Machine learning, Bayesian inference, Probabilistic programming, Deep generative models

I am postdoc working in Yee Whye Teh’s group at the department of statistics in Oxford. Please see my personal website here for further details.

## 2019

• , , , M. P. Kumar , A Statistical Approach to Assessing Neural Network Robustness, in International Conference on Learning Representations (ICLR), 2019.
Project: bigbayes
• Y. Zhou , , T. Kohn , , H. Yang , F. Wood , A Low-Level Probabilistic Programming Language for Non-Differentiable Models, International Conference on Artificial Intelligence and Statistics (AISTATS, to appear), 2019.
Project: bigbayes

## 2018

• , , R. Zinkov , N. Siddharth , , , F. Wood , Faithful Inversion of Generative Models for Effective Amortized Inference, in Advances in Neural Information Processing Systems (NeurIPS), 2018.
Project: bigbayes
• , , F. Wood , , Amortized Monte Carlo Integration, in Symposium on Advances in Approximate Bayesian Inference, 2018.
Project: bigbayes
• , , T. A. Le , , M. Igl , F. Wood , , Tighter Variational Bounds are Not Necessarily Better, in International Conference on Machine Learning (ICML), 2018.
Project: bigbayes
• , M. Jankowiak , E. Bingham , , , N. Goodman , Variational Optimal Experiment Design: Efficient Automation of Adaptive Experiments, NeurIPS Workshop on Bayesian Deep Learning, 2018.
Project: bigbayes
• , , R. Zinkov , N. Siddharth , , , F. Wood , Faithful Inversion of Generative Models for Effective Amortized Inference, in Advances in Neural Information Processing Systems (NeurIPS), 2018.
Project: bigbayes
• B. J. Gram-Hansen , Y. Zhou , T. Kohn , , H. Yang , F. Wood , Discontinuous Hamiltonian Monte Carlo for Probabilistic Programs, The International Conference on Probabilistic Programming, 2018.
• , , S. Narayanaswamy , , Disentangling Disentanglement, NeurIPS Workshop on Bayesian Deep Learning, 2018.
Project: bigbayes
• , Y. Zhou , , , F. Wood , H. Yang , J. Meent , Inference Trees: Adaptive Inference with Exploration, arXiv preprint arXiv:1806.09550, 2018.
Project: bigbayes
• , , Y. Zhou , J. Meent , , On Exploration, Exploitation and Learning in Adaptive Importance Sampling, arXiv preprint arXiv:1810.13296, 2018.
Project: bigbayes
• , Nesting Probabilistic Programs, Conference on Uncertainty in Artificial Intelligence (UAI), 2018.
Project: bigbayes
• , R. Cornish , H. Yang , A. Warrington , F. Wood , On Nesting Monte Carlo Estimators, International Conference on Machine Learning (ICML), 2018.
Project: bigbayes
• T. A. Le , M. Igl , , T. Jin , F. Wood , Auto-Encoding Sequential Monte Carlo, in International Conference on Learning Representations, 2018.

## 2017

• , Automating Inference, Learning, and Design using Probabilistic Programming, PhD thesis, University of Oxford, 2017.
• B. T. Vincent , , The DARC Toolbox: automated, flexible, and efficient delayed and risky choice experiments using Bayesian adaptive design, 2017.
• , , , , H. Ge , M. Lomelí , Z. Ghahramani , , Sampling and inference for discrete random probability measures in probabilistic programs, NIPS Workshop on Advances in Approximate Bayesian Inference, 2017.
Project: bigbayes

## 2016

• , T. A. Le , J. Meent , M. A. Osborne , F. Wood , Bayesian Optimization for Probabilistic Programs, in Advances in Neural Information Processing Systems, 2016, 280–288.
• , R. Cornish , H. Yang , F. Wood , On the Pitfalls of Nested Monte Carlo, NIPS Workshop on Advances in Approximate Bayesian Inference, 2016.
• D. Janz , B. Paige , , J. Meent , F. Wood , Probabilistic Structure Discovery in Time Series Data, NIPS Workshop on Artificial Intelligence for Data Science, 2016.
• , C. A. Naesseth , F. Lindsten , B. Paige , J. Meent , , F. Wood , Interacting Particle Markov Chain Monte Carlo, in Proceedings of the 33rd International Conference on Machine Learning, 2016, vol. 48.

## 2015

• , F. Wood , Canonical Correlation Forests, arXiv preprint arXiv:1507.05444, 2015.