You can apply to any team in RIKEN AIP. See here for details.
See here for details.
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).