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UCL ELLIS
UCL, a global leader in AI and machine learning, is a core component of the ELLIS network through its ELLIS Unit. ELLIS is a European AI network of excellence comprising Units within 30 research institutions. It focuses on fundamental science, technical innovation and societal impact. The ELLIS Unit at UCL spans across multiple departments (Gatsby Computational Neuroscience Unit, Department of Computer Science, Department of Statistical Science and Department of Electronic and Electrical Engineering).

“Some of the most effective learning algorithms are those that combine perspectives from many different models or parameters. This has always seemed a fitting metaphor for effective research. And now ELLIS will provide a new architecture to keep our real-life committee machine functioning --- reinforcing, deepening and enlarging the channels that connect us to colleagues throughout Europe At UCL we're excited to be a part of this movement to grow together. We look forward to sharing new collaborations, workshops, exchanges, joint studentships and more, and to the insight and breakthroughs that will undoubtedly follow. ”

Prof Maneesh Sahani
Director, Gatsby Computational Neuroscience Unit

“Advances in AI that benefit people and planet require global cooperation across disciplines and sectors. The ELLIS network is a vital part of that effort and UCL is proud to be a contributor. ”

Prof Geraint Rees
UCL Pro-Vice-Provost (AI)

News


Events


A conversion theorem and minimax optimality for continuum contextual bandits

Speaker: Arya Akhavan
Event Date: 25 April 2025

I will talk about the contextual continuum bandits problem, where the learner sequentially receives a side-information vector and must choose an action from a convex set, minimizing a function associated with the context. The goal is to minimize all the underlying functions for the received contexts, leading to the contextual notion of regret, which is stronger than the standard static regret. Assuming that the objective functions are γ-Hölder with respect to the contexts, for some 0<γ≤1, I will describe a reduction that shows how any algorithm achieving sub-linear static regret can be extended to achieve sub-linear contextual regret. I will present a static-to-contextual regret conversion theorem, which provides an upper bound on the contextual regret of the output algorithm as a function of the static regret of the input algorithm. Then, I will showcase the implications of this general result for three fundamental cases of how the objective function depends on the action variable: (a) Lipschitz bandits, (b) convex bandits, (c) strongly convex and smooth bandits. Lastly, I will present a minimax lower bound that implies two key facts. First, obtaining sub-linear contextual regret may be impossible for functions that are not continuous with respect to the context. Second, for convex bandits and strongly convex and smooth bandits, the algorithms we propose achieve — up to a logarithmic factor — the minimax optimal rate of contextual regret as a function of the number of queries.

People


Computer Science

Gatsby Computational Neuroscience Unit

Department of Statistical Science

Department of Electronic and Electrical Engineering

UCL Energy Institute

Department of Experimental Psychology