Juha Harviainen
@juhahar.bsky.social
Postdoctoral researcher at University of Helsinki / parameterized algorithms, perfect sampling, and Bayesian networks / https://juhaharviainen.com/
In collaboration with Kseniya Rychkova and Mikko Koivisto, our paper "Quantum Speedups for Bayesian Network Structure Learning" was accepted to UAI 2025.
We show the time complexity of classical algorithms for structure learning to be presumably optimal and give better quantum algorithms.
We show the time complexity of classical algorithms for structure learning to be presumably optimal and give better quantum algorithms.
May 7, 2025 at 1:48 PM
In collaboration with Kseniya Rychkova and Mikko Koivisto, our paper "Quantum Speedups for Bayesian Network Structure Learning" was accepted to UAI 2025.
We show the time complexity of classical algorithms for structure learning to be presumably optimal and give better quantum algorithms.
We show the time complexity of classical algorithms for structure learning to be presumably optimal and give better quantum algorithms.
With colleagues from TU Wien—Frank Sommer, Manuel Sorge, and @szeider.bsky.social—our paper "Optimal Decision Tree Pruning Revisited: Algorithms and Complexity" got accepted to ICML. The paper provides a comprehensive study of parameterized complexity of pruning decision trees to reduce overfitting.
May 2, 2025 at 6:01 AM
With colleagues from TU Wien—Frank Sommer, Manuel Sorge, and @szeider.bsky.social—our paper "Optimal Decision Tree Pruning Revisited: Algorithms and Complexity" got accepted to ICML. The paper provides a comprehensive study of parameterized complexity of pruning decision trees to reduce overfitting.
Our paper "On Tractability of Learning Bayesian Networks with Ancestral Constraints" with Pekka Parviainen was accepted to AISTATS'25 🎉
The paper studies parameterized complexity of structure learning for varying constraints when we can say something about the order of any set of nodes larger than k
The paper studies parameterized complexity of structure learning for varying constraints when we can say something about the order of any set of nodes larger than k
January 23, 2025 at 10:57 AM
Our paper "On Tractability of Learning Bayesian Networks with Ancestral Constraints" with Pekka Parviainen was accepted to AISTATS'25 🎉
The paper studies parameterized complexity of structure learning for varying constraints when we can say something about the order of any set of nodes larger than k
The paper studies parameterized complexity of structure learning for varying constraints when we can say something about the order of any set of nodes larger than k