Tom Rainforth
@tom_rainforth
Associate Professor in Machine Learning at the University of Oxford, Head of RainML Research Lab (http://rainml.uk)
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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
Apple and Oxford just made AI 6.5x better at problem-solving. The secret: it teaches AI agents to ask perfect questions. This rockets success rates from 14% to 91%. No need for fine-tuning or retraining. It runs on current models. Here's how it works: It's a strategic loop…
I have an opening for a 2-year postdoc in probabilistic machine learning and/or experimental design. The application deadline is the 3rd of September. See here for details and how to apply: tinyurl.com/rainmlpostdoc2…
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!
Our new paper (arxiv.org/abs/2310.14888) shows that probabilistic programs with stochastic support are implicitly Bayesian model averages (BMA) which leads to issues if we assume our model is misspecified! w/ @TimReichelt3 and Luke Ong A thread (1/5)
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!
Wow. Just heard I've been awarded the Corcoran Memorial Prize for my DPhil thesis by @OxfordStats ! It's wonderful and unexpected news :) A double thank you to my supervisors @tom_rainforth @yeewhye for four years terrific guidance and also for putting me forward for the award
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