17 citations · 44 across the 4 of their papers we have counts for
4 papers
Generative AI for Programming Education: Benchmarking ChatGPT, GPT-4, and Human Tutors
Tung Phung, Victor-Alexandru Pădurean, José Cambronero +5
Generative AI and large language models hold great promise in enhancing computing education by powering next-generation educational technologies for introductory programming. Recen…
Generating High-Precision Feedback for Programming Syntax Errors using Large Language Models
Tung Phung, José Cambronero, Sumit Gulwani +4
Large language models (LLMs), such as Codex, hold great promise in enhancing programming education by automatically generating feedback for students. We investigate using LLMs to g…
LF-PPL: A Low-Level First Order Probabilistic Programming Language for Non-Differentiable Models
Yuan Zhou, Bradley J. Gram-Hansen, Tobias Kohn +3
We develop a new Low-level, First-order Probabilistic Programming Language (LF-PPL) suited for models containing a mix of continuous, discrete, and/or piecewise-continuous variable…
Hamiltonian Monte Carlo for Probabilistic Programs with Discontinuities
Bradley Gram-Hansen, Yuan Zhou, Tobias Kohn +3
Hamiltonian Monte Carlo (HMC) is arguably the dominant statistical inference algorithm used in most popular "first-order differentiable" Probabilistic Programming Languages (PPLs).…