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Fri, 26 Jan 2018 16:03:10 -0800 (PST) MIME-Version: 1.0 Received: by 10.25.195.6 with HTTP; Fri, 26 Jan 2018 16:03:10 -0800 (PST) From: Andrzej Murawski Date: Sat, 27 Jan 2018 00:03:10 +0000 X-Gmail-Original-Message-ID: Message-ID: To: concurrency@listserver.tue.nl, finite-model-theory@lists.rwth-aachen.de, appsem@lists.tcs.ifi.lmu.de, lfcs-interest@inf.ed.ac.uk, agda@lists.chalmers.se, Association for Symbolic Logic , cade@itu.dk, coq-club@pauillac.inria.fr, agents@cs.umbc.edu, caml-list@inria.fr Content-Type: text/plain; charset="UTF-8" Content-Transfer-Encoding: quoted-printable X-Oxford-Username: lina0645 X-Oxmail-Spam-Status: score=-0.0 tests=T_RP_MATCHES_RCVD -0.0 T_RP_MATCHES_RCVD Envelope sender domain matches handover relay domain X-Oxmail-Spam-Level: / X-Validation-by: andrzej.murawski@cs.ox.ac.uk Subject: [Caml-list] FOPSS Logic and Learning School advertisement FOPSS Logic and Learning School advertisement The Logic & Learning School is an opportunity to learn from, and interact with, the world's experts leading recent progress in understanding the relationships between logic and learning. These experts come from both academia and some of the leading industrial research labs (Amazon Research and DeepMind). In the last few decades, logic has emerged as a fundamental paradigm for understanding complex systems. It has turned out to be instrumental in formal methods such as program verification, reasoning about hardware, reasoning about real-time systems and, more recently, probabilistic systems. Machine learning has recently had spectacular successes in fields such as image recognition, game playing, and many areas that involve the extraction of information from large datasets. The use of statistical approaches yields practical solutions to problems that seemed out of reach just a few years ago. The understanding of why these approaches are so successful has lagged behind the empirical successes. Using logic as the foundation to understand machine learning to obtain the best of both worlds is a major challenge. The programme of the Logic & Learning School consists of ten lectures of three hours each, starting with four introductory courses on computational and statistical learning theory, reinforcement learning and Bayesian inference, and six advanced courses on exciting and recent developments relating logic and learning. The lectures target an audience of logicians and computer scientists broadly construed and do not assume any knowledge on machine learning. Accordingly, the School represents a perfect opportunity to learn for both students and working researchers. The School will take place in St Anne's College in the centre of Oxford, an ideal learning environment with accommodation and lunches provided on site. The lectures will be from Monday 2 July in the morning to Friday 6 July in the afternoon, which is the week before the main activities of FLoC. Complete list of speakers Borja Balle (Amazon Research Cambridge) Spectral algorithms for automata learning Richard Evans (DeepMind) Inductive logic programming and deep learning Nina Gierasimczuk (Technical University of Danemark) Learning and epistemic modal logic Varun Kanade (University of Oxford) Statistical learning theory Guy Katz (Stanford University and Hebrew University of Jerusalem) Verification of machine learning programs Jan K=C5=99et=C3=ADnsk=C3=BD (Technical University of Munich) Learning for = verification Stephen H. Muggleton (Imperial College London) Inductive logic programming Doina Precup (McGill University and DeepMind) Reinforcement learning Dan Roy (University of Toronto) Bayesian learning James Worrell (University of Oxford) Computational learning theory Registration The summer school is a residential course held at St Anne's College, Oxford. The registration fee includes bed & breakfast accommodation for 6 days (1-6th July 2018), buffet lunches and evening meals. There will be a banquet on 4th July at St John's College. Arrivals are on 30th June 2018 and departures on 6th July. As the number of rooms available at St Anne's is very limited, early registration is strongly advised to avoid disappointment. Registration fees are: Early bird =C2=A3750 15 April, 2018 Late =C2=A3850 15 May, 2018 The summer school is perfectly aligned for students who want to attend the four-yearly Federated Logic Conference (FLOC) taking place in Oxford after the summer school. FLOC will feature a number of AI-related events, including a public lecture by Stuart Russell at the Sheldonian Theatre (http://www.floc2018.org/speaker/stuart-russell/), a Debate in the Oxford Union Chamber on Ethics for Robots (http://www.floc2018.org/speaker/debate/), and the Summit on Machine Learning Meets Formal Methods (http://www.floc2018.org/summit-on-machine-learning/). Students and postdocs may also be interested in the FLOC Volunteer Programm= e: http://www.floc2018.org/volunteer/ For registration and further information about the Logic & Learning School (opens early February) see: http://www.floc2018.org/fopss/ Information about FLOC 2018 can be found at: http://www.floc2018.org/