Nicolas Mayoraz

According to our database, Nicolas Mayoraz authored at least 7 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Private Learning with Public Features.

Walid Krichene, Nicolas Mayoraz, Steffen Rendle, Shuang Song, Abhradeep Thakurta, Li Zhang

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2021

RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems.

Martin Mladenov, Chih-Wei Hsu, Vihan Jain, Eugene Ie, Christopher Colby, Nicolas Mayoraz, Hubert Pham, Dustin Tran, Ivan Vendrov, Craig Boutilier

CoRR, 2021

2020

Demonstrating Principled Uncertainty Modeling for Recommender Ecosystems with RecSim NG.

Martin Mladenov, Chih-Wei Hsu, Vihan Jain, Eugene Ie, Christopher Colby, Nicolas Mayoraz, Hubert Pham, Dustin Tran, Ivan Vendrov, Craig Boutilier

Proceedings of the RecSys 2020: Fourteenth ACM Conference on Recommender Systems, 2020

Rankmax: An Adaptive Projection Alternative to the Softmax Function.

Weiwei Kong, Walid Krichene, Nicolas Mayoraz, Steffen Rendle, Li Zhang

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

Scaling Up Collaborative Filtering Data Sets through Randomized Fractal Expansions.

Francois Belletti, Karthik Lakshmanan, Walid Krichene, Nicolas Mayoraz, Yi-Fan Chen, John R. Anderson, Taylor Robie, Tayo Oguntebi, Dan Shirron, Amit Bleiwess

CoRR, 2019

Efficient Training on Very Large Corpora via Gramian Estimation.

Walid Krichene, Nicolas Mayoraz, Steffen Rendle, Li Zhang, Xinyang Yi, Lichan Hong, Ed H. Chi, John R. Anderson

Proceedings of the 7th International Conference on Learning Representations, 2019

2017

Folding: Why Good Models Sometimes Make Spurious Recommendations.

Doris Xin, Nicolas Mayoraz, Hubert Pham, Karthik Lakshmanan, John R. Anderson

Proceedings of the Eleventh ACM Conference on Recommender Systems, 2017