXiv]
• Subset-of-Data Variational Inference for Deep Gaussian-Process Regression,
  (UAI 2021) A. Jain, P.K. Srijith, M.E. Khan, [Published version] [arXiv] [Code]
• Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning,
  (ICML 2021) A. Immer, M. Bauer, V. Fortuin, G. Rätsch, M. E. Khan [Published version] [arXiv] [Code]
• Tractable structured natural gradient descent using local parameterizations,
  (ICML 2021) W. Lin, F. Nielsen, M. E. Khan, M. Schmidt [Published version] [arXiv]
• Non-exponentially weighted aggregation: regret bounds for unbounded loss functions,
  (ICML 2021) P. Alquier [Published version] [arXiv]
• Improving predictions of Bayesian neural networks via local linearization,
  (AIstats 2021) A. Immer, M. Korzepa, M. Bauer [Published version] [arXiv] [Code]
• A Theoretical Analysis of Catastrophic Forgetting through the NTK Overlap Matrix,
  (AISTATS 2021) T. Doan, M. Abbana Bennani, B. Mazoure, G. Rabusseau, P. Alquier [Published version] [arXiv]
• Simultaneous dimension reduction and clustering via the NMF-EM algorithm,
  (Advances in Data Analysis and Classification) L. Carel, P. Alquier [Published version] [arXiv]

2020

• Continual Deep Learning by Functional Regularisation of Memorable Past
  (NeurIPS 2020) P. Pan^*, S. Swaroop^*, A. Immer, R. Eschenhagen, R. E. Turner, M.E. Khan [Published version] [ArXiv] [Code] [Poster] Oral presentation, 1% of all submissions (105 out of 9454 submissions).
  
• Approximate Bayesian Inference,
  (Entropy) P. Alquier [Paper]
• Concentration of tempered posteriors and of their variational approximations,
  (Annals of Statistics) P. Alquier, J. Ridgway [Published version] [arXiv]
• Fast Variational Learning in State-Space Gaussian Process Models,
  (MLSP 2020) P. E. Chang, W. J. Wilkinson, M.E. Khan, A. Solin [Published version] [arXiv]
• High-dimensional VAR with low-rank transition,
  (Statistics and Computing) P. Alquier, K. Bertin, P. Doukhan, R. Garnier [Published version] [arXiv]
• AI for Social Good: Unlocking the Opportunity for Positive Impact,
  (Nature Communications 2020) with Nenad Tomašev and many others
  [Paper] [Declaration on AI2SG] [Dagstuhl AI4SG 2019] [Press Release]
• Training Binary Neural Networks using the Bayesian Learning Rule,
  (ICML 2020) X. Meng, R. Bachmann, M.E. Khan [Published version] [arXiv] [Code]
• Handling the Positive-Definite Constraint in the Bayesian Learning Rule,
  (ICML 2020) W. Lin, M. Schmidt, M.E. Khan [Published version] [arXiv] [Code]
• VILD: Variational Imitation Learning with Diverse-quality Demonstrations,
  (ICML 2020) V. Tangkaratt, B. Han, M.E. Khan, M. Sugiyama [Published version] [arXiv] [Code]
• MMD-Bayes: Bayesian Estimation via Maximum Mean Discrepancy,
  (AABI 2019) B.E. Chérief-Abdellatif, P. Alquier [Published version] [arXiv]
• Exact Recovery of Low-rank Tensor Decomposition under Reshuffling,
  (AAAI 2020) C. Li, M.E. Khan, Z. Sun, G. Niu, B. Han, S. Xie, Q. Zhao [arXiv]

2019

• Practical Deep Learning with Bayesian Principles,
  (NeurIPS 2019) K. Osawa, S. Swaroop, A. Jain, R. Eschenhagen, R.E. Turner, R. Yokota, M.E. Khan. [Published version] [arXiv] [Code]
• Approximate Inference Turns Deep Networks into Gaussian Processes,
  (NeurIPS 2019) M.E. Khan, A. Immer, E. Abedi, M. korzepa. [Published version] [arXiv] [Code]
• A Generalization Bound for Online Variational Inference (best paper award),
  (ACML 2019) B.-E. Chérief-Abdellatif, P. Alquier, M.E. Khan. [Published version] [arXiv]
• Matrix factorization for multivariate time series analysis,
  (EJS) P. Alquier, N. Marie [Published version] [arXiv]
• Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-family Approximations,
  (ICML, 2019) W. Lin, M.E. Khan, M. Schmidt. [arXiv] [Published version] [Code]
  Also appeared at the Symposium on Advances in Approximate Bayesian Inference at NeurIPS 2018 [Short Paper]
  
• Stein's Lemma for the Reparameterization Trick with Exponential Family Mixtures,
  (ICML workshop on Stein's Method in ML and Stats, 2019) W. Lin, M.E. Khan, M. Schmidt. [arXiv]
• Scalable Training of Inference Networks for Gaussian-Process Models,
  (ICML, 2019) J. Shi, M.E. Khan, J. Zhu. [arXiv] [ Published version] [Code]
• TD-Regularized Actor-Critic Methods,
  (Machine Learning, 2019. A short version appeared at EWRL 2018)
  S. Parisi, V. Tangkaratt, J. Peters, M.E. Khan. [ Published version] [arXiv] [Short version at EWRL 2018]

2018

• SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient,
  (NeurIPS 2018) A. Miskin, F. Kunstner, D. Nielsen, M. Schmidt, M.E. Khan. [ Published version] [arXiv] [Poster] [3-min Video] [Code]
• Natural Variational Continual Learning,
  (Continual Learning Workshop at NIPS 2018)
  H. Tseran, M.E. Khan, T. Harada, T. Bui [Paper].
• Fast yet Simple Natural-Gradient Descent for Variational Inference in Complex Models,
  (ISITA 2018) M.E. Khan and D. Nielsen, [arXiv] [IEEE explore] [Slides]
• Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam,
  (ICML 2018) M.E. Khan, D. Nielsen, V. Tangkaratt, W. Lin, Y. Gal, and A. Srivastava, [ Published version] [arXiv] [Code] [Slides]
• Variational Message Passing with Structured Inference Networks,
  (ICLR 2018) W. Lin, N. Hubacher, and M.E. Khan, [Paper] [arXiv Version] [Code]
• Bayesian Nonparametric Poisson-Process Allocation for Time-Sequence Modeling,
  (AI-Stats 2018) H. Ding, M.E. Khan, I. sato, M. Sugiyama, [Published version] [Code]

2017

• Vprop: Variational Inference using RMSprop,
  (NIPS 2017, Workshop on Bayesian Deep Learning)
  M.E. Khan, Z. Liu, V. Tangkaratt, and Y. Gal [Workshop version] [Poster]
• Variational Adaptive-Newton Method for Explorative-Learning,
  (NIPS 2017, Workshop on Advances in Approximate Bayesian Inference)
  M.E. Khan, W. Lin, V. Tangkaratt, Z. Liu, and D. Nielsen [arXiv Version] [Poster]
• Natural-Gradient Stochastic Variational Inference for Non-Conjugate Structured Variational Autoencoder,
  (ICML 2017, Workshop on Deep Structure Prediction) W. Lin, M.E. Khan, N. Hubacher, and D. Nielsen [Paper]
• Conjugate-Computation Variational Inference : Converting Variational Inference in Non-Conjugate Models to Inferences in Conjugate Models,
  (AIstats 2017) M.E. Khan and W. Lin [ Published version ] [arXiv ] [Code for Logistic Reg + GPs] [Code for Correlated Topic Model]
• SmarPer: Context-Aware and Automatic Runtime-Permissions for Mobile Devices,
  (38th IEEE Symposium on Security and Privacy (S&P;), San Jose, CA, USA, May 22-24, 2017)
  K. Olejnik, I. I. Dacosta Petrocelli, J. C. Soares Machado, K. Huguenin, M.E. Khan , and J.-P. Hubaux [Published paper] [Code] [SmarPer Homepage]
• Gaussian-Process-Based Emulators for Building Performance Simulation,
  (Building Simulation 2017) P. Rastogi, M.E. Khan and M. Anderson [Paper] [Building Simulation Data] [Code]