Research output per year
Research output per year
Research output
- 10 results
Search results
-
2024
MagLearn – Data-driven Machine Learning Framework with Transfer and Few-shot Training for Modeling Magnetic Core Loss
Zhang, L., McKeague, T., Cui, B., Rasekh, N., Wang, J., Liu, S. & Martinez, A., 20 Nov 2024, 2024 26th European Conference on Power Electronics and Applications (EPE'24 ECCE Europe). Institute of Electrical and Electronics Engineers (IEEE), 7 p.Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)
Open AccessFile53 Downloads (Pure) -
MagNet Challenge for Data-Driven Power Magnetics Modeling
Chen, M., Li, H., Wang, S., Guillod, T., Serrano, D., Zhang, L., McKeague, T., Rasekh, N., Cui, B., Wang, J., Liu, S., Martinez, A., Jiang, S., Perreault, D., Maksimovic, D., Hui, R., Kolar, J., Shumacher, D. & Sullivan, C., 30 Sept 2024, In: IEEE Open Journal of Power Electronics.Research output: Contribution to journal › Article (Academic Journal) › peer-review
Open Access4 Citations (Scopus) -
2023
Density Ratio Estimation and Neyman Pearson Classification with Missing Data
Givens, J., Liu, S. & Reeve, H. W. J., 25 Apr 2023, Proceedings of The 26th International Conference on Artificial Intelligence and Statistics. Proceedings of Machine Learning Research, Vol. 206. p. 8645-8681 37 p. (Proceedings of Machine Learning Research).Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)
Open AccessFile2 Citations (Scopus)60 Downloads (Pure) -
2019
Fisher Efficient Inference of Intractable Models
Liu, S., Kanamori, T. & Jitkrittum, W., 2019, Advances in Neural Information Processing Systems 32 (NIPS 2019).Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)
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Heterogeneous Model Reuse via Optimizing Multiparty Multiclass Margin
Wu, X., Liu, S. & Zhou, Z.-H., Jun 2019, Proceedings of the 36th International Conference on Machine Learning. Vol. 97. p. 6840-6849Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)
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2017
Model Inference with Stein Density Ratio Estimation
Liu, S., Jitkrittum, W. & Ek, C. H., 2017, (Unpublished).Research output: Contribution to conference › Conference Paper
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Support consistency of direct sparse-change learning in Markov networks
Liu, S., Suzuki, T., Relator, R., Jun, S., Sugiyama, M. & Fukumizu, K., 13 Jun 2017, In: Annals of Statistics. 45, 3, p. 959-990 30 p., 3.Research output: Contribution to journal › Article (Academic Journal) › peer-review
Open AccessFile16 Citations (Scopus)277 Downloads (Pure) -
Trimmed Density Ratio Estimation
Liu, S., Takeda, A., Suzuki, T. & Fukumizu, K., 4 Dec 2017, Advances in Neural Information Processing Systems 30 (NIPS 2017). 11 p.Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)
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2016
Estimating Posterior Ratio for Classification: Transfer Learning from Probabilistic Perspective
Liu, S. & Fukumizu, K., 5 May 2016, Proceedings of the 2016 SIAM International Conference on Data Mining. p. 747-755 9 p.Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)
2 Citations (Scopus) -
Structure Learning of Partitioned Markov Networks
Liu, S., Suzuki, T., Sugiyama, M. & Fukumizu, K., 19 Jun 2016, (E-pub ahead of print) Proceedings of The 33rd International Conference on Machine Learning. Vol. 48.Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)
Open Access