Nicolas Mayoraz

12 papers A* 1A 3Journal 6Unranked 2

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Efficient Hyperparameter Search for Non-Stationary Model Training. CoRR Berivan Isik, Matthew Fahrbach, Dima Kuzmin, Nicolas Mayoraz, Emil Praun, Steffen Rendle, Raghavendra Vasudeva
2025	J	`jnl`	Successive Halving with Learning Curve Prediction via Latent Kronecker Gaussian Processes. CoRR Jihao Andreas Lin, Nicolas Mayoraz, Steffen Rendle, Dima Kuzmin, Emil Praun, Berivan Isik
2024	A	`conf`	Private Learning with Public Features. AISTATS Walid Krichene, Nicolas Mayoraz, Steffen Rendle, Shuang Song, Abhradeep Thakurta, Li Zhang
2024	—	`conf`	Training Differentially Private Ad Prediction Models With Semi-Sensitive Features. AdKDD@KDD Lynn Chua, Qiliang Cui, Badih Ghazi, Charlie Harrison, Pritish Kamath, Walid Krichene, Ravi Kumar, Pasin Manurangsi, Nicolas Mayoraz, Hema Venkata Krishna Giri Narra, Steffen Rendle, Amer Sinha, Avinash V. Varadarajan, Chiyuan Zhang
2023	J	`jnl`	Private Learning with Public Features. CoRR Walid Krichene, Nicolas Mayoraz, Steffen Rendle, Shuang Song, Abhradeep Thakurta, Li Zhang
2021	J	`jnl`	RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems. CoRR Martin Mladenov, Chih-Wei Hsu, Vihan Jain, Eugene Ie, Christopher Colby, Nicolas Mayoraz, Hubert Pham, Dustin Tran, Ivan Vendrov, Craig Boutilier
2020	A	`conf`	Demonstrating Principled Uncertainty Modeling for Recommender Ecosystems with RecSim NG. RecSys Martin Mladenov, Chih-Wei Hsu, Vihan Jain, Eugene Ie, Christopher Colby, Nicolas Mayoraz, Hubert Pham, Dustin Tran, Ivan Vendrov, Craig Boutilier
2020	A*	`conf`	Rankmax: An Adaptive Projection Alternative to the Softmax Function. NeurIPS Weiwei Kong, Walid Krichene, Nicolas Mayoraz, Steffen Rendle, Li Zhang
2019	—	`conf`	Efficient Training on Very Large Corpora via Gramian Estimation. ICLR (Poster) Walid Krichene, Nicolas Mayoraz, Steffen Rendle, Li Zhang, Xinyang Yi, Lichan Hong, Ed H. Chi, John R. Anderson
2019	J	`jnl`	Scaling Up Collaborative Filtering Data Sets through Randomized Fractal Expansions. CoRR Francois Belletti, Karthik Lakshmanan, Walid Krichene, Nicolas Mayoraz, Yi-Fan Chen, John R. Anderson, Taylor Robie, Tayo Oguntebi, Dan Shirron, Amit Bleiwess
2018	J	`jnl`	Efficient Training on Very Large Corpora via Gramian Estimation. CoRR Walid Krichene, Nicolas Mayoraz, Steffen Rendle, Li Zhang, Xinyang Yi, Lichan Hong, Ed H. Chi, John R. Anderson
2017	A	`conf`	Folding: Why Good Models Sometimes Make Spurious Recommendations. RecSys Doris Xin, Nicolas Mayoraz, Hubert Pham, Karthik Lakshmanan, John R. Anderson