Mikko Koivisto
29 papers · 2004–2026 · 8 venues · across top CS/AI venues
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Achievements
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πΊοΈ Taxonomy Completionist (39) π Academic Marathon Β· Ultra π Conference Polyglot Β· Silver π Interdisciplinary Bridge Β· Silver π£ Hot Topic Early Bird
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Keyword Trendsetter Combo
(3)
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Deep Specialist
(10)
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Keyword Champion
(13)
π§
Keyword Pioneer
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Conference Pioneer
π₯
Unstoppable Β· Silver
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Century Club Β· Bronze
ποΈ
Keyword Collector
(100)
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Renaissance Researcher
(5)
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Research topics
Machine Learning
(22)
Artificial Intelligence
(16)
Mathematics & Optimization
(16)
Optimization & Theory
(15)
Bayesian & Probabilistic Β· Artificial Intelligence
(14)
Bayesian & Probabilistic Β· Machine Learning
(11)
Mathematics
(9)
Optimization
(9)
Core Methods
(6)
Computer Science
(5)
Knowledge & Reasoning
(5)
Core AI
(5)
Foundations
(5)
Learning Types
(5)
Reasoning
(4)
Keywords
bayesian network
(13)
directed acyclic graph
(11)
structure learning
(10)
markov chain monte carlo
(8)
graphical model
(6)
rejection sampling
(4)
posterior probability
(4)
bayesian network structure learning
(4)
partial order
(4)
dynamic programming
(3)
importance sampling
(3)
combinatorial optimization
(3)
matrix permanent
(2)
causal discovery
(2)
posterior distribution
(2)
linear extension
(2)
causal effect
(2)
approximate counting
(2)
exact inference
(2)
bayesian inference
(2)
Papers
Counting and Sampling Subsets Using Block Covers
UAI 2026
Scaling Up Bayesian DAG Sampling
UAI 2026
Quantum Speedups for Bayesian Network Structure Learning
UAI 2025
Estimating the Permanent by Nesting Importance Sampling
ICML 2024
Faster Perfect Sampling of Bayesian Network Structures
UAI 2024
A Faster Practical Approximation Scheme for the Permanent
AAAI 2023
On inference and learning with probabilistic generating circuits
UAI 2023
Revisiting Bayesian network learning with small vertex cover
UAI 2023
Trustworthy Monte Carlo
NeurIPS 2022
Approximating the Permanent with Deep Rejection Sampling
NeurIPS 2021
A Bayesian Approach for Estimating Causal Effects from Observational Data
AAAI 2020
Error-Correcting and Verifiable Parallel Inference in Graphical Models
AAAI 2020
Layering-MCMC for Structure Learning in Bayesian Networks
UAI 2020
Towards Scalable Bayesian Learning of Causal DAGs
NeurIPS 2020
Counting and Sampling Markov Equivalent Directed Acyclic Graphs
AAAI 2019
Exact Sampling of Directed Acyclic Graphs from Modular Distributions
UAI 2019
On Structure Priors for Learning Bayesian Networks
AISTATS 2019
A Scalable Scheme for Counting Linear Extensions
IJCAI 2018
Finding Optimal Bayesian Networks with Local Structure
PGM 2018
Intersection-Validation: A Method for Evaluating Structure Learning without Ground Truth
AISTATS 2018
The Mixing of Markov Chains on Linear Extensions in Practice
IJCAI 2017
Counting Linear Extensions of Sparse Posets
IJCAI 2016
Structure Discovery in Bayesian Networks by Sampling Partial Orders
JMLR 2016
Dealing with small data: On the generalization of context trees
ICML 2015
Learning Chordal Markov Networks by Dynamic Programming
NeurIPS 2014
Annealed Importance Sampling for Structure Learning in Bayesian Networks
IJCAI 2013
Finding Optimal Bayesian Networks Using Precedence Constraints
JMLR 2013
Bayesian structure discovery in Bayesian networks with less space
AISTATS 2010
Exact Bayesian Structure Discovery in Bayesian Networks
JMLR 2004