Efficient Inference for Higher-Order Probabilistic Programs

Context of the research proposal:

Many scientific models can be naturally expressed as stochastic simulators. Probabilistic programming allows users to exploit the source code information of these simulators to conduct Bayesian inference. Full-scale Bayesian inference in general stochastic simulators essentially provides users with a principled way to invert simulators based on observed data. For example, given a simulator that models disease outbreaks and some observed data we can infer the underlying latent parameters which best describe the given disease outbreak.

However, most inference algorithms in Bayesian statistics are designed for models which have a fixed dimensionality. In contrast, higher-order probabilistic programming allows the user to define models which have a variable (possibly even infinite) number of latent variables. The generality of models expressed in higher-order probabilistic programs requires the design of new inference algorithms which are sufficiently general and can exploit the program structure of the simulator. The potential impact of efficient and general Bayesian inference in these simulators would be enormous as it would allow for entirely new scientific workflows of building accurate simulators which can be inverted and improved based on observed data.

Aims and objectives:

Develop novel inference algorithms which are more tailored to a specific probabilistic program based on static analysis of source code
Integrate inference algorithms within popular probabilistic programming environments such as Pyro, Turing or PyProb so that they are accessible to a large number of users

Novelty of the research methodology:

Efficient inference algorithms for higher-order probabilistic programs are still an active area of research. Current approaches are mostly based on Importance Sampling, Sequential Monte Carlo or Variational Inference. We hope to improve upon these approaches and/or potentially unlock entirely new types of inference algorithms.

Alignment to EPSRC's strategies and research areas:

Artificial Intelligence technologies
Programming languages and compilers
Statistic and applied probability
Theoretical computer science

AIMS's impact will be felt across domains of acute need within the UK. We expect AIMS to benefit: UK economic performance, through start-up creation; existing UK firms, both through research and addressing skills needs; UK health, by contributing to cancer research, and quality of life, through the delivery of autonomous vehicles; UK public understanding of and policy related to the transformational societal change engendered by autonomous systems.

Autonomous systems are acknowledged by essentially all stakeholders as important to the future UK economy. PwC claim that there is a £232 billion opportunity offered by AI to the UK economy by 2030 (10% of GDP). AIMS has an excellent track record of leadership in spinout creation, and will continue to foster the commercial projects of its students, through the provision of training in IP, licensing and entrepreneurship. With the help of Oxford Science Innovation (investment fund) and Oxford University Innovation (technology transfer office), student projects will be evaluated for commercial potential.

AIMS will also concretely contribute to UK economic competitiveness by meeting the UK's needs for experts in autonomous systems. To meet this need, AIMS will train cohorts with advanced skills that span the breadth of AI, machine learning, robotics, verification and sensor systems. The relevance of the training to the needs of industry will be ensured by the industrial partnerships at the heart of AIMS. These partnerships will also ensure that AIMS will produce research that directly targets UK industrial needs. Our partners span a wide range of UK sectors, including energy, transport, infrastructure, factory automation, finance, health, space and other extreme environments.

The autonomous systems that AIMS will enable also offer the prospect of epochal change in the UK's quality of life and health. As put by former Digital Secretary Matt Hancock, "whether it's improving travel, making banking easier or helping people live longer, AI is already revolutionising our economy and our society." AIMS will help to realise this potential through its delivery of trained experts and targeted research. In particular, two of the four Grand Challenge missions in the UK Industrial Strategy highlight the positive societal impact underpinned by autonomous systems. The "Artificial Intelligence and data" challenge has as its mission to "Use data, Artificial Intelligence and innovation to transform the prevention, early diagnosis and treatment of chronic diseases by 2030". To this mission, AIMS will contribute the outputs of its research pillar on cancer research. The "Future of mobility" challenge highlights the importance the autonomous vehicles will have in making transport "safer, cleaner and better connected." To this challenge, AIMS offers the world-leading research of its robotic systems research pillar.

AIMS will further promote the positive realisation of autonomous technologies through direct influence on policy. The world-leading academics amongst AIMS's supervisory pool are well-connected to policy formation e.g. Prof Osborne serving as a Commissioner on the Independent Commission on the Future of Work. Further, Dr Dan Mawson, Head of the Economy Unit; Economy and Strategic Analysis Team at BEIS will serve as an advisor to AIMS, ensuring bidirectional influence between policy objectives and AIMS research and training.

Broad understanding of autonomous systems is crucial in making a society robust to the transformations they will engender. AIMS will foster such understanding through its provision of opportunities for AIMS students to directly engage with the public. Given the broad societal importance of getting autonomous systems right, AIMS will deliver core training on the ethical, governance, economic and societal implications of autonomous systems.