← Authors

Mikko Koivisto

27 papers · 2004–2025 · 8 conferences · across top CS/AI conferences

Achievements

🌍 Conference Polyglot (8) πŸƒ Academic Marathon (21) 🌈 Renaissance Researcher (5) πŸ—ΊοΈ Taxonomy Completionist (14) πŸŒ‰ Interdisciplinary Bridge 🐝 Cross-Pollinator (13) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🌟 Keyword Trendsetter Combo (3) πŸ† Keyword Champion (2) 🧬 Topic Evolution πŸ’Ž Century Club (27) πŸ“ˆ Trend Setter πŸš€ Conference Pioneer πŸ”₯ Unstoppable (13) πŸ—ƒοΈ Keyword Collector (84)

Conferences

UAI (6) AAAI (4) IJCAI (4) NIPS (4) AISTATS (3) JMLR (3) ICML (2) PGM (1)

Top co-authors

Juha Harviainen (8) Teppo NiinimΓ€ki (6) Topi Talvitie (6) Jussi Viinikka (4) Ralf Eggeling (4) Kustaa Kangas (3) Pekka Parviainen (3) Johan Pensar (2) Petteri Kaski (2) Aleksis Vuoksenmaa (2)

Research topics

Statistics (1) Probability (1)

Keywords

bayesian network (10) structure learning (8) directed acyclic graph (8) markov chain monte carlo (6) graphical model (6) partial order (4) dynamic programming (3) combinatorial optimization (3) posterior probability (3) linear extension (2) importance sampling (2) bayesian inference (2) matrix permanent (2) exact inference (2) bayesian network structure learning (2) posterior distribution (2) causal discovery (2) causal effect (2) rejection sampling (2) graph sampling (2)

Papers

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
Revisiting Bayesian network learning with small vertex cover UAI 2023
On inference and learning with probabilistic generating circuits UAI 2023
A Faster Practical Approximation Scheme for the Permanent AAAI 2023
Trustworthy Monte Carlo NIPS 2022
Approximating the Permanent with Deep Rejection Sampling NIPS 2021
Error-Correcting and Verifiable Parallel Inference in Graphical Models AAAI 2020
Towards Scalable Bayesian Learning of Causal DAGs NIPS 2020
Layering-MCMC for Structure Learning in Bayesian Networks UAI 2020
A Bayesian Approach for Estimating Causal Effects from Observational Data AAAI 2020
Exact Sampling of Directed Acyclic Graphs from Modular Distributions UAI 2019
Counting and Sampling Markov Equivalent Directed Acyclic Graphs AAAI 2019
On Structure Priors for Learning Bayesian Networks AISTATS 2019
Intersection-Validation: A Method for Evaluating Structure Learning without Ground Truth AISTATS 2018
A Scalable Scheme for Counting Linear Extensions IJCAI 2018
Finding Optimal Bayesian Networks with Local Structure PGM 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 NIPS 2014
Finding Optimal Bayesian Networks Using Precedence Constraints JMLR 2013
Annealed Importance Sampling for Structure Learning in Bayesian Networks IJCAI 2013
Bayesian structure discovery in Bayesian networks with less space AISTATS 2010
Exact Bayesian Structure Discovery in Bayesian Networks JMLR 2004