Antti Hyttinen
15 papers · 2012–2022 · 6 venues · across top CS/AI venues
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Achievements
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🗺️ Taxonomy Completionist (26) 🏃 Academic Marathon · Half 🌍 Conference Polyglot · Silver 🌉 Interdisciplinary Bridge · Silver 🐣 Hot Topic Early Bird
🏆
Keyword Champion
(2)
🧬
Topic Evolution
🔬
Deep Specialist
(11)
💎
Century Club · Bronze
🔥
Unstoppable · Bronze
🚀
Conference Pioneer
🗃️
Keyword Collector
(57)
📈
Trend Setter
Top co-authors
Research topics
Artificial Intelligence
(13)
Machine Learning
(13)
Core AI
(12)
Mathematics & Optimization
(8)
Knowledge & Reasoning
(7)
Reasoning
(7)
Optimization & Theory
(6)
Core Methods
(5)
Learning Types
(5)
Optimization
(5)
Bayesian & Probabilistic · Artificial Intelligence
(4)
Bayesian & Probabilistic · Machine Learning
(3)
Mathematics
(3)
Computer Science
(1)
Deep Learning
(1)
Keywords
causal discovery
(9)
structure learning
(4)
graphical model
(4)
bayesian network
(4)
causal structure
(3)
combinatorial optimization
(2)
maximum satisfiability
(2)
causal inference
(2)
context-specific independence
(2)
causal graph
(2)
constraint-based learning
(2)
causal effect
(2)
bayesian inference
(2)
directed acyclic graph
(2)
exact search
(2)
branch and bound
(1)
time series
(1)
posterior distribution
(1)
markov chain monte carlo
(1)
graph learning
(1)
Papers
A Hardware Perspective to Evaluating Probabilistic Circuits
PGM 2022
Binary independent component analysis: a non-stationarity-based approach
UAI 2022
Maximal ancestral graph structure learning via exact search
UAI 2021
A Bayesian Approach for Estimating Causal Effects from Observational Data
AAAI 2020
Learning Optimal Cyclic Causal Graphs from Interventional Data
PGM 2020
Towards Scalable Bayesian Learning of Causal DAGs
NeurIPS 2020
Identifying Causal Effects via Context-specific Independence Relations
NeurIPS 2019
Learning Optimal Causal Graphs with Exact Search
PGM 2018
Reduced Cost Fixing for Maximum Satisfiability
IJCAI 2018
Structure Learning for Bayesian Networks over Labeled DAGs
PGM 2018
A Core-Guided Approach to Learning Optimal Causal Graphs
IJCAI 2017
Learning Chordal Markov Networks via Branch and Bound
NeurIPS 2017
Causal Discovery from Subsampled Time Series Data by Constraint Optimization
PGM 2016
Experiment Selection for Causal Discovery
JMLR 2013
Learning Linear Cyclic Causal Models with Latent Variables
JMLR 2012