Matthew Hoffman
18 papers · 2007–2025 · 5 conferences · across top CS/AI conferences
Achievements
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πΊοΈ Taxonomy Completionist (16) π§ Keyword Pioneer π Renaissance Researcher (6) π Interdisciplinary Bridge π£ Hot Topic Early Bird
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Cross-Pollinator
(13)
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Interdisciplinary Bridge
πΊοΈ
Taxonomy Completionist
(16)
π₯
Mega-Team
(20)
π±
Topic Pioneer
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Keyword Champion
ποΈ
Keyword Collector
(80)
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Conference Pioneer
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Trend Setter
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Century Club
(18)
β‘
Prolific Year
(5)
Conferences
AISTATS (6)
ICML (5)
NIPS (4)
ICLR (2)
WACV (1)
Top co-authors
Research topics
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)
semi-supervised learning
(1)
black-box optimization
(1)
semantic segmentation
(1)
langevin dynamics
(1)
online learning
(1)
latent dirichlet allocation
(1)
offline reinforcement learning
(1)
stochastic gradient descent
(1)
constrained optimization
(1)
pseudo labeling
(1)
class imbalance
(1)
bayesian nonparametrics
(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
Hamiltonian Monte Carlo Swindles
AISTATS 2020
Modular Meta-Learning with Shrinkage
NIPS 2020
RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning
NIPS 2020
Black-Box Variational Inference as a Parametric Approximation to Langevin Dynamics
ICML 2020
Automatic Reparameterisation of Probabilistic Programs
ICML 2020
Latent Constraints: Learning to Generate Conditionally from Unconditional Generative Models
ICLR 2018
On the challenges of learning with inference networks on sparse, high-dimensional data
AISTATS 2018
Multimodal Prediction and Personalization of Photo Edits with Deep Generative Models
AISTATS 2018
The Segmented iHMM: A Simple, Efficient Hierarchical Infinite HMM
ICML 2016
A Variational Analysis of Stochastic Gradient Algorithms
ICML 2016
Stochastic Structured Variational Inference
AISTATS 2015
Predictive Entropy Search for Bayesian Optimization with Unknown Constraints
ICML 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
NIPS 2010
Bayesian Policy Learning with Trans-Dimensional MCMC
NIPS 2007