Frank Wood
59 papers · 2006–2025 · 10 conferences · across top CS/AI conferences
Achievements
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๐บ๏ธ Taxonomy Completionist (16) ๐งญ Keyword Pioneer ๐ Interdisciplinary Bridge ๐ Renaissance Researcher (5) ๐ Conference Polyglot (10)
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Interdisciplinary Bridge
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Conference Polyglot
(10)
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Keyword Pioneer
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Keyword Trendsetter Combo
(3)
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Keyword Champion
๐งฌ
Topic Evolution
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Grand Slam
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Triple Crown
๐ฌ
Deep Specialist
(20)
๐ค
Dynamic Duo
(10)
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Conference Pioneer
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Unstoppable
(16)
๐๏ธ
Keyword Collector
(77)
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Century Club
(59)
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Prolific Year
(6)
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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
Research topics
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
Donโt 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 Donโt 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
TargetโAware 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 Pรณlya 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 โThe Discrete Infinite Logistic Normal Distribution for Mixed-Membership Modelingโ
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