12 citations · 24 across the 5 of their papers we have counts for
5 papers
Coalitions of Large Language Models Increase the Robustness of AI Agents
Prattyush Mangal, Carol Mak, Theo Kanakis +3
The emergence of Large Language Models (LLMs) have fundamentally altered the way we interact with digital systems and have led to the pursuit of LLM powered AI agents to assist in…
Nonparametric Involutive Markov Chain Monte Carlo
Carol Mak, Fabian Zaiser, Luke Ong
A challenging problem in probabilistic programming is to develop inference algorithms that work for arbitrary programs in a universal probabilistic programming language (PPL). We p…
Nonparametric Hamiltonian Monte Carlo
Carol Mak, Fabian Zaiser, Luke Ong
Probabilistic programming uses programs to express generative models whose posterior probability is then computed by built-in inference engines. A challenging goal is to develop ge…
Densities of Almost Surely Terminating Probabilistic Programs are Differentiable Almost Everywhere
Carol Mak, C. -H. Luke Ong, Hugo Paquet +1
We study the differential properties of higher-order statistical probabilistic programs with recursion and conditioning. Our starting point is an open problem posed by Hongseok Yan…
A Differential-form Pullback Programming Language for Higher-order Reverse-mode Automatic Differentiation
Carol Mak, Luke Ong
Building on the observation that reverse-mode automatic differentiation (AD) -- a generalisation of backpropagation -- can naturally be expressed as pullbacks of differential 1-for…