Frank Wood

2023-04-24

ICML.cc/2023/Conference (poster)

Critic Sequential Monte Carlo

Vasileios Lioutas

Jonathan Wilder Lavington

Justice Sefas

Matthew Niedoba

Yunpeng Liu

Berend Zwartsenberg

Setareh Dabiri

Adam Ścibior

We introduce CriticSMC, a new algorithm for planning as inference built from a composition of sequential Monte Carlo with learned Soft-Q fun… (see more)ction heuristic factors. These heuristic factors, obtained from parametric approximations of the marginal likelihood ahead, more effectively guide SMC towards the desired target distribution, which is particularly helpful for planning in environments with hard constraints placed sparsely in time. Compared with previous work, we modify the placement of such heuristic factors, which allows us to cheaply propose and evaluate large numbers of putative action particles, greatly increasing inference and planning efficiency. CriticSMC is compatible with informative priors, whose density function need not be known, and can be used as a model-free control algorithm. Our experiments on collision avoidance in a high-dimensional simulated driving task show that CriticSMC significantly reduces collision rates at a low computational cost while maintaining realism and diversity of driving behaviors across vehicles and environment scenarios.

2023-02-01

ICLR.cc/2023/Conference (poster)

Graphically Structured Diffusion Models

Christian Dietrich Weilbach

William Harvey

We introduce a framework for automatically defining and learning deep generative models with problem-specific structure. We tackle problem d… (see more)omains that are more traditionally solved by algorithms such as sorting, constraint satisfaction for Sudoku, and matrix factorization. Concretely, we train diffusion models with an architecture tailored to the problem specification. This problem specification should contain a graphical model describing relationships between variables, and often benefits from explicit representation of subcomputations. Permutation invariances can also be exploited. Across a diverse set of experiments we improve the scaling relationship between problem dimension and our model's performance, in terms of both training time and final accuracy.

2023-01-01

ICML (published)

Video Killed the HD-Map: Predicting Multi-Agent Behavior Directly From Aerial Images

Yunpeng Liu

Vasileios Lioutas

Jonathan Wilder Lavington

Matthew Niedoba

Justice Sefas

Setareh Dabiri

Dylan Green

Xiaoxuan Liang

Berend Zwartsenberg

Adam Ścibior

The development of algorithms that learn multi-agent behavioral models using human demonstrations has led to increasingly realistic simulati… (see more)ons in the field of autonomous driving. In general, such models learn to jointly predict trajectories for all controlled agents by exploiting road context information such as drivable lanes obtained from manually annotated high-definition (HD) maps. Recent studies show that these models can greatly benefit from increasing the amount of human data available for training. However, the manual annotation of HD maps which is necessary for every new location puts a bottleneck on efficiently scaling up human traffic datasets. We propose an aerial image-based map (AIM) representation that requires minimal annotation and provides rich road context information for traffic agents like pedestrians and vehicles. We evaluate multi-agent trajectory prediction using the AIM by incorporating it into a differentiable driving simulator as an image-texture-based differentiable rendering module. Our results demonstrate competitive multi-agent trajectory prediction performance especially for pedestrians in the scene when using our AIM representation as compared to models trained with rasterized HD maps.

2023-01-01

2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC) (published)

TITRATED: Learned Human Driving Behavior without Infractions via Amortized Inference

Vasileios Lioutas

Adam Ścibior

2022-08-12

TMLR (accepted)

Near-Optimal Glimpse Sequences for Improved Hard Attention Neural Network Training

William Harvey

Michael Teng

Hard visual attention is a promising approach to reduce the computational burden of modern computer vision methodologies. However, hard atte… (see more)ntion mechanisms can be difficult and slow to train, which is especially costly for applications like neural architecture search where multiple networks must be trained. We introduce a method to amortise the cost of training by generating an extra supervision signal for a subset of the training data. This supervision is in the form of sequences of ‘good’ locations to attend to for each image. We find that the best method to generate supervision sequences comes from framing hard attention for image classification as a Bayesian optimal experimental design (BOED) problem. From this perspective, the optimal locations to attend to are those which provide the greatest expected reduction in the entropy of the classification distribution. We introduce methodology from the BOED literature to approximate this optimal behaviour and generate ‘near-optimal’ supervision sequences. We then present a hard attention network training objective that makes use of these sequences and show that it allows faster training than prior work. We finally demonstrate the utility of faster hard attention training by incorporating supervision sequences in a neural architecture search, resulting in hard attention architectures which can outperform networks with access to the entire image.

2022-07-18

2022 International Joint Conference on Neural Networks (IJCNN) (published)

Probabilistic surrogate networks for simulators with unbounded randomness

Andreas Munk

Berend Zwartsenberg

Adam Ścibior

Atilim Güneş Baydin

Andrew Lawrence Stewart

Goran Fernlund

Anoush Poursartip

We present a framework for automatically structuring and training fast, approximate, deep neural surrogates of stochastic simulators. Unlike… (see more) traditional approaches to surrogate modeling, our surrogates retain the interpretable structure and control flow of the reference simulator. Our surrogates target stochastic simulators where the number of random variables itself can be stochastic and potentially unbounded. Our framework further enables an automatic replacement of the reference simulator with the surrogate when undertaking amortized inference. The fidelity and speed of our surrogates allow for both faster stochastic simulation and accurate and substantially faster posterior inference. Using an illustrative yet non-trivial example we show our surrogates' ability to accurately model a probabilistic program with an unbounded number of random variables. We then proceed with an example that shows our surrogates are able to accurately model a complex structure like an unbounded stack in a program synthesis example. We further demonstrate how our surrogate modeling technique makes amortized inference in complex black-box simulators an order of magnitude faster. Specifically, we do simulator-based materials quality testing, inferring safety-critical latent internal temperature profiles of composite materials undergoing curing.

2022-01-01

UAI (published)

proceedings.mlr.press

Planning as Inference in Epidemiological Models

Andrew Warrington

Saeid Naderiparizi

Christian Dietrich Weilbach

Vaden Masrani

William Harvey

Adam Ścibior

Boyan Beronov

Seyed Ali Nasseri

In this work we demonstrate how existing software tools can be used to automate parts of infectious disease-control policy-making via perfor… (see more)ming inference in existing epidemiological dynamics models. The kind of inference tasks undertaken include computing, for planning purposes, the posterior distribution over putatively controllable, via direct policy-making choices, simulation model parameters that give rise to acceptable disease progression outcomes. Neither the full capabilities of such inference automation software tools nor their utility for planning is widely disseminated at the current time. Timely gains in understanding about these tools and how they can be used may lead to more fine-grained and less economically damaging policy prescriptions, particularly during the current COVID-19 pandemic.

2020-03-30

arXiv.org (preprint)

dblp.uni-trier.de

Planning as Inference in Epidemiological Models

Andrew Warrington

Saeid Naderiparizi

Christian Dietrich Weilbach

Vaden Masrani

William Harvey

Adam Ścibior

Boyan Beronov

Seyed Ali Nasseri

In this work we demonstrate how existing software tools can be used to automate parts of infectious disease-control policy-making via perfor… (see more)ming inference in existing epidemiological dynamics models. The kind of inference tasks undertaken include computing, for planning purposes, the posterior distribution over putatively controllable, via direct policy-making choices, simulation model parameters that give rise to acceptable disease progression outcomes. Neither the full capabilities of such inference automation software tools nor their utility for planning is widely disseminated at the current time. Timely gains in understanding about these tools and how they can be used may lead to more fine-grained and less economically damaging policy prescriptions, particularly during the current COVID-19 pandemic.

2020-03-30

arXiv.org (preprint)

dblp.uni-trier.de

Coping With Simulators That Don't Always Return

Andrew Warrington

Saeid Naderiparizi

Deterministic models are approximations of reality that are easy to interpret and often easier to build than stochastic alternatives. Unfort… (see more)unately, as nature is capricious, observational data can never be fully explained by deterministic models in practice. Observation and process noise need to be added to adapt deterministic models to behave stochastically, such that they are capable of explaining and extrapolating from noisy data. We investigate and address computational inefficiencies that arise from adding process noise to deterministic simulators that fail to return for certain inputs; a property we describe as "brittle." We show how to train a conditional normalizing flow to propose perturbations such that the simulator succeeds with high probability, increasing computational efficiency.

2020-03-28

ArXiv (preprint)

Coping With Simulators That Don't Always Return

Andrew Warrington

Saeid Naderiparizi

2020-01-01

AISTATS (published)

proceedings.mlr.press

Structured Conditional Continuous Normalizing Flows for Efficient Amortized Inference in Graphical Models

Christian Dietrich Weilbach

Boyan Beronov