Tom Rainforth

We've got really good at utilizing data. But methods for acquiring that data are often still rudimentary. Our new review paper shows how Bayesian experimental design has recently transformed to now provide a powerful mechanism to acquire data intelligently arxiv.org/abs/2302.14545

I have an opening for a 2.5-year postdoc position in the RainML lab as part of my ERC grant on probabilistic machine learning and intelligent data acquisition. Application deadline 10th July 2024. See here for details and to apply: tinyurl.com/rainmlpostdoc

I'm delighted to announce that from September I will officially be an Associate Professor (remaining at the Oxford stats department)

In-context learning can learn novel input-output relationships beyond what can be picked up from input context alone, but doesn't behave like conventional learning algorithm. Find out more at our ICLR poster #129 this afternoon. Paper: openreview.net/forum?id=YPIA7…, led by @janundnik

Are you at ICLR? Have you heard that In-Context Learning in LLMs does not learn label relationships? Well that's not true. Visit our poster TODAY to find out how LLMs incorporate label information. Spoiler: it's not Bayesian inference. Poster #129, May 7, 4.30 pm

I will be presenting our work on "Beyond Bayesian Model Averaging over Paths in Probabilistic Programs with Stochastic Support" at AISTATS in Valencia tomorrow (details in thread below). If you are interested in probabilistic programming, come and say hi at poster session 1!

The current default recipe for Bayesian active learning doesn’t really work beyond MNIST scale. We suggest why that is and identify a simple fix. arxiv.org/abs/2404.17249 @aistats_conf with @adamefoster @tom_rainforth 1/5

It is @NeurIPS time again! I am excited to present our trans-dimensional jump diffusion work with @AndrewC_ML @willarvey @ValentinDeBort1 @tom_rainforth and @ArnaudDoucet1 ! Come over on Thursday 2nd poster session, neurips.cc/virtual/2023/p…. arxiv.org/abs/2305.16261 #NeurIPS2023

Interested in large language models? Worried about impacts of climate change? Come join us @oxcsml @NatureRecovery @UniofOxford in pushing the frontiers in #LLMs and at the same time help #NatureRecovery and address the impacts of #ClimateChange! bit.ly/4750IBO

We construct neural processes by iteratively transforming a simple stochastic process into an expressive one, similar to flow/diffusion-based models, but in function space! Join us at our #NeurIPS2023 poster session: neurips.cc/virtual/2023/p… on Wednesday morning!

Incredibly well deserved. Congratulations Adam!

Me and @TimReichelt3 will shortly present on Efficient Inference for Probabilistic Programs with Stochastic Support for the ProbProg seminar series at 11am EST. Join here: app.gather.town/events/QNDU4JL… Papers: proceedings.mlr.press/v119/zhou20e.h… proceedings.neurips.cc/paper_files/pa… arxiv.org/pdf/2310.14888…