This program is tentative and subject to change.
We study the problem of synthesizing domain-specific languages (DSLs) for few-shot learning in symbolic domains. Given a base language and instances of few-shot learning problems— where each instance is split into training and testing samples— the DSL synthesis problem we introduce asks for a grammar over the base language that guarantees that small expressions solving training samples also solve corresponding testing samples. We prove that the problem is decidable for a class of languages whose semantics over fixed structures can be evaluated by tree automata and when expression size corresponds to parse tree depth in the grammar, and, furthermore, the grammars solving the problem correspond to a regular set of trees. We also prove decidability results for variants of the problem where DSLs are only required to express solutions for input learning problems and where DSLs are defined using macro grammars.
This program is tentative and subject to change.
Sat 18 OctDisplayed time zone: Perth change
13:45 - 15:30 | |||
13:45 15mTalk | Active Learning for Neurosymbolic Program Synthesis OOPSLA Celeste Barnaby University of Texas at Austin, Jocelyn Qiaochu Chen New York University, University of Alberta, Ramya Ramalingam University of Pennsylvania, Osbert Bastani University of Pennsylvania, Işıl Dillig University of Texas at Austin | ||
14:00 15mTalk | Language-Parametric Reference Synthesis OOPSLA Daniel A. A. Pelsmaeker Delft University of Technology, Netherlands, Aron Zwaan Delft University of Technology, Casper Bach University of Southern Denmark, Arjan J. Mooij Zürich University of Applied Sciences | ||
14:15 15mTalk | Multi-Modal Sketch-based Behavior Tree Synthesis OOPSLA Wenmeng Zhang College of Computer Science and Technology, National University of Defense Technology, Changsha, China, Zhenbang Chen College of Computer, National University of Defense Technology, Weijiang Hong National University of Defense Technology, Changsha, China | ||
14:30 15mTalk | Synthesizing DSLs for Few-Shot Learning OOPSLA Paul Krogmeier University of Illinois at Urbana-Champaign, P. Madhusudan University of Illinois at Urbana-Champaign | ||
14:45 15mTalk | Synthesizing Implication Lemmas for Interactive Theorem Proving OOPSLA Ana Brendel University of Texas at Austin, Aishwarya Sivaraman Meta, Todd Millstein University of California at Los Angeles | ||
15:00 15mTalk | Synthesizing Sound and Precise Abstract Transformers for Nonlinear Hyperbolic PDE Solvers OOPSLA Jacob Laurel Georgia Institute of Technology, Ignacio Laguna Lawrence Livermore National Laboratory, Jan Hueckelheim Argonne National Laboratory | ||
15:15 15mTalk | Tunneling Through the Hill: Multi-Way Intersection for Version-Space Algebras in Program Synthesis OOPSLA Guanlin Chen Peking University, Ruyi Ji Peking University, Shuhao Zhang Peking University, Yingfei Xiong Peking University | ||