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ORCIDRichard Nock
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- affiliation: CSIRO, Data61, Sydney, Australia
- affiliation: University of the French West Indies, CEREGMIA, Martinique, France
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2020 – today
- 2023
[i63]Kevin Lam, Christian J. Walder, Spiridon Penev, Richard Nock:
LegendreTron: Uprising Proper Multiclass Loss Learning. CoRR abs/2301.11695 (2023)- [i62]Tyler Sypherd, Nathan Stromberg, Richard Nock, Visar Berisha, Lalitha Sankar:
Smoothly Giving up: Robustness for Simple Models. CoRR abs/2302.09114 (2023) - 2022
- [c120]Yao Ni, Piotr Koniusz, Richard I. Hartley, Richard Nock:
Manifold Learning Benefits GANs. CVPR 2022: 11255-11264 - [c119]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 - [c118]Richard Nock, Mathieu Guillame-Bert:
Generative Trees: Adversarial and Copycat. ICML 2022: 16906-16951 - [c117]Tyler Sypherd, Richard Nock, Lalitha Sankar:
Being Properly Improper. ICML 2022: 20891-20932 - [c116]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 - [i61]Richard Nock, Mathieu Guillame-Bert:
Generative Trees: Adversarial and Copycat. CoRR abs/2201.11205 (2022) - [i60]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) - [i59]Yishay Mansour, Richard Nock, Robert C. Williamson:
What killed the Convex Booster ? CoRR abs/2205.09628 (2022) - [i58]Ehsan Amid, Richard Nock, Manfred K. Warmuth:
Clustering above Exponential Families with Tempered Exponential Measures. CoRR abs/2211.02765 (2022) - 2021
- [j42]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) - [c115]Frank Nielsen, Richard Nock:
Computing Statistical Divergences with Sigma Points. GSI 2021: 677-684 - [c114]Zac Cranko, Zhan Shi, Xinhua Zhang, Richard Nock, Simon Kornblith:
Generalised Lipschitz Regularisation Equals Distributional Robustness. ICML 2021: 2178-2188 - [c113]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 - [i57]Richard Nock, Tyler Sypherd, Lalitha Sankar:
Being Properly Improper. CoRR abs/2106.09920 (2021) - [i56]Yao Ni, Piotr Koniusz, Richard I. Hartley, Richard Nock:
Manifold Learning Benefits GANs. CoRR abs/2112.12618 (2021) - 2020
- [j41]Huijun Wu, Chen Wang, Richard Nock, Wei Wang, Jie Yin, Kai Lu, Liming Zhu:
SMINT: Toward Interpretable and Robust Model Sharing for Deep Neural Networks. ACM Trans. Web 14(3): 11:1-11:28 (2020) - [c112]Hisham Husain, Borja Balle, Zac Cranko, Richard Nock:
Local Differential Privacy for Sampling. AISTATS 2020: 3404-3413 - [c111]Christian Simon, Piotr Koniusz, Richard Nock, Mehrtash Harandi:
Adaptive Subspaces for Few-Shot Learning. CVPR 2020: 4135-4144 - [c110]Christian Simon, Piotr Koniusz, Richard Nock, Mehrtash Harandi:
On Modulating the Gradient for Meta-learning. ECCV (8) 2020: 556-572 - [c109]Richard Nock, Aditya Krishna Menon:
Supervised learning: no loss no cry. ICML 2020: 7370-7380 - [c108]Christian J. Walder, Richard Nock:
All your loss are belong to Bayes. NeurIPS 2020 - [i55]Richard Nock, Wilko Henecka:
Boosted and Differentially Private Ensembles of Decision Trees. CoRR abs/2001.09384 (2020) - [i54]Richard Nock, Aditya Krishna Menon:
Supervised Learning: No Loss No Cry. CoRR abs/2002.03555 (2020) - [i53]Zac Cranko, Zhan Shi, Xinhua Zhang, Richard Nock, Simon Kornblith:
Generalised Lipschitz Regularisation Equals Distributional Robustness. CoRR abs/2002.04197 (2020) - [i52]Frank Nielsen, Richard Nock:
Cumulant-free closed-form formulas for some common (dis)similarities between densities of an exponential family. CoRR abs/2003.02469 (2020) - [i51]Christian J. Walder, Richard Nock:
All your loss are belong to Bayes. CoRR abs/2006.04633 (2020) - [i50]Alexander Soen, Hisham Husain, Richard Nock:
Data Preprocessing to Mitigate Bias with Boosted Fair Mollifiers. CoRR abs/2012.00188 (2020)
2010 – 2019
- 2019
- [c107]Samitha Herath, Mehrtash Harandi, Basura Fernando, Richard Nock:
Min-Max Statistical Alignment for Transfer Learning. CVPR 2019: 9288-9297 - [c106]Frank Nielsen, Richard Nock:
The Bregman Chord Divergence. GSI 2019: 299-308 - [c105]Soumava Kumar Roy, Mehrtash Harandi, Richard Nock, Richard I. Hartley:
Siamese Networks: The Tale of Two Manifolds. ICCV 2019: 3046-3055 - [c104]Zac Cranko, Aditya Krishna Menon, Richard Nock, Cheng Soon Ong, Zhan Shi, Christian J. Walder:
Monge blunts Bayes: Hardness Results for Adversarial Training. ICML 2019: 1406-1415 - [c103]Zac Cranko, Richard Nock:
Boosted Density Estimation Remastered. ICML 2019: 1416-1425 - [c102]Richard Nock, Robert C. Williamson:
Lossless or Quantized Boosting with Integer Arithmetic. ICML 2019: 4829-4838 - [c101]Hisham Husain, Richard Nock, Robert C. Williamson:
A Primal-Dual link between GANs and Autoencoders. NeurIPS 2019: 413-422 - [c100]Amir Dezfouli, Hassan Ashtiani, Omar Ghattas, Richard Nock, Peter Dayan, Cheng Soon Ong:
Disentangled behavioural representations. NeurIPS 2019: 2251-2260 - [c99]Daniel Filonik, Tian Feng, Ke Sun, Richard Nock, Alex Collins, Tomasz Bednarz:
Non-Euclidean Embeddings for Graph Analytics and Visualisation. SIGGRAPH Asia Posters 2019: 47:1-47:2 - [i49]Christian J. Walder, Richard Nock, Cheng Soon Ong, Masashi Sugiyama:
New Tricks for Estimating Gradients of Expectations. CoRR abs/1901.11311 (2019) - [i48]Hisham Husain, Richard Nock, Robert C. Williamson:
Adversarial Networks and Autoencoders: The Primal-Dual Relationship and Generalization Bounds. CoRR abs/1902.00985 (2019) - [i47]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, 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, Mariana Raykova, Hang Qi, Daniel Ramage, Ramesh Raskar, 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. CoRR abs/1912.04977 (2019) - 2018
- [c98]Frank Nielsen, Richard Nock:
On the Geometry of Mixtures of Prescribed Distributions. ICASSP 2018: 2861-2865 - [c97]Amir Dezfouli, Edwin V. Bonilla, Richard Nock:
Variational Network Inference: Strong and Stable with Concrete Support. ICML 2018: 1212-1221 - [c96]Marta Avalos, Richard Nock, Cheng Soon Ong, Julien Rouar, Ke Sun:
Representation Learning of Compositional Data. NeurIPS 2018: 6680-6690 - [c95]Kelvin Hsu, Richard Nock, Fabio Ramos:
Hyperparameter Learning for Conditional Kernel Mean Embeddings with Rademacher Complexity Bounds. ECML/PKDD (2) 2018: 227-242 - [i46]Richard Nock, Stephen Hardy, Wilko Henecka, Hamish Ivey-Law, Giorgio Patrini, Guillaume Smith, Brian Thorne:
Entity Resolution and Federated Learning get a Federated Resolution. CoRR abs/1803.04035 (2018) - [i45]Zac Cranko, Richard Nock:
Boosted Density Estimation Remastered. CoRR abs/1803.08178 (2018) - [i44]Zac Cranko, Aditya Krishna Menon, Richard Nock, Cheng Soon Ong, Zhan Shi, Christian J. Walder:
Monge beats Bayes: Hardness Results for Adversarial Training. CoRR abs/1806.02977 (2018) - [i43]Hisham Husain, Zac Cranko, Richard Nock:
Integral Privacy for Density Estimation with Approximation Guarantees. CoRR abs/1806.04819 (2018) - [i42]Leif W. Hanlen, Richard Nock, Hanna Suominen, Neil Bacon:
Private Text Classification. CoRR abs/1806.06998 (2018) - [i41]Qiongkai Xu, Juyan Zhang, Lizhen Qu, Lexing Xie, Richard Nock:
D-PAGE: Diverse Paraphrase Generation. CoRR abs/1808.04364 (2018) - [i40]Kelvin Hsu, Richard Nock, Fabio Ramos:
Hyperparameter Learning for Conditional Mean Embeddings with Rademacher Complexity Bounds. CoRR abs/1809.00175 (2018) - [i39]Zac Cranko, Simon Kornblith, Zhan Shi, Richard Nock:
Lipschitz Networks and Distributional Robustness. CoRR abs/1809.01129 (2018) - [i38]Frank Nielsen, Richard Nock:
The Bregman chord divergence. CoRR abs/1810.09113 (2018) - 2017
- [j40]Frank Nielsen, Richard Nock:
MaxEnt Upper Bounds for the Differential Entropy of Univariate Continuous Distributions. IEEE Signal Process. Lett. 24(4): 402-406 (2017) - [j39]Frank Nielsen, Richard Nock:
Generalizing Skew Jensen Divergences and Bregman Divergences With Comparative Convexity. IEEE Signal Process. Lett. 24(8): 1123-1127 (2017) - [c94]Boris Muzellec, Richard Nock, Giorgio Patrini, Frank Nielsen:
Tsallis Regularized Optimal Transport and Ecological Inference. AAAI 2017: 2387-2393 - [c93]Giorgio Patrini, Alessandro Rozza, Aditya Krishna Menon, Richard Nock, Lizhen Qu:
Making Deep Neural Networks Robust to Label Noise: A Loss Correction Approach. CVPR 2017: 2233-2241 - [c92]Frank Nielsen, Richard Nock:
Bregman Divergences from Comparative Convexity. GSI 2017: 639-647 - [c91]Richard Nock, Zac Cranko, Aditya Krishna Menon, Lizhen Qu, Robert C. Williamson:
f-GANs in an Information Geometric Nutshell. NIPS 2017: 456-464 - [i37]Frank Nielsen, Richard Nock:
Generalizing Jensen and Bregman divergences with comparative convexity and the statistical Bhattacharyya distances with comparable means. CoRR abs/1702.04877 (2017) - [i36]Amir Dezfouli, Edwin V. Bonilla, Richard Nock:
Semi-parametric Network Structure Discovery Models. CoRR abs/1702.08530 (2017) - [i35]Richard Nock, Frank Nielsen:
Distribution-free Evolvability of Vector Spaces: All it takes is a Generating Set. CoRR abs/1704.02708 (2017) - [i34]Richard Nock, Zac Cranko, Aditya Krishna Menon, Lizhen Qu, Robert C. Williamson:
f-GANs in an Information Geometric Nutshell. CoRR abs/1707.04385 (2017) - [i33]Frank Nielsen, Richard Nock:
On w-mixtures: Finite convex combinations of prescribed component distributions. CoRR abs/1708.00568 (2017) - [i32]Stephen Hardy, Wilko Henecka, Hamish Ivey-Law, Richard Nock, Giorgio Patrini, Guillaume Smith, Brian Thorne:
Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption. CoRR abs/1711.10677 (2017) - 2016
- [j38]Richard Nock, Frank Nielsen, Shun-ichi Amari:
On Conformal Divergences and Their Population Minimizers. IEEE Trans. Inf. Theory 62(1): 527-538 (2016) - [c90]Frank Nielsen, Boris Muzellec, Richard Nock:
Classification with mixtures of curved mahalanobis metrics. ICIP 2016: 241-245 - [c89]Richard Nock, Raphaël Canyasse, Roksana Boreli, Frank Nielsen:
k-variates++: more pluses in the k-means++. ICML 2016: 145-154 - [c88]Giorgio Patrini, Frank Nielsen, Richard Nock, Marcello Carioni:
Loss factorization, weakly supervised learning and label noise robustness. ICML 2016: 708-717 - [c87]Giorgio Patrini, Richard Nock, Stephen Hardy, Tibério S. Caetano:
Fast Learning from Distributed Datasets without Entity Matching. IJCAI 2016: 1909-1917 - [c86]Richard Nock, Aditya Krishna Menon, Cheng Soon Ong:
A scaled Bregman theorem with applications. NIPS 2016: 19-27 - [c85]Richard Nock:
On Regularizing Rademacher Observation Losses. NIPS 2016: 37-45 - [c84]Frank Nielsen, Richard Nock:
Patch Matching with Polynomial Exponential Families and Projective Divergences. SISAP 2016: 109-116 - [i31]Richard Nock, Raphaël Canyasse, Roksana Boreli, Frank Nielsen:
k-variates++: more pluses in the k-means++. CoRR abs/1602.01198 (2016) - [i30]Giorgio Patrini, Frank Nielsen, Richard Nock, Marcello Carioni:
Loss factorization, weakly supervised learning and label noise robustness. CoRR abs/1602.02450 (2016) - [i29]Giorgio Patrini, Richard Nock, Stephen Hardy, Tibério S. Caetano:
Fast Learning from Distributed Datasets without Entity Matching. CoRR abs/1603.04002 (2016) - [i28]Frank Nielsen, Richard Nock:
Fast $(1+ε)$-approximation of the Löwner extremal matrices of high-dimensional symmetric matrices. CoRR abs/1604.01592 (2016) - [i27]Richard Nock, Giorgio Patrini, Finnian Lattimore, Tibério S. Caetano:
The Crossover Process: Learnability meets Protection from Inference Attacks. CoRR abs/1606.04160 (2016) - [i26]Richard Nock, Aditya Krishna Menon, Cheng Soon Ong:
A scaled Bregman theorem with applications. CoRR abs/1607.00360 (2016) - [i25]Giorgio Patrini, Alessandro Rozza, Aditya Krishna Menon, Richard Nock, Lizhen Qu:
Making Neural Networks Robust to Label Noise: a Loss Correction Approach. CoRR abs/1609.03683 (2016) - [i24]Boris Muzellec, Richard Nock, Giorgio Patrini, Frank Nielsen:
Tsallis Regularized Optimal Transport and Ecological Inference. CoRR abs/1609.04495 (2016) - [i23]Frank Nielsen, Boris Muzellec, Richard Nock:
Large Margin Nearest Neighbor Classification using Curved Mahalanobis Distances. CoRR abs/1609.07082 (2016) - [i22]Frank Nielsen, Richard Nock:
A series of maximum entropy upper bounds of the differential entropy. CoRR abs/1612.02954 (2016) - 2015
- [j37]Meriam Bayoudh, Emmanuel Roux, Gilles Richard, Richard Nock:
Structural knowledge learning from maps for supervised land cover/use classification: Application to the monitoring of land cover/use maps in French Guiana. Comput. Geosci. 76: 31-40 (2015) - [j36]Richard Nock, Wafa Bel Haj Ali, Roberto D'Ambrosio, Frank Nielsen, Michel Barlaud:
Gentle Nearest Neighbors Boosting over Proper Scoring Rules. IEEE Trans. Pattern Anal. Mach. Intell. 37(1): 80-93 (2015) - [c83]Frank Nielsen, Richard Nock:
Total Jensen divergences: Definition, properties and clustering. ICASSP 2015: 2016-2020 - [c82]Richard Nock, Giorgio Patrini, Arik Friedman:
Rademacher Observations, Private Data, and Boosting. ICML 2015: 948-956 - [i21]Richard Nock, Giorgio Patrini, Arik Friedman:
Rademacher Observations, Private Data, and Boosting. CoRR abs/1502.02322 (2015) - [i20]Richard Nock:
Learning Games and Rademacher Observations Losses. CoRR abs/1512.05244 (2015) - 2014
- [j35]Frank Nielsen, Richard Nock, Shun-ichi Amari:
On Clustering Histograms with k-Means by Using Mixed α-Divergences. Entropy 16(6): 3273-3301 (2014) - [j34]Frank Nielsen, Richard Nock:
On the Chi Square and Higher-Order Chi Distances for Approximating $f$ -Divergences. IEEE Signal Process. Lett. 21(1): 10-13 (2014) - [j33]Frank Nielsen, Richard Nock:
Optimal Interval Clustering: Application to Bregman Clustering and Statistical Mixture Learning. IEEE Signal Process. Lett. 21(10): 1289-1292 (2014) - [c81]Frank Nielsen, Richard Nock:
Visualizing hyperbolic Voronoi diagrams. SoCG 2014: 90 - [c80]Wafa Bel Haj Ali, Richard Nock, Michel Barlaud:
Boosting Stochastic Newton with Entropy Constraint for Large-Scale Image Classification. ICPR 2014: 232-237 - [c79]Giorgio Patrini, Richard Nock, Tibério S. Caetano, Paul Rivera:
(Almost) No Label No Cry. NIPS 2014: 190-198 - [i19]Frank Nielsen, Richard Nock:
A note on the optimal scalar Bregman k-means clustering with an application to learning best statistical mixtures. CoRR abs/1403.2485 (2014) - [i18]Frank Nielsen, Richard Nock:
Further heuristics for $k$-means: The merge-and-split heuristic and the $(k, l)$-means. CoRR abs/1406.6314 (2014) - [i17]Frank Nielsen, Richard Nock:
Further results on the hyperbolic Voronoi diagrams. CoRR abs/1410.1036 (2014) - 2013
- [c78]Frank Nielsen, Richard Nock:
Consensus Region Merging for Image Segmentation. ACPR 2013: 325-329 - [c77]Erol Elisabeth, Richard Nock, Fred Celimene:
Demonstrator of a Tourist Recommendation System. BDA 2013: 171-175 - [c76]Richard Nock, Frank Nielsen, Eric Briys:
Non-linear book manifolds: learning from associations the dynamic geometry of digital libraries. JCDL 2013: 313-322 - [c75]Richard Nock, Frank Nielsen:
Information-geometric lenses for multiple foci+contexts interfaces. SIGGRAPH ASIA Technical Briefs 2013: 18:1-18:4 - [p1]Paolo Piro, Richard Nock, Wafa Bel Haj Ali:
Boosting k-Nearest Neighbors Classification. Advanced Topics in Computer Vision 2013: 341-375 - [i16]Marc Sebban, Richard Nock:
Combining Feature and Prototype Pruning by Uncertainty Minimization. CoRR abs/1301.3891 (2013) - [i15]Frank Nielsen, Richard Nock:
On the Chi square and higher-order Chi distances for approximating f-divergences. CoRR abs/1309.3029 (2013) - [i14]Frank Nielsen, Richard Nock:
Total Jensen divergences: Definition, Properties and k-Means++ Clustering. CoRR abs/1309.7109 (2013) - [i13]Richard Nock, Frank Nielsen, Shun-ichi Amari:
On conformal divergences and their population minimizers. CoRR abs/1311.5125 (2013) - 2012
- [j32]Richard Nock, Paolo Piro, Frank Nielsen, Wafa Bel Haj Ali, Michel Barlaud:
Boosting k-NN for Categorization of Natural Scenes. Int. J. Comput. Vis. 100(3): 294-314 (2012) - [j31]Paolo Piro, Richard Nock, Frank Nielsen, Michel Barlaud:
Leveraging k-NN for generic classification boosting. Neurocomputing 80: 3-9 (2012) - [c74]Wafa Bel Haj Ali, Dario Giampaglia, Michel Barlaud, Paolo Piro, Richard Nock, Thierry Pourcher:
Classification of biological cells using bio-inspired descriptors. ICPR 2012: 3353-3357 - [c73]Roberto D'Ambrosio, Wafa Bel Haj Ali, Richard Nock, Paolo Soda, Frank Nielsen, Michel Barlaud:
Biomedical Images Classification by Universal Nearest Neighbours Classifier Using Posterior Probability. MLMI 2012: 119-127 - [c72]Roberto D'Ambrosio, Richard Nock, Wafa Bel Haj Ali, Frank Nielsen, Michel Barlaud:
Boosting Nearest Neighbors for the Efficient Estimation of Posteriors. ECML/PKDD (1) 2012: 314-329 - [i12]Frank Nielsen, Richard Nock:
The hyperbolic Voronoi diagram in arbitrary dimension. CoRR abs/1210.8234 (2012) - 2011
- [j30]Brice Magdalou, Richard Nock:
Income distributions and decomposable divergence measures. J. Econ. Theory 146(6): 2440-2454 (2011) - [j29]Frank Nielsen, Richard Nock:
Skew Jensen-Bregman Voronoi Diagrams. Trans. Comput. Sci. 14: 102-128 (2011) - [c71]Richard Nock, Brice Magdalou, Eric Briys, Frank Nielsen:
On tracking portfolios with certainty equivalents on a generalization of Markowitz model: the Fool, the Wise and the Adaptive. ICML 2011: 73-80 - [i11]Frank Nielsen, Richard Nock:
On Rényi and Tsallis entropies and divergences for exponential families. CoRR abs/1105.3259 (2011) - [i10]Richard Nock:
Inducing Interpretable Voting Classifiers without Trading Accuracy for Simplicity: Theoretical Results, Approximation Algorithms. CoRR abs/1106.1818 (2011) - [i9]Frank Nielsen, Richard Nock:
A closed-form expression for the Sharma-Mittal entropy of exponential families. CoRR abs/1112.4221 (2011) - 2010
- [j28]Jean-Daniel Boissonnat, Frank Nielsen, Richard Nock:
Bregman Voronoi Diagrams. Discret. Comput. Geom. 44(2): 281-307 (2010) - [c70]