Adam Scibior — accepted AI-conference papers

2024

Nearest Neighbour Score Estimators for Diffusion Generative Models

Matthew Niedoba, Dylan Green, Saeid Naderiparizi, Vasileios Lioutas, Jonathan Wilder Lavington, Xiaoxuan Liang +6

ICML 2024poster

Score function estimation is the cornerstone of both training and sampling from diffusion generative models. Despite this fact, the most commonly used estimators are either biased neural network approximations or high variance Monte Carlo estimators based on the conditional score. We introduce a nov…

Cited by 1 Source ↗PDF ↗code ↗Scholar ↗

2023

A Diffusion-Model of Joint Interactive Navigation

Matthew Niedoba, Jonathan Wilder Lavington, Yunpeng Liu, Vasileios Lioutas, Justice Sefas, Xiaoxuan Liang +5

NeurIPS 2023poster

Simulation of autonomous vehicle systems requires that simulated traffic participants exhibit diverse and realistic behaviors. The use of prerecorded real-world traffic scenarios in simulation ensures realism but the rarity of safety critical events makes large scale collection of driving scenarios…

Cited by 15 Source ↗PDF ↗Scholar ↗

2023

Critic Sequential Monte Carlo

Vasileios Lioutas, Jonathan Wilder Lavington, Justice Sefas, Matthew Niedoba, Yunpeng Liu, Berend Zwartsenberg +3

ICLR 2023poster

We introduce CriticSMC, a new algorithm for planning as inference built from a composition of sequential Monte Carlo with learned Soft-Q function heuristic factors. These heuristic factors, obtained from parametric approximations of the marginal likelihood ahead, more effectively guide SMC towards t…

Cited by 9 Source ↗PDF ↗Scholar ↗

2022

Amortized Rejection Sampling in Universal Probabilistic Programming

Saeid Naderiparizi, Adam Scibior, Andreas Munk, Mehrdad Ghadiri, Atilim Gunes Baydin, Bradley J. Gram-Hansen +6

AISTATS 2022poster

Naive approaches to amortized inference in probabilistic programs with unbounded loops can produce estimators with infinite variance. This is particularly true of importance sampling inference in programs that explicitly include rejection sampling as part of the user-programmed generative procedure.…

Cited by 7 Source ↗PDF ↗code ↗Scholar ↗

2022

Vehicle Type Specific Waypoint Generation

Yunpeng Liu, Jonathan Wilder Lavington, Adam Scibior, Frank Wood

IROS 2022poster

We develop a generic mechanism for generating vehicle-type specific sequences of waypoints from a probabilistic foundation model of driving behavior. Many foundation behavior models are trained on data that does not include vehicle information, which limits their utility in downstream applications s…

Cited by 1 Source ↗Scholar ↗

2021

Robust Asymmetric Learning in POMDPs

Andrew Warrington, Jonathan W Lavington, Adam Scibior, Mark Schmidt, Frank Wood

ICML 2021oral

Policies for partially observed Markov decision processes can be efficiently learned by imitating expert policies generated using asymmetric information. Unfortunately, existing approaches for this kind of imitation learning have a serious flaw: the expert does not know what the trainee cannot see,…

Cited by 41 Source ↗PDF ↗code ↗Scholar ↗

2020

Semi-supervised Sequential Generative Models

Michael Teng, Tuan Anh Le, Adam Scibior, Frank Wood

UAI 2020poster

We introduce a novel objective for training deep generative time-series models with discrete latent variables for which supervision is only sparsely available. This instance of semi-supervised learning is challenging for existing methods, because the exponential number of possible discrete latent co…

Cited by 5 Source ↗PDF ↗Scholar ↗

2016

Consistent Kernel Mean Estimation for Functions of Random Variables

Carl-Johann Simon-Gabriel, Adam Scibior, Ilya O Tolstikhin, Bernhard Schölkopf

NeurIPS 2016poster

We provide a theoretical foundation for non-parametric estimation of functions of random variables using kernel mean embeddings. We show that for any continuous function f, consistent estimators of the mean embedding of a random variable X lead to consistent estimators of the mean embedding of f(X).…

Cited by 12 Source ↗PDF ↗Scholar ↗