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Matthew Hoffman

18 papers · 2007–2025 · 5 conferences · across top CS/AI conferences

Achievements

🌍 Conference Polyglot (5) πŸƒ Academic Marathon (18) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🌈 Renaissance Researcher (6) 🐝 Cross-Pollinator (13) πŸŒ‰ Interdisciplinary Bridge πŸ—ΊοΈ Taxonomy Completionist (16) πŸ‘₯ Mega-Team (20) 🌱 Topic Pioneer πŸ† Keyword Champion πŸ—ƒοΈ Keyword Collector (80) πŸš€ Conference Pioneer πŸ“ˆ Trend Setter πŸ’Ž Century Club (18) ⚑ Prolific Year (5)

Conferences

AISTATS (6) ICML (5) NIPS (4) ICLR (2) WACV (1)

Top co-authors

Ryan Adams (3) David Blei (2) Ardavan Saeedi (2) Bobak Shahriari (2) Pavel Sountsov (2) Matthew Johnson (2) Abram L. Friesen (2) Nando de Freitas (2) Arnaud Doucet (2) Jerry Li (1)

Research topics

Optimization (1)

Keywords

markov chain monte carlo (5) variational inference (4) stochastic optimization (2) policy learning (2) stochastic variational inference (2) bayesian inference (2) bayesian optimization (2) hamiltonian monte carlo (2) semi-supervised learning (1) semantic segmentation (1) langevin dynamics (1) black-box optimization (1) latent dirichlet allocation (1) topic modeling (1) online learning (1) stochastic gradient descent (1) class imbalance (1) constrained optimization (1) offline reinforcement learning (1) pseudo labeling (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