Tenured research scientist position available, see the official job posting.
Position for 3 year contract.
See here for details.
Invited talk by Emtiyaz Khan at Tübingen AI Center (Philipp Hennig’s group).
Invited talk by Emtiyaz Khan at Secondmind.
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 ]
A new paper on Bayesian Learning Rule by M.E. Khan and H. Rue.
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.
Pierre Alquier joined the editorial board of JMLR.
Our paper on continual learning by functional regularization on the memorable past is accepted as an oral presentation at NeurIPS 2020.
New preprint by Pierre Alquier on online learning with unbounded loss functions.
Emtiyaz Khan gave a talk on DNN2GP.
Emtiyaz Khan received a Kakenhi Grant (Series B) on “life-long learning” (approx. USD 158K).