Research
I am an associate professor in machine learning.
My main research interests lie within probabilistic graphical models, especially in structure learning in Bayesian networks.
Publications
Academic article
- Miraki, Amir; Parviainen, Pekka; Arghandeh, Reza (2025). Probabilistic forecasting of renewable energy and electricity demand using Graph-based Denoising Diffusion Probabilistic Model. (external link)
- Kim, Hyeongji; Choi, Changkyu; Kampffmeyer, Michael Christian et al. (2025). ProxyDR: Deep Hyperspherical Metric Learning with Distance Ratio-Based Formulation. (external link)
- Kjærsgaard, Rune D.; Parviainen, Pekka; Saurabh, Saket et al. (2024). Fair Soft Clustering. (external link)
- Kundu, Madhumita; Parviainen, Pekka; Saurabh, Saket (2024). Time–Approximation Trade-Offs for Learning Bayesian Networks. (external link)
- Kundu, Madhumita; Parviainen, Pekka; Saurabh, Saket (2024). Discovering Bayesian Networks when Few Variables Matter. (external link)
- Korhonen, Tuukka; Fomin, Fedor; Parviainen, Pekka (2024). Structural perspective on constraint-based learning of Markov networks. (external link)
- Inamdar, Tanmay; Kundu, Madhumita; Parviainen, Pekka et al. (2024). Exponential-Time Approximation Schemes via Compression. (external link)
- Miraki, Amir; Parviainen, Pekka; Arghandeh, Reza (2024). Electricity demand forecasting at distribution and household levels using explainable causal graph neural network. (external link)
- Kim, Hyeongji; Parviainen, Pekka; Malde, Ketil (2023). Measuring Adversarial Robustness using a Voronoi-Epsilon Adversary. (external link)
- Fiedler, Johannes; Palau, Adria Salvador; Osestad, Eivind Kristen et al. (2023). Realistic mask generation for matter-wave lithography via machine learning. (external link)
- Håvardstun, Brigt Arve Toppe; Ferri, Cesar; Hernández-Orallo, José et al. (2022). On the trade-off between fidelity and teaching complexity. (external link)
- Gillot, Pierre; Parviainen, Pekka (2022). Convergence of Feedback Arc Set-Based Heuristics for Linear Structural Equation Models. (external link)
- Talvitie, Topi; Parviainen, Pekka (2020). Learning Bayesian Networks with Cops and Robbers. (external link)
- Gillot, Pierre; Parviainen, Pekka (2020). Scalable Bayesian Network Structure Learning via Maximum Acyclic Subgraph. (external link)
- Qin, Xiangju; Blomstedt, Paul; Leppäaho, Eemeli et al. (2019). Distributed Bayesian matrix factorization with limited communication. (external link)
Poster
- Kim, Hyeongji; Choi, Changkyu; Kampffmeyer, Michael Christian et al. (2024). ProxyDR: Deep Hyperspherical Metric Learning with Distance Ratio-Based Formulation. (external link)
- Kim, Hyeongji; Choi, Changkyu; Kampffmeyer, Michael Christian et al. (2024). ProxyDR: Deep Hyperspherical Metric Learning with Distance Ratio-Based Formulation. (external link)
Doctoral dissertation
Academic lecture
Academic chapter/article/Conference paper
- Håvardstun, Brigt Arve Toppe; Ferri, Cesar; Hernández-Orallo, José et al. (2023). XAI with Machine Teaching When Humans Are (Not) Informed About the Irrelevant Features. (external link)
- Gillot, Pierre; Parviainen, Pekka (2022). Learning Large DAGs by Combining Continuous Optimization and Feedback Arc Set Heuristics. (external link)
See a complete overview of publications in Cristin.
List of my publications can be found in my google scholar profile.