Professor of Computer Science, UCL

Joined November 2007
Check out recent work on learning to discover.
Large Discovery Models: learning where to search next An LLM proposes, a Bayesian surrogate scores uncertainty, and the loop iterates — cutting validation error by 2.4x, binding energy by 18%, and boosting molecular objectives by 60%+ across programs, proteins, and molecules.
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Large Discovery Models: learning where to search next An LLM proposes, a Bayesian surrogate scores uncertainty, and the loop iterates — cutting validation error by 2.4x, binding energy by 18%, and boosting molecular objectives by 60%+ across programs, proteins, and molecules.
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Jun Wang retweeted
Fine-tuning LLM Agents without Fine-tuning LLMs Catchy title and very cool memory technique to improve deep research agents. Great for continuous, real-time learning without gradient updates. Here are my notes:
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I guess we will have a lot to talk about #ChatGPT in our upcoming #wsdm2023 tutorial “proactive conversational agents.” I don’t have any concern/complaint/resistance for this intelligent new being. I look forward to my own evolution via interactions w/ this true AI.
Home, Sweet Home. Will be presenting a WSDM'23 Tutorial on "proactive conversational agents" w/ @lizi_liao and @chirag_shah in Singapore, Sweet Singapore.
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Congratulations to UKRI Board members Professor Anthony Finkelstein (@profserious), who has received a knighthood, and @JohnFingleton1, who has received a CBE. #NewYearHonours
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So proud! Our new hire @HarrieOos wins SIGIR best paper with a single-authored contribution, and former student @seawan has an honerable mention for the Test-of-time award for his paper on Portfolio theory for IR!! #SIGIR2021
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An exciting demonstration of curiosity driven learning in #minecraft @Project_Malmo
Replying to @hardmaru
And what about a mountain car embedded in a 3D #minecraft world, without ANY reward? Well, curiosity-driven learning also gets the cart up the hill here! (paper to come soon, and congrats to Remy Portelas) youtube.com/watch?v=CTuHll7G… @katjahofmann
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We provide a new theoretical analysis to understand how they happen, and what we can do about them: arxiv.org/abs/1902.10730
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"Artificially Intelligent Players Invent Nonverbal 'Languages' to Win Card Games": My piece for @IEEESpectrum on bridge and Hanabi research by @seawan @ucl @j_foerst @UniofOxford @DeepMindAI spectrum.ieee.org/tech-talk/…
See our recent work on AI playing the Bridge Card game and how AI agents can learn communication language secretly.
"Artificially Intelligent Players Invent Nonverbal 'Languages' to Win Card Games": My piece for @IEEESpectrum on bridge and Hanabi research by @seawan @ucl @j_foerst @UniofOxford @DeepMindAI spectrum.ieee.org/tech-talk/…
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Today @UCL launches #MadeAtUCL to showcase our ground-breaking research and discoveries changing people's lives. Know some amazing UCL research that deserves the spotlight? Submit it here: bit.ly/MadeAtUCL-s #MadeAtUni
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From finance and mobile communications to digital advertising disq.us/t/37aopn4
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Our recent work on AI playing Contract Bridge card game.
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We explain the difference through late 20th century mainstream song ... mediagamma.com/blog/supervis…
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Open ETL for Real Time Decision Making with Dr. Shuai Yuan (MediaGamma) youtu.be/hhJ21go-WUQ via @YouTube
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