POPL 2024
Sun 14 - Sat 20 January 2024 London, United Kingdom
Sun 14 Jan 2024 15:20 - 15:30 at Kelvin Lecture - Third Session Chair(s): Steven Holtzen, Matthijs Vákár

Despite the fact that Automatic Differentiation (AD) underlies much of machine learning, scientific computing and graphics, static analyses of AD code has been limited. In this position paper, we argue why AD code needs precise, general and scalable abstract interpretation and why this abstract interpretation should leverage the specific structure inherent to AD. Lastly, we demonstrate a new use of abstract interpretation of AD to analyze Coordinate MLPs from Graphics, attaining orders of magnitude more precision than prior work.

Sun 14 Jan

Displayed time zone: London change

14:00 - 15:30
Third SessionLAFI at Kelvin Lecture
Chair(s): Steven Holtzen Northeastern University, Matthijs Vákár Utrecht University
14:00
10m
Talk
Effect Handlers for Choice-Based Learning
LAFI
Gordon Plotkin Google, Ningning Xie University of Toronto
File Attached
14:10
10m
Talk
Guaranteed Bounds for Discrete Probabilistic Programs with Loops via Generating Functions
LAFI
Fabian Zaiser University of Oxford, Andrzej Murawski University of Oxford, C.-H. Luke Ong NTU
File Attached
14:20
10m
Talk
JuliaBUGS: A Graph-Based Probabilistic Programming Language using BUGS syntax
LAFI
Xianda Sun University of Cambridge, Philipp Gabler Independent researcher, Andrew Thomas University of Cambridge, Hong Ge University of Cambridge
14:30
10m
Talk
Mixture Languages
LAFI
Oliver Richardson Cornell University, Jialu Bao Cornell University
File Attached
14:40
10m
Talk
Structured Tensor Algebra for Efficient Discrete Probabilistic Inference
LAFI
Amir Shaikhha University of Edinburgh
14:50
10m
Talk
Towards a Categorical Model of the Lilac Separation Logic
LAFI
John Li Northeastern University, Jon Aytac Sandia National Laboratories, Philip Johnson-Freyd Sandia National Laboratories, Amal Ahmed Northeastern University, USA, Steven Holtzen Northeastern University
File Attached
15:00
10m
Talk
Toward Probabilistic Coarse-to-Fine Program Synthesis
LAFI
Maddy Bowers Massachusetts Institute of Technology, Alexander K. Lew Massachusetts Institute of Technology, Vikash K. Mansinghka Massachusetts Institute of Technology, Joshua B. Tenenbaum Massachusetts Institute of Technology, Armando Solar-Lezama Massachusetts Institute of Technology
15:10
10m
Talk
Static Posterior Inference of Bayesian Probabilistic Programming via Polynomial SolvingOnline
LAFI
Peixin Wang University of Oxford, Hongfei Fu Shanghai Jiao Tong University, Tengshun Yang SKLCS, Institute of Software, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Guanyan Li University of Oxford, C.-H. Luke Ong NTU
15:20
10m
Talk
Abstract Interpretation for Automatic DifferentiationOnline
LAFI
Jacob Laurel University of Illinois at Urbana-Champaign, Siyuan Brant Qian University of Illinois at Urbana-Champaign; Zhejiang University, Gagandeep Singh University of Illinois at Urbana-Champaign; VMware Research, Sasa Misailovic University of Illinois at Urbana-Champaign