News
Tenured research scientist position available, see the official job posting.
Position for 3 year contract.
Invited talk by Emtiyaz Khan at Durham CDT Data Science Workshop.
Invited talk by Pierre Alquier at Ecology and Dependance 2021 (EcoDep) on [ Risk Bound for High Dimensional Time Series Completion ]
Invited talk by Emtiyaz Khan at Theory and Foundation of Continual Learning [ Slides ] [ SlidesLive Video ]
One paper accepted at UAI2021: Subset-of-Data Variational Inference for Deep Gaussian-Process Regression by A. Jain, P.K. Srijith, M.E. Khan.
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.
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.
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.
“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
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
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
Our paper on continual learning by functional regularization on the memorable past is accepted as an oral presentation at NeurIPS 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).
Emtiyaz Khan received a Kakenhi Grant (Series B) on “life-long learning” (approx. USD 158K).