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Frank Wood

59 papers · 2006–2025 · 10 conferences · across top CS/AI conferences

Achievements

πŸ—ΊοΈ Taxonomy Completionist (16) 🐝 Cross-Pollinator (14) 🐣 Hot Topic Early Bird πŸƒ Academic Marathon (19) 🌈 Renaissance Researcher (5) πŸŒ‰ Interdisciplinary Bridge 🌍 Conference Polyglot (10) 🧭 Keyword Pioneer 🌟 Keyword Trendsetter Combo (3) πŸ† Keyword Champion 🧬 Topic Evolution πŸ† Grand Slam πŸ‘‘ Triple Crown πŸ”¬ Deep Specialist (20) 🀝 Dynamic Duo (10) πŸš€ Conference Pioneer πŸ”₯ Unstoppable (16) πŸ—ƒοΈ Keyword Collector (77) πŸ’Ž Century Club (59) ⚑ Prolific Year (6) πŸ“ˆ Trend Setter

Conferences

ICML (17) NIPS (16) AISTATS (11) UAI (5) ICLR (4) JMLR (2) AAAI (1) CVPR (1) EMNLP (1) WACV (1)

Top co-authors

Tom Rainforth (10) Saeid Naderiparizi (8) Tuan Anh Le (8) Adam Scibior (7) Berend Zwartsenberg (7) Vaden Masrani (6) Brooks Paige (6) Yee Whye Teh (5) Andreas Munk (4) Atilim Gunes Baydin (4)

Research topics

Statistics (1) Probability (1)

Keywords

variational inference (13) generative model (10) amortized inference (10) markov chain monte carlo (8) probabilistic programming (8) sequential monte carlo (7) bayesian inference (7) bayesian nonparametrics (6) graphical model (5) dirichlet process (3) inference compilation (3) stochastic simulator (3) diffusion model (3) importance sampling (3) evidence lower bound (3) semi-supervised learning (2) mahalanobis distance (2) hidden markov model (2) nonparametric bayesian (2) posterior inference (2)

Papers

Constrained Generative Modeling with Manually Bridged Diffusion Models AAAI 2025
Towards a Mechanistic Explanation of Diffusion Model Generalization ICML 2025
Don’t be so Negative! Score-based Generative Modeling with Oracle-assisted Guidance ICML 2024
Layerwise Proximal Replay: A Proximal Point Method for Online Continual Learning ICML 2024
All-in-one simulation-based inference ICML 2024
Nearest Neighbour Score Estimators for Diffusion Generative Models ICML 2024
A Diffusion-Model of Joint Interactive Navigation NIPS 2023
Uncertain Evidence in Probabilistic Models and Stochastic Simulators ICML 2023
Graphically Structured Diffusion Models ICML 2023
Critic Sequential Monte Carlo ICLR 2023
Probabilistic surrogate networks for simulators with unbounded randomness UAI 2022
Enhancing Few-Shot Image Classification With Unlabelled Examples WACV 2022
Amortized Rejection Sampling in Universal Probabilistic Programming AISTATS 2022
Flexible Diffusion Modeling of Long Videos NIPS 2022
BayesPCN: A Continually Learnable Predictive Coding Associative Memory NIPS 2022
Conditional Image Generation by Conditioning Variational Auto-Encoders ICLR 2022
q-Paths: Generalizing the geometric annealing path using power means UAI 2021
Robust Asymmetric Learning in POMDPs ICML 2021
Sequential core-set Monte Carlo UAI 2021
Semi-supervised Sequential Generative Models UAI 2020
Coping With Simulators That Don’t Always Return AISTATS 2020
Structured Conditional Continuous Normalizing Flows for Efficient Amortized Inference in Graphical Models AISTATS 2020
Improved Few-Shot Visual Classification CVPR 2020
All in the Exponential Family: Bregman Duality in Thermodynamic Variational Inference ICML 2020
Gaussian Process Bandit Optimization of the Thermodynamic Variational Objective NIPS 2020
Target–Aware Bayesian Inference: How to Beat Optimal Conventional Estimators JMLR 2020
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model NIPS 2019
The Thermodynamic Variational Objective NIPS 2019
Amortized Monte Carlo Integration ICML 2019
Revisiting Reweighted Wake-Sleep for Models with Stochastic Control Flow UAI 2019
LF-PPL: A Low-Level First Order Probabilistic Programming Language for Non-Differentiable Models AISTATS 2019
Tighter Variational Bounds are Not Necessarily Better ICML 2018
On Nesting Monte Carlo Estimators ICML 2018
Bayesian Distributed Stochastic Gradient Descent NIPS 2018
Faithful Inversion of Generative Models for Effective Amortized Inference NIPS 2018
Online Learning Rate Adaptation with Hypergradient Descent ICLR 2018
Auto-Encoding Sequential Monte Carlo ICLR 2018
Deep Variational Reinforcement Learning for POMDPs ICML 2018
Learning Disentangled Representations with Semi-Supervised Deep Generative Models NIPS 2017
Inference Compilation and Universal Probabilistic Programming AISTATS 2017
Generalized PΓ³lya Urn for Time-Varying Pitman-Yor Processes JMLR 2017
Interacting Particle Markov Chain Monte Carlo ICML 2016
Black-Box Policy Search with Probabilistic Programs AISTATS 2016
Bayesian Optimization for Probabilistic Programs NIPS 2016
Inference Networks for Sequential Monte Carlo in Graphical Models ICML 2016
Particle Gibbs with Ancestor Sampling for Probabilistic Programs AISTATS 2015
Asynchronous Anytime Sequential Monte Carlo NIPS 2014
A New Approach to Probabilistic Programming Inference AISTATS 2014
The Dependent Dirichlet Process Mixture of Objects for Detection-free Tracking and Object Modeling AISTATS 2014
A Compilation Target for Probabilistic Programming Languages ICML 2014
Hierarchically-coupled hidden Markov models for learning kinetic rates from single-molecule data ICML 2013
A Joint Learning Model of Word Segmentation, Lexical Acquisition, and Phonetic Variability EMNLP 2013
Low rank continuous-space graphical models AISTATS 2012
Discussion of β€œThe Discrete Infinite Logistic Normal Distribution for Mixed-Membership Modeling” AISTATS 2011
Hierarchically Supervised Latent Dirichlet Allocation NIPS 2011
Probabilistic Deterministic Infinite Automata NIPS 2010
Dependent Dirichlet Process Spike Sorting NIPS 2008
Characterizing neural dependencies with copula models NIPS 2008
Particle Filtering for Nonparametric Bayesian Matrix Factorization NIPS 2006