References in zbMATH (referenced in 58 articles )

Showing results 1 to 20 of 58.
Sorted by year (citations)

1 2 3 next

  1. Kroer, Christian; Peysakhovich, Alexander; Sodomka, Eric; Stier-Moses, Nicolas E.: Computing large market equilibria using abstractions (2022)
  2. Pfannschmidt, Karlson; Gupta, Pritha; Haddenhorst, Björn; Hüllermeier, Eyke: Learning context-dependent choice functions (2022)
  3. Anderson, Paul E.; Chartier, Timothy P.; Langville, Amy N.; Pedings-Behling, Kathryn E.: The rankability of weighted data from pairwise comparisons (2021)
  4. Atarashi, Kyohei; Oyama, Satoshi; Kurihara, Masahito: Factorization machines with regularization for sparse feature interactions (2021)
  5. Balasubramanian, Krishnakumar: Nonparametric modeling of higher-order interactions via hypergraphons (2021)
  6. Burashnikova, Aleksandra; Maximov, Yury; Clausel, Marianne; Laclau, Charlotte; Iutzeler, Franck; Amini, Massih-Reza: Learning over no-preferred and preferred sequence of items for robust recommendation (2021)
  7. Chen, Yunxiao; Ying, Zhiliang; Zhang, Haoran: Unfolding-model-based visualization: theory, method and applications (2021)
  8. Dong, Shuyu; Absil, P.-A.; Gallivan, K. A.: Riemannian gradient descent methods for graph-regularized matrix completion (2021)
  9. Garber, Dan: On the convergence of projected-gradient methods with low-rank projections for smooth convex minimization over trace-norm balls and related problems (2021)
  10. Gomez-Uribe, Carlos A.; Karrer, Brian: The decoupled extended Kalman filter for dynamic exponential-family factorization models (2021)
  11. Kadıoğlu, Serdar; Kleynhans, Bernard; Wang, Xin: Optimized item selection to boost exploration for recommender systems (2021)
  12. Rago, Antonio; Cocarascu, Oana; Bechlivanidis, Christos; Lagnado, David; Toni, Francesca: Argumentative explanations for interactive recommendations (2021)
  13. Ramanan, Nandini; Kunapuli, Gautam; Khot, Tushar; Fatemi, Bahare; Kazemi, Seyed Mehran; Poole, David; Kersting, Kristian; Natarajan, Sriraam: Structure learning for relational logistic regression: an ensemble approach (2021)
  14. Wang, Wei; Stephens, Matthew: Empirical Bayes matrix factorization (2021)
  15. Watanabe, Chihiro; Suzuki, Taiji: Selective inference for latent block models (2021)
  16. Babkin, Andrey: Incorporating side information into robust matrix factorization with Bayesian quantile regression (2020)
  17. Beck, Amir; Hallak, Nadav: On the convergence to stationary points of deterministic and randomized feasible descent directions methods (2020)
  18. Beckerleg, Melanie; Thompson, Andrew: A divide-and-conquer algorithm for binary matrix completion (2020)
  19. Cheng, Xiaoye; Zhang, Jingjing; Yan, Lu (Lucy): Understanding the impact of individual users’ rating characteristics on the predictive accuracy of recommender systems (2020)
  20. Connamacher, Harold; Pancha, Nikil; Liu, Rui; Ray, Soumya: \textscRankboost(+): an improvement to \textscRankboost (2020)

1 2 3 next