Seminars

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Abstract: AI systems are rapidly replacing traditional information seeking media such as web search, yet their epistemic diversity - defined as the diversity of real-world claims in t

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This paper is available at https://arxiv.org/abs/2606.26760

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Federated learning enables devices to collaboratively train models while keeping their training data local.

Speaker: Woody Lam

Date: 12 October from 1:05 until 1:55 pm (lunch and networking after with representatives of GSA)

Location: room FW26 at the Department of Computer Science and Technology at the University of Cambridge

Please sign up via our events listings at www.gsacapital.com/careers

The unitary synthesis problem, first introduced by Aaronson and Kuperberg in 2006, is a question about the relationship between doing quantum tasks (i.e., implementing unitaries) and doing classical tasks (i.e., implementing functions).

Local SGD, also known as Federated Averaging, is a simple approach to communication-efficient distributed optimization in which each client performs several stochastic-gradient steps between communication rounds.

Biomedical data offer unprecedented opportunities for scientific discovery and precision medicine, but their sensitive, high-dimensional, and interconnected nature creates privacy risks.

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