UCL, a global leader in AI and machine learning, has joined the ELLIS network with a new 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
Bounding Causal Effects with Leaky Instruments
Speaker: David Watson
Event Date: 15 November 2024
Instrumental variables (IVs) are a popular and powerful tool for estimating causal effects in the presence of unobserved confounding. However, classical approaches rely on strong assumptions such as the exclusion criterion, which states that instrumental effects must be entirely mediated by treatments. This assumption often fails in practice. When IV methods are improperly applied to data that do not meet the exclusion criterion, estimated causal effects may be badly biased. In this work, we propose a novel solution that provides partial identification in linear systems given a set of leaky instruments, which are allowed to violate the exclusion criterion to some limited degree. We derive a convex optimization objective that provides provably sharp bounds on the average treatment effect under some common forms of information leakage, and implement inference procedures to quantify the uncertainty of resulting estimates. We demonstrate our method in a set of experiments with simulated data, where it performs favorably against the state of the art. An accompanying R package, leakyIV, is available from CRAN.
Speaker: Jeremias Knoblauch
Event Date: 22 November 2024
Speaker: Andrew Zhou Xiaohua
Event Date: 22 November 2024
