We also presented some “specious art” this year at ICML. Never thought of myself as an artist 🧑‍🎨 arxiv.org/abs/2006.11741

#UncertaintyQuantification in #imageRetrieval is crucial for downstream decisions! In our #ICCV2021 📰, we model embeddings as distributions rather than points, such that data uncertainty is propagated to retrieval. We mirror the triplet loss, and learn stochastic features.🧵1/4

I just joined ISBA! 🎉

Announcing the virtual seminar series on Gaussian Processes, Spatiotemporal Modeling, and Decision-making Systems! Time: Mondays at 16:00 UTC The inaugural keynote will be given by Tamara Broderick on March 14th (pi day). Please register on the website! gp-seminar-series.github.io

Looking forward to presenting our paper “Last Layer Marginal Likelihood for Invariance Learning” (arxiv.org/pdf/2106.07512…) next week at #AISTATS2022. Thanks to my fabulous collaborators Martin Jørgensen, Sebastian Ober @ICLR2025 and Mark van der Wilk. A quick peek below 🧵 (1/4)

Thrilled to announce the 2nd ELLIS Doctoral Symposium: 19-23 September, in person ELLIS Alicante. Apply by May 20 to join us in Alicante for #ELLISforEurope keynotes, poster sessions, social events and more! #EDS2022 #AIforGood ellis.eu/events/ellis-d…

It is with great sadness that I learn of the passing of Ole Eiler Barndorff-Nielsen. (March 18, 1935 - June 26, 2022). "He lived a long and positive life, full of love and mathematics" his obituary reads. An academic giant whom I am fortunate to have worked with. RIP Ole.

If you’re at #UAI2022 consider visiting this brilliant work from Pola! 🥇If you are not, consider reading it 🤓

“But they don’t scale”. Gaussian processes regression takes O(n^3) time for most kernels, hugely limiting their adoption. Sparse methods enabling O(n) time just aren’t that strong. With our kernel, a GP runs in O(n log n) time and beats the classic Matérn kernel. #NeurIPS2022 1/8

Delighted to advertise our paper at #NeurIPS2022 It presents a novel, cheap, but more diverse gradient-free sampler for parallelising GP active learning. It scales to large batch sizes and we apply it to Bayesian inference via Bayesian quadrature, e.g., battery analytics. 🧵1/n

1/📯I am hiring again! Postdoc or PhD position in Sample-Efficient Probabilistic Machine Learning Computer Science @ University of Helsinki with strong links to Finnish Center for AI 🦣 @[email protected] Please see blurb below, and full ad here: helsinki.fi/en/researchgro… Applications evaluated on a rolling basis. Please RT!

For batch active learning, how can the algorithm determine the batch size? Use computational uncertainty via kernel quadrature! Check out our new AISTATS paper with M A Osborne, Martin Jørgensen, Xingchen Wan, Vu Nguyen!🎉 #ProbabilisticNumerics paper: arxiv.org/abs/2306.05843

1/📯I am hiring!📯 Two-year postdoctoral position in Sample-Efficient Probabilistic Machine Learning Computer Science @ University of Helsinki with strong links to Finnish Center for AI 🦣 @[email protected] Please see blurb below, and full ad here: helsinki.fi/en/researchgro…