<div dir="ltr"><div dir="ltr"><div><span style="font-family:monospace,monospace">tl;dr: <br></span></div><div><span style="font-family:monospace,monospace"></span></div><div><span style="font-family:monospace,monospace">* Programme is out</span></div><div><span style="font-family:monospace,monospace">* Early registration deadline is Monday 10 Dec (AoE).<br></span></div><div dir="ltr"><span style="font-family:monospace,monospace"><br></span></div><div dir="ltr"><span style="font-family:monospace,monospace">LAFI 2019: Languages for Inference (formerly PPS)<br>================================================<br>Tuesday, 15 January 2019, Cascais/Lisbon, Portugal<br>A workshop affiliated with POPL 2019<br><a href="https://popl19.sigplan.org/track/lafi-2019">https://popl19.sigplan.org/track/lafi-2019</a><br><br>Important dates (anywhere on earth)<br>-------------------------------------------------<br> Early Registration Deadline    Mon 10 Dec 2018<br> Workshop                       Tue 15 Jan 2019<br>                                (day before POPL)<br>-------------------------------------------------<br><br><br>Registration: <a href="https://popl19.sigplan.org/attending/Registration">https://popl19.sigplan.org/attending/Registration</a><br><br><br>Invited Speaker: Matthijs Vákár (Columbia University)<br>Invited talk:<br>  Connecting probabilistic programming theory<br>           to applications in Stan</span></div><div dir="ltr"><br><span style="font-family:monospace,monospace"></span></div><div dir="ltr"><span style="font-family:monospace,monospace">Full programme: <a href="https://popl19.sigplan.org/track/lafi-2019#program">https://popl19.sigplan.org/track/lafi-2019#program</a><br></span></div><div dir="ltr"><span style="font-family:monospace,monospace"><br></span></div><div dir="ltr"><span style="font-family:monospace,monospace">Context<br>=======<br><br>Inference concerns re-calibrating program parameters based on<br>observed data, and has gained wide traction in machine learning and<br>data science. Inference can be driven by probabilistic analysis and<br>simulation, and through back-propagation and<br>differentiation. Languages for inference offer built-in support for<br>expressing probabilistic models and inference methods as programs, to<br>ease reasoning, use, and reuse. The recent rise of practical<br>implementations as well as research activity in inference-based<br>programming has renewed the need for semantics to help us share<br>insights and innovations.<br><br>This workshop aims to bring programming-language and machine-learning<br>researchers together to advance all aspects of languages for<br>inference. Topics include but are not limited to:<br><br>+ design of programming languages for inference and/or differentiable<br>  programming;<br>+ inference algorithms for probabilistic programming languages,<br>  including ones that incorporate automatic differentiation;<br>+ automatic differentiation algorithms for differentiable programming<br>  languages;<br>+ probabilistic generative modelling and inference;<br>+ variational and differential modelling and inference;<br>+ semantics (axiomatic, operational, denotational, games, etc) and<br>  types for inference and/or differentiable programming;<br>+ efficient and correct implementation;<br>+ and last but not least, applications of inference and/or<br>  differentiable programming.<br><br>This year we are explicitly expanding the focus of the workshop from<br>statistical probabilistic programming to encompass differentiable<br>programming for statistical machine learning.<br><br>We expect this workshop to be informal, and our goal is to foster<br>collaboration and establish common ground. Thus, the proceedings will<br>not be a formal or archival publication.<br>Nevertheless, as a concrete basis for fruitful discussions, we call<br>for extended abstracts describing specific and ideally ongoing work on<br>probabilistic programming languages, semantics, and systems.<br><br>In line with the SIGPLAN Republication Policy:<br><br><a href="http://www.sigplan.org/Resources/Policies/Republication/">http://www.sigplan.org/Resources/Policies/Republication/</a><br><br>inclusion of extended abstracts in the programme is not intended to<br>preclude later formal publication.<br><br>Programme committee:<br>Atılım Güneş Baydin        University of Oxford Department of Engineering<br>Bart van Merriënboer       University of Montreal<br>Christine Tasson           University Paris Diderot<br>David Duvenaud             University of Toronto<br>Jeffrey Siskind (co-chair) School of Electrical and Computer<br>                           Engineering, Purdue University<br>Matthew Johnson            Google Brain<br>Ohad Kammar     (co-chair) University of Oxford Department of<br>                           Computer Science<br>Praveen Narayanan          Indiana University<br>Ryan Culpepper             Czech Technical University<br>Sophia Gold                Tezos<br>Steven Holtzen             University of California Los Angeles<br>Tom Rainforth              University of Oxford Department of Statistics<br></span><br></div></div></div>