POPL 2025 (series) / LAFI 2025 (series) / LAFI 2025 /
Data-oriented Design for Differentiable, Probabilistic Programming (Remote)
This submission describes an reimplementation of the core data structure of LazyPPL, the lazy rose tree, using techniques from data oriented design with the aim of reducing memory usage and increasing performance. On top of this, we implement an reverse-mode autodiff graph specialized to computing the gradients of the values of the lazy rose tree with respect to the score of the probabilistic program.
Sun 19 JanDisplayed time zone: Mountain Time (US & Canada) change
Sun 19 Jan
Displayed time zone: Mountain Time (US & Canada) change
11:00 - 12:30 | |||
11:00 40mTalk | Invited talk: TORAX - A Fast and Differentiable Tokamak Transport Simulator in JAX (Remote) LAFI Jonathan Citrin Google Deepmind | ||
11:41 15mTalk | Data-Parallel Differentiation by Optic Composition LAFI | ||
11:57 15mTalk | Data-oriented Design for Differentiable, Probabilistic Programming (Remote) LAFI Owen Lynch University of Oxford, Maria-Nicoleta Craciun University of Oxford, Younesse Kaddar University of Oxford, Sam Staton University of Oxford | ||
12:13 15mTalk | A Domain-Specific PPL for Reasoning about Reasoning (or: a memo on memo) LAFI Kartik Chandra MIT, Tony Chen MIT, Joshua B. Tenenbaum Massachusetts Institute of Technology, Jonathan Ragan-Kelley Massachusetts Institute of Technology |