25 citations · 25 across the 3 of their papers we have counts for
3 papers
cs.LG2022
Distance-Ratio-Based Formulation for Metric Learning
Hyeongji Kim, Pekka Parviainen, Ketil Malde
In metric learning, the goal is to learn an embedding so that data points with the same class are close to each other and data points with different classes are far apart. We propo…
cs.LG2021
Learning Large DAGs by Combining Continuous Optimization and Feedback Arc Set Heuristics
Pierre Gillot, Pekka Parviainen
Bayesian networks represent relations between variables using a directed acyclic graph (DAG). Learning the DAG is an NP-hard problem and exact learning algorithms are feasible only…
cs.LG2012★ 25 cited
Local Structure Discovery in Bayesian Networks
Teppo Niinimaki, Pekka Parviainen
Learning a Bayesian network structure from data is an NP-hard problem and thus exact algorithms are feasible only for small data sets. Therefore, network structures for larger netw…