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Publication search results
found 247 matches
- 2024
- Ehsan Amid, Frank Nielsen, Richard Nock, Manfred K. Warmuth:
Optimal Transport with Tempered Exponential Measures. AAAI 2024: 10838-10846 - Richard Nock, Ehsan Amid, Frank Nielsen, Alexander Soen, Manfred K. Warmuth:
Tempered Calculus for ML: Application to Hyperbolic Model Embedding. CoRR abs/2402.04163 (2024) - Mathieu Guillame-Bert, Richard Nock:
Boosting gets full Attention for Relational Learning. CoRR abs/2402.14926 (2024) - Nathan Stromberg, Rohan Ayyagari, Sanmi Koyejo, Richard Nock, Lalitha Sankar:
Label Noise Robustness for Domain-Agnostic Fair Corrections via Nearest Neighbors Label Spreading. CoRR abs/2406.09561 (2024) - Richard Nock, Yishay Mansour:
How to Boost Any Loss Function. CoRR abs/2407.02279 (2024) - 2023
- Ehsan Amid, Richard Nock, Manfred K. Warmuth:
Clustering above Exponential Families with Tempered Exponential Measures. AISTATS 2023: 2994-3017 - Tyler Sypherd, Nathaniel Stromberg, Richard Nock, Visar Berisha, Lalitha Sankar:
Smoothly Giving up: Robustness for Simple Models. AISTATS 2023: 5376-5410 - Kevin H. Lam, Christian J. Walder, Spiridon I. Penev, Richard Nock:
LegendreTron: Uprising Proper Multiclass Loss Learning. ICML 2023: 18454-18470 - Yishay Mansour, Richard Nock, Robert C. Williamson:
Random Classification Noise does not defeat All Convex Potential Boosters Irrespective of Model Choice. ICML 2023: 23706-23742 - Alexander Soen, Hisham Husain, Richard Nock:
Fair Densities via Boosting the Sufficient Statistics of Exponential Families. ICML 2023: 32105-32144 - Richard Nock, Ehsan Amid, Manfred K. Warmuth:
Boosting with Tempered Exponential Measures. NeurIPS 2023 - Kevin H. Lam, Christian J. Walder, Spiridon I. Penev, Richard Nock:
LegendreTron: Uprising Proper Multiclass Loss Learning. CoRR abs/2301.11695 (2023) - Tyler Sypherd, Nathan Stromberg, Richard Nock, Visar Berisha, Lalitha Sankar:
Smoothly Giving up: Robustness for Simple Models. CoRR abs/2302.09114 (2023) - Richard Nock, Ehsan Amid, Manfred K. Warmuth:
Boosting with Tempered Exponential Measures. CoRR abs/2306.05487 (2023) - Richard Nock, Mathieu Guillame-Bert:
Generative Forests. CoRR abs/2308.03648 (2023) - Ehsan Amid, Frank Nielsen, Richard Nock, Manfred K. Warmuth:
Optimal Transport with Tempered Exponential Measures. CoRR abs/2309.04015 (2023) - Ehsan Amid, Frank Nielsen, Richard Nock, Manfred K. Warmuth:
The Tempered Hilbert Simplex Distance and Its Application To Non-linear Embeddings of TEMs. CoRR abs/2311.13459 (2023) - 2022
- Yao Ni, Piotr Koniusz, Richard I. Hartley, Richard Nock:
Manifold Learning Benefits GANs. CVPR 2022: 11255-11264 - Moein Khajehnejad, Forough Habibollahi, Richard Nock, Ehsan Arabzadeh, Peter Dayan, Amir Dezfouli:
Neural Network Poisson Models for Behavioural and Neural Spike Train Data. ICML 2022: 10974-10996 - Richard Nock, Mathieu Guillame-Bert:
Generative Trees: Adversarial and Copycat. ICML 2022: 16906-16951 - Tyler Sypherd, Richard Nock, Lalitha Sankar:
Being Properly Improper. ICML 2022: 20891-20932 - Alexander Soen, Ibrahim M. Alabdulmohsin, Sanmi Koyejo, Yishay Mansour, Nyalleng Moorosi, Richard Nock, Ke Sun, Lexing Xie:
Fair Wrapping for Black-box Predictions. NeurIPS 2022 - Richard Nock, Mathieu Guillame-Bert:
Generative Trees: Adversarial and Copycat. CoRR abs/2201.11205 (2022) - Alexander Soen, Ibrahim Alabdulmohsin, Sanmi Koyejo, Yishay Mansour, Nyalleng Moorosi, Richard Nock, Ke Sun, Lexing Xie:
Fair Wrapping for Black-box Predictions. CoRR abs/2201.12947 (2022) - Yishay Mansour, Richard Nock, Robert C. Williamson:
What killed the Convex Booster ? CoRR abs/2205.09628 (2022) - Ehsan Amid, Richard Nock, Manfred K. Warmuth:
Clustering above Exponential Families with Tempered Exponential Measures. CoRR abs/2211.02765 (2022) - 2021
- Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista A. Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D'Oliveira, Hubert Eichner, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaïd Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Hang Qi, Daniel Ramage, Ramesh Raskar, Mariana Raykova, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, Sen Zhao:
Advances and Open Problems in Federated Learning. Found. Trends Mach. Learn. 14(1-2): 1-210 (2021) - Frank Nielsen, Richard Nock:
Computing Statistical Divergences with Sigma Points. GSI 2021: 677-684 - Zac Cranko, Zhan Shi, Xinhua Zhang, Richard Nock, Simon Kornblith:
Generalised Lipschitz Regularisation Equals Distributional Robustness. ICML 2021: 2178-2188 - Richard Nock, Stephen Hardy, Wilko Henecka, Hamish Ivey-Law, Jakub Nabaglo, Giorgio Patrini, Guillaume Smith, Brian Thorne:
The Impact of Record Linkage on Learning from Feature Partitioned Data. ICML 2021: 8216-8226
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