Matthew Hoffman
19 papers · 2005–2025 · 5 venues · across top CS/AI venues
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Achievements
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πΊοΈ Taxonomy Completionist (43) π Academic Marathon Β· Ultra π Conference Polyglot Β· Silver π Interdisciplinary Bridge Β· Bronze π£ Hot Topic Early Bird
π±
Topic Pioneer
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Deep Specialist
(11)
π₯
Mega-Team Β· Squad
π
Keyword Champion
π§
Keyword Pioneer
π
Trend Setter
π
Conference Pioneer
ποΈ
Keyword Collector
(94)
β‘
Prolific Year Β· Productive
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Century Club Β· Bronze
π
Renaissance Researcher
(6)
Top co-authors
Research topics
Machine Learning
(17)
Optimization & Theory
(11)
Artificial Intelligence
(7)
Learning Types
(7)
Bayesian & Probabilistic Β· Machine Learning
(6)
Deep Learning
(5)
Models
(5)
Core AI
(4)
Mathematics & Optimization
(3)
Bayesian & Probabilistic Β· Artificial Intelligence
(3)
Optimization
(3)
Computer Vision
(2)
Natural Language Processing
(2)
Reinforcement Learning
(2)
Application Areas
(2)
Keywords
markov chain monte carlo
(5)
variational inference
(4)
policy learning
(2)
bayesian optimization
(2)
hamiltonian monte carlo
(2)
bayesian inference
(2)
stochastic optimization
(2)
stochastic variational inference
(2)
semantic segmentation
(1)
online learning
(1)
zero-shot learning
(1)
black-box optimization
(1)
langevin dynamics
(1)
offline reinforcement learning
(1)
bayesian nonparametrics
(1)
stochastic gradient descent
(1)
topic modeling
(1)
knowledge distillation
(1)
expectation maximization
(1)
few-shot learning
(1)
Papers
BOND: Aligning LLMs with Best-of-N Distillation
ICLR 2025
Semantic Segmentation With Active Semi-Supervised Learning
WACV 2023
An Adaptive-MCMC Scheme for Setting Trajectory Lengths in Hamiltonian Monte Carlo
AISTATS 2021
Automatic Reparameterisation of Probabilistic Programs
ICML 2020
Black-Box Variational Inference as a Parametric Approximation to Langevin Dynamics
ICML 2020
Hamiltonian Monte Carlo Swindles
AISTATS 2020
Modular Meta-Learning with Shrinkage
NeurIPS 2020
RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning
NeurIPS 2020
Latent Constraints: Learning to Generate Conditionally from Unconditional Generative Models
ICLR 2018
Multimodal Prediction and Personalization of Photo Edits with Deep Generative Models
AISTATS 2018
On the challenges of learning with inference networks on sparse, high-dimensional data
AISTATS 2018
A Variational Analysis of Stochastic Gradient Algorithms
ICML 2016
The Segmented iHMM: A Simple, Efficient Hierarchical Infinite HMM
ICML 2016
Predictive Entropy Search for Bayesian Optimization with Unknown Constraints
ICML 2015
Stochastic Structured Variational Inference
AISTATS 2015
On correlation and budget constraints in model-based bandit optimization with application to automatic machine learning
AISTATS 2014
Online Learning for Latent Dirichlet Allocation
NeurIPS 2010
Bayesian Policy Learning with Trans-Dimensional MCMC
NeurIPS 2007
An Expectation Maximization Algorithm for Continuous Markov Decision Processes with Arbitrary Reward
AISTATS 2005