# News

**January 30, 2023**Our work “SAM as an Optimal Relaxation of Bayes” (T. Moellenhoff and M. E. Khan) is accepted at ICLR 2023 (notable top 5%). We have another paper “The Lie-Group Bayesian Learning Rule” (E. M. Kiral, T. Moellenhoff, M.E. Khan) accepted at AISTATS 2023.

**January 20, 2023**Happy Buzaaba is co-organizing the ICLR2023 workshop on AfricaNLP to be held on 5th May in Kigali, Rwanda.

**December 12, 2022**We organized the “Continual Lifelong Learning Workshop” at ACML2022 in Hyderabad, India.

**December 08, 2022**Invited Prof. Michael Choi (National University of Singapore and Yale-NUS College) for a seminar talk. Title “From reversiblizations of non-reversible Markov chains to landscape modification”.

**December 03, 2022**Emtiyaz Khan gave an invited talk at NeurIPS 2022 Workshop on Broadening Research Collaborations in Machine Learning [Slides].

**November 24, 2022**Emtiyaz Khan gave an invited talk at University of Bath in Prof. Olga Isupova’s group [Slides].

**November 09, 2022**Today, we hosted Prof. Yarin Gal for a seminar on “Uncertainty in Deep Learning: Lessons Learned from Medical Imaging”.

**October 25, 2022**Forthcoming talks by Geoffrey Wolfer:

- [16 Nov, 2022] [ CCSP Seminar ] Talk: "Geometric Aspects of Markov Models"
- [22 Nov, 2022] [ IBIS'22 ] Poster: "Variance-Aware Estimation of Kernel Mean Embedding"
- [1 Dec, 2022] [ SITA'22 ] Talk: "Markovian Embeddings of Markov Chains"

**October 24, 2022**Pierre will visit Toulouse School of Economics as an invited researcher in November. He will give a series of 5 lectures at on PAC-Bayes and Mutual Information bounds, on Nov. 8, 15, 21, 22 and 28. The lectures are intended for PhD students but will be also opened for master students. They will be essentially based on User-friendly introduction to PAC-Bayes bounds. He will also give the following talks:

- [10 Nov, 2022] [ MAD-Stat seminar, Toulouse School of Econonomics ] Talk: "The nice properties of MMD for statistical estimation"
- [14 Nov, 2022] [ Séminaire Parisien de Statistique à l'IHP ] Talk: "Concentration and robustness of discrepancy-based ABC"
- [17 Nov, 2022] [ Probability and Statistics Seminar, University of Luxembourg ] Talk: "Minimum MMD estimation"

**September 02, 2022**Forthcoming talks by Geoffrey Wolfer:

- [19 Sep, 2022] [ IG4DS ] Talk: "Information Geometry of Reversible Markov Chains" [ Slides ] [ Video ]
- [20 Sep, 2022] [ IG4DS ] Talk: "Geometric Aspects of Data-Processing of Markov Chains" [ Slides ] [ Video ]
- [26 Sep, 2022] [ BIID'10 ] Talk: "Geometric Aspects of Data-Processing of Markov Chains"

**August 24, 2022**Forthcoming talk by Pierre:

- [08 Sep, 2022] [ O'Bayes 2022, Santa Cruz, California ] Talk: "Concentration and robustness of discrepancy-based ABC"

**May 26, 2022**[ ENTROPY ] special issue on Approximate Bayesian Inference (edited by Pierre) is now available as a book and as a pdf file that can be downloaded for free on the editor website: [ Book page ]

**May 01, 2022**Recent talks by Pierre:

- [20 Jan, 2022] StatML CDT seminar (Imperial & Oxford): "Matrix factorization for time series analysis" [ Slides ]
- [02 Feb, 2022] Invited talk at [ AABI2022 ] "What to expect from PAC-Bayes bounds" [ Slides ] [ Video ]
- [03 Mar, 2022] RIKEN AIP High-dimensional modelling team seminar: "Deviation inequalities for Markov chains" [ Slides ] [ Video ]
- [19 Apr, 2022] [ DELTA ] Seminar: "PAC-Bayes bounds and contraction of the posterior"
- [21 Apr, 2022] [ International Bayes Club ] Seminar: "PAC-Bayes bounds and contraction of the posterior"
- [28 Apr, 2022] [ One World ABC ] Seminar: "Concentration and robustness of discrepancy-based ABC" [ Slides ] [ Video ]

Forthcoming talks:

- [31 May, 2022] [ Statistical estimation and deep learning in UQ for PDEs ] Talk: "Robust estimation via minimum distance estimation"
- [14 Jun, 2022] [ New Trends in Statistical Learning II ] Talk: "A theoretical analysis of catastrophic forgetting"
- [23 Jun, 2022] [ EcoDep 2022 Conference on Networks Reconstruction ] Talk: "A theoretical analysis of catastrophic forgetting"

**March 30, 2022**Emtiyaz Khan gave the following talks in March and Feb, 2022

- [24 Mar, 2022] Talk at MLT_init_ [ Slides ]
- [16 Mar, 2022] Cambridge CBL Reading Group [ Slides ]
- [11 Mar, 2022] DeepMind/ELLIS CSML Seminar Series at UCL [ Slides ] [ Video ]
- [ 1 Mar, 2022] ATR-AIP Joint Seminar on Neuroscience-Inspired AI [ Slides ]
- [22 Feb, 2022] AI4Sec Seminar Series at Huwaei Research Munich [ Slides ] [ Summary ]
- [17 Feb, 2022] StatML CDT seminar at Imperial College London and University of Oxford [ Slides ] [ Summary ]

**December 08, 2021**Emtiyaz Khan, Dharmesh Tailor and Siddharth Swaroop will be giving an invited talk on Adaptive and Robust Learning with Bayes at the NeurIPS 2021 Bayesian deep learning workshop on Dec. 14th 11:10 - 11:30 GMT.

**December 06, 2021**We won both tracks of the NeurIPS competition on Approximate Inference in Bayesian Deep Learning! Please join our talks at the NeurIPS 2021 competition track (Dec. 9th, 6pm GMT) and at the BDL workshop (Dec. 14th, 4:40pm GMT) to learn about our solution.

**October 01, 2021**Our Bayes-duality project is launched with a funding of $2.76 million by JST-ANR’s CREST proposal

**September 29, 2021**Two papers accepted at NeurIPS 2021:

- Knowledge-Adaptation Priors, by M.E. Khan and S. Swaroop
- Dual parameterization of SVGP, by P. Chang, V. ADAM, M.E. Khan, A. Solin

**September 28, 2021**Paper by Dimitri Meunier & Pierre Alquier accepted+published in Entropy: [Meta-strategy for Learning Tuning Parameters with Guarantees]

**September 16, 2021**Invited talk by Emtiyaz Khan at Durham CDT Data Science Workshop.

**September 13, 2021**Invited talk by Pierre Alquier at Ecology and Dependance 2021 (EcoDep) on [ Risk Bound for High Dimensional Time Series Completion ]

**July 23, 2021**Invited talk by Emtiyaz Khan at Theory and Foundation of Continual Learning [ Slides ] [ SlidesLive Video ]

**May 13, 2021**One paper accepted at UAI2021: Subset-of-Data Variational Inference for Deep Gaussian-Process Regression by A. Jain, P.K. Srijith, M.E. Khan.

**May 10, 2021**Three papers were accepted at ICML2021:

- Scalable marginal likelihood estimation for model selection in deep learning by A. Immer, M. Bauer, V. Fortuin, G. Rätsch, M. E. Khan.
- Tractable structured natural gradient descent using local parameterizations by W. Lin, F. Nielsen, M. E. Khan, M. Schmidt.
- Non-exponentially weighted aggregation: regret bounds for unbounded loss functions by P. Alquier.

**March 10, 2021**16th AIP Open Seminar: talks by Approximate Bayesian Inference Team. Talks by

- Emtiyaz Khan: Bayesian principles for Learning-Machines,
- Dharmesh Tailor: Memorable Experiences of Learning-Machines,
- Pierre Alquier: Meta-Strategy for Hyperparameter Tuning with Guarantees.

**January 28, 2021**Two papers were accepted at AISTATS2021:

- A Theoretical Analysis of Catastrophic Forgetting through the NTK Overlap Matrix by T. Doan, M. Abbana Bennani, B. Mazoure, G. Rabusseau, P. Alquier.
- Improving predictions of Bayesian neural networks via local linearization by A. Immer, M. Korzepa, M. Bauer.

**November 11, 2020**“Approximate Bayesian Inference”, the editorial of a forthcoming special issue in Entropy, written by P. Alquier, is now published: paper. You can submit a paper until the end of Feb. 2021. Special Issue on Approximate Bayesian Inference

**November 05, 2020**The videos of two recent talks by Pierre Alquier are now online:

- “Regret bound for online variational inference” (Oct. 29) - Workshop on online decision making, Berkeley
- “Estimation with the MMD distance” (Nov. 4) - DataSig seminar series

**November 02, 2020**A series of seminars on *“Bayesian principles for learning machines”* held by Emtiyaz Khan will take place at the following dates and locations.

- 3 Nov. 2020 - Waterloo AI institute
- 5 Nov. 2020 - TU Darmstadt
- 17 Nov. 2020 - Uppsala University

**September 26, 2020**Our paper on continual learning by functional regularization on the memorable past is accepted as an oral presentation at NeurIPS 2020.

**August 17, 2020**We have two tutorials at SMILES 2020! See Emtiyaz Khan’s tutorial on DL with Bayes (slides, video) and Pierre Alquier on Sequential Prediction Problems (video).

**April 01, 2020**Emtiyaz Khan received a Kakenhi Grant (Series B) on “life-long learning” (approx. USD 158K).