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2020 – today
- 2024
[j3]Taman Narayan, Serena Lutong Wang, Kevin Robert Canini, Maya R. Gupta:
Expected Pinball Loss For Quantile Regression And Inverse CDF Estimation. Trans. Mach. Learn. Res. 2024 (2024)- 2021
- [j2]Nathan Zhang, Kevin Robert Canini, Sean Silva, Maya R. Gupta:
Fast Linear Interpolation. ACM J. Emerg. Technol. Comput. Syst. 17(2): 20:1-20:15 (2021) - [i3]Taman Narayan, Serena Lutong Wang, Kevin Robert Canini, Maya R. Gupta:
Regularization Strategies for Quantile Regression. CoRR abs/2102.05135 (2021)
2010 – 2019
- 2018
- [c12]Maya R. Gupta, Dara Bahri, Andrew Cotter, Kevin Robert Canini:
Diminishing Returns Shape Constraints for Interpretability and Regularization. NeurIPS 2018: 6835-6845 - 2017
- [c11]Seungil You, David Ding, Kevin Robert Canini, Jan Pfeifer, Maya R. Gupta:
Deep Lattice Networks and Partial Monotonic Functions. NIPS 2017: 2981-2989 - [i2]Seungil You, David Ding, Kevin Robert Canini, Jan Pfeifer, Maya R. Gupta:
Deep Lattice Networks and Partial Monotonic Functions. CoRR abs/1709.06680 (2017) - 2016
- [j1]Maya R. Gupta, Andrew Cotter, Jan Pfeifer, Konstantin Voevodski
, Kevin Robert Canini, Alexander Mangylov, Wojtek Moczydlowski, Alexander Van Esbroeck:
Monotonic Calibrated Interpolated Look-Up Tables. J. Mach. Learn. Res. 17: 109:1-109:47 (2016) - [c10]Mahdi Milani Fard, Kevin Robert Canini, Andrew Cotter, Jan Pfeifer, Maya R. Gupta:
Fast and Flexible Monotonic Functions with Ensembles of Lattices. NIPS 2016: 2919-2927 - [c9]Mahdi Milani Fard, Quentin Cormier, Kevin Robert Canini, Maya R. Gupta:
Launch and Iterate: Reducing Prediction Churn. NIPS 2016: 3171-3179 - 2015
- [i1]Maya R. Gupta, Andrew Cotter, Jan Pfeifer, Konstantin Voevodski, Kevin Robert Canini, Alexander Mangylov, Wojtek Moczydlowski:
Monotonic Calibrated Interpolated Look-Up Tables. CoRR abs/1505.06378 (2015) - 2013
- [c8]Indraneel Mukherjee, Kevin Robert Canini, Rafael M. Frongillo, Yoram Singer:
Parallel Boosting with Momentum. ECML/PKDD (3) 2013: 17-32 - 2011
- [b1]Kevin Robert Canini:
Nonparametric Hierarchical Bayesian Models of Categorization. University of California, Berkeley, USA, 2011 - [c7]Kevin Robert Canini, Thomas L. Griffiths:
A Nonparametric Bayesian Model of Multi-Level Category Learning. AAAI 2011: 307-312 - [c6]Joseph L. Austerweil, Thomas L. Griffiths, Todd M. Gureckis, Robert L. Goldstone, Kevin Robert Canini, Matt Jones:
Grow your own representations: Computational constructivism. CogSci 2011 - [c5]Daphna Buchsbaum, Kevin Robert Canini, Thomas L. Griffiths:
Segmenting and Recognizing Human Action using Low-level Video Features. CogSci 2011 - [c4]Kevin Robert Canini, Thomas L. Griffiths, Wolf Vanpaemel, Michael L. Kalish:
Discovering Inductive Biases in Categorization through Iterated Learning. CogSci 2011 - [c3]Kevin Robert Canini, Bongwon Suh, Peter Pirolli:
Finding Credible Information Sources in Social Networks Based on Content and Social Structure. SocialCom/PASSAT 2011: 1-8 - 2010
- [c2]Kevin Robert Canini, Mikhail M. Shashkov, Thomas L. Griffiths:
Modeling Transfer Learning in Human Categorization with the Hierarchical Dirichlet Process. ICML 2010: 151-158
2000 – 2009
- 2009
- [c1]Kevin Robert Canini, Lei Shi, Thomas L. Griffiths:
Online Inference of Topics with Latent Dirichlet Allocation. AISTATS 2009: 65-72
Coauthor Index
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