Artificial Intelligence and Machine Learning in Cambridge 2019

About

Description

Cambridge and the silicon fens have a large machine learning community across the university and many small and larger Cambridge companies.

This academic workshop aims to connect this community better and to learn from each other about interesting research and applications in the broad machine learning spectrum.

Registration

To register for this event please email Sarah Roberts with your full name, affiliation,  and any dietary/accessibility requirements you may have.

Organizers

Ryota Tomioka, Microsoft Research Cambridge

Katja Hofmann, Microsoft Research Cambridge

Miguel Hernandez-Lobato, University of Cambridge

Data Privacy

By registering for CamAIML, you give Microsoft permission to collect and use personal information you provide to manage your event experience. Your information will be deleted within 30 days of the event, however, you may revoke your consent at any time by contacting us at sarober@microsoft.com

Should you have any queries, please contact Sarah Roberts.

We look forward to seeing you there.

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Agenda

09:15 – 09:45 Registration & Welcome tea/coffee Auditorium Breakout Area
09:45 – 10:05 Tom Minka TrueSkill 2: An improved Bayesian skill rating system
10:05 – 10:25 Cheng Zhang Project EDDI: Efficient Dynamic Discovery of High-Value Information
10:25 – 10:45 Marc Brockschmidt Learning from Programs
10:45 – 11:05 Olya Ohrimenko Contamination Attacks and Mitigation in Multi-Party Machine Learning
11:05 – 11:30 Coffee break Auditorium Breakout Area
11:30 – 11:50 José Miguel Hernández Lobato Advances in machine learning for molecules
11:50 – 12:10 Yichuan Zhang Probability Measure Morphing: A Novel Mathematical Foundation of Statistical Inference
12:10 – 12:30 Eric Nalisnick Do Deep Generative Models Know What They Don’t Know?
12:30 – 12:50 Robert Peharz Faster Attend-Infer-Repeat with Tractable Probabilistic Models
13:00 – 14:00 Lunch Auditorium Breakout Area
14:00 – 14:20 Mihaela Van der Schaar Personalized treatments: Precision medicine beyond predictions
14:20 – 14:40 Dave Janz Successor Uncertainties: Exploration and Uncertainty in Temporal Difference Learning
14:40 – 15:00 Tameem Adel On Interpretable representations and the tradeoff between accuracy and interpretability
15:00 – 15:20 Mark Girolami Probabilistic Numerical Analysis: A New Concept?
15:30 – 16:00 Coffee break Auditorium Breakout Area
16:00 – 16:20 Kun Zhang Finding causality and making prediction in the presence of distribution shift
16:20 – 16:40 Kamil Ciosek Dynamic Programming: Old and New
16:40 – 17:00 Marc-Alexandre Côté TextWorld – A framework for training reinforcement learning agents on text-based games
17:00 – 17:20 Luisa Zintgraf Fast Context Adaption via Meta-Learning
Followed by Networking, Canapés & Drinks