#regularization — Public Fediverse posts

Awesome work! Just came across Motonobu Kanagawa & colleagues‘ monograph on #GaussianProcesses and reproducing kernel #HilbertSpace methods. Discusses the deep mathematical equivalence between GP inference and kernel-based #regularization in #RKHSs. Definitely one for the reading list!

📄 https://arxiv.org/abs/2506.17366

#Mathematics #DataScience #Statistics

Awesome work! Just came across Motonobu Kanagawa & colleagues‘ monograph on #GaussianProcesses and reproducing kernel #HilbertSpace methods. Discusses the deep mathematical equivalence between GP inference and kernel-based #regularization in #RKHSs. Definitely one for the reading list!

📄 https://arxiv.org/abs/2506.17366

#Mathematics #DataScience #Statistics

SLAY-ASR, или как я перестал волноваться и полюбил тренировать модели

Как добавить аудио-модальность в LLMку максимально экономно? Рассказываю про серию попыток добиться совместимости эмбеддингов разной природы Погрузиться

https://habr.com/ru/articles/1009614/

#representation_learning #multimodality #multimodal_llm #machine_learning #audiomodality #regularization #contrastive_learning #whisper #gemma3

«Informative session on the Spanish extraordinary regularization» (…in Spanish) 🗓️ Thursday 26 #February2026 🕔 17:00–18:30 CET 🇪🇸 #Spain 📍 N/A (online) ✍️ Inscription. 🎯 For SWers! 👠 #Event #SexWork #Online #Regularization #Immigration #OTRAS

«Informative session on the Spanish extraordinary regularization» (…in Spanish) 🗓️ Thursday 26 #February2026 🕔 17:00–18:30 CET 🇪🇸 #Spain 📍 N/A (online) ✍️ Inscription. 🎯 For SWers! 👠 #Event #SexWork #Online #Regularization #Immigration #OTRAS

'On the Approximation of Kernel functions', by Paul Dommel, Alois Pichler.

http://jmlr.org/papers/v26/24-0270.html

#kernels #kernel #regularization

'On the Approximation of Kernel functions', by Paul Dommel, Alois Pichler.

http://jmlr.org/papers/v26/24-0270.html

#kernels #kernel #regularization

'Deep Out-of-Distribution Uncertainty Quantification via Weight Entropy Maximization', by Antoine de Mathelin, François Deheeger, Mathilde Mougeot, Nicolas Vayatis.

http://jmlr.org/papers/v26/23-1359.html

#regularization #entropy #gaussian

'Deep Out-of-Distribution Uncertainty Quantification via Weight Entropy Maximization', by Antoine de Mathelin, François Deheeger, Mathilde Mougeot, Nicolas Vayatis.

http://jmlr.org/papers/v26/23-1359.html

#regularization #entropy #gaussian

'An Optimal Transport Approach for Computing Adversarial Training Lower Bounds in Multiclass Classification', by Nicolas Garcia Trillos, Matt Jacobs, Jakwang Kim, Matthew Werenski.

http://jmlr.org/papers/v25/24-0268.html

#adversarial #regularization #classifiers

'An Optimal Transport Approach for Computing Adversarial Training Lower Bounds in Multiclass Classification', by Nicolas Garcia Trillos, Matt Jacobs, Jakwang Kim, Matthew Werenski.

http://jmlr.org/papers/v25/24-0268.html

#adversarial #regularization #classifiers

'An Inexact Projected Regularized Newton Method for Fused Zero-norms Regularization Problems', by Yuqia Wu, Shaohua Pan, Xiaoqi Yang.

http://jmlr.org/papers/v25/23-1700.html

#regularization #regularized #gradient

'An Inexact Projected Regularized Newton Method for Fused Zero-norms Regularization Problems', by Yuqia Wu, Shaohua Pan, Xiaoqi Yang.

http://jmlr.org/papers/v25/23-1700.html

#regularization #regularized #gradient

'Entropic Gromov-Wasserstein Distances: Stability and Algorithms', by Gabriel Rioux, Ziv Goldfeld, Kengo Kato.

http://jmlr.org/papers/v25/24-0039.html

#regularization #wasserstein #variational

'Entropic Gromov-Wasserstein Distances: Stability and Algorithms', by Gabriel Rioux, Ziv Goldfeld, Kengo Kato.

http://jmlr.org/papers/v25/24-0039.html

#regularization #wasserstein #variational

'Spectral Regularized Kernel Goodness-of-Fit Tests', by Omar Hagrass, Bharath K. Sriperumbudur, Bing Li.

http://jmlr.org/papers/v25/23-1031.html

#regularization #regularized #spectral

'Spectral Regularized Kernel Goodness-of-Fit Tests', by Omar Hagrass, Bharath K. Sriperumbudur, Bing Li.

http://jmlr.org/papers/v25/23-1031.html

#regularization #regularized #spectral

'Stochastic Regularized Majorization-Minimization with weakly convex and multi-convex surrogates', by Hanbaek Lyu.

http://jmlr.org/papers/v25/23-0349.html

#regularization #regularized #minimization

'Stochastic Regularized Majorization-Minimization with weakly convex and multi-convex surrogates', by Hanbaek Lyu.

http://jmlr.org/papers/v25/23-0349.html

#regularization #regularized #minimization

'Random Smoothing Regularization in Kernel Gradient Descent Learning', by Liang Ding, Tianyang Hu, Jiahang Jiang, Donghao Li, Wenjia Wang, Yuan Yao.

http://jmlr.org/papers/v25/23-0580.html

#regularization #smoothing #gradient

'Random Smoothing Regularization in Kernel Gradient Descent Learning', by Liang Ding, Tianyang Hu, Jiahang Jiang, Donghao Li, Wenjia Wang, Yuan Yao.

http://jmlr.org/papers/v25/23-0580.html

#regularization #smoothing #gradient

'Functional optimal transport: regularized map estimation and domain adaptation for functional data', by Jiacheng Zhu, Aritra Guha, Dat Do, Mengdi Xu, XuanLong Nguyen, Ding Zhao.

http://jmlr.org/papers/v25/22-0217.html

#transport #regularization #measures

'Functional optimal transport: regularized map estimation and domain adaptation for functional data', by Jiacheng Zhu, Aritra Guha, Dat Do, Mengdi Xu, XuanLong Nguyen, Ding Zhao.

http://jmlr.org/papers/v25/22-0217.html

#transport #regularization #measures

'On Regularized Radon-Nikodym Differentiation', by Duc Hoan Nguyen, Werner Zellinger, Sergei Pereverzyev.

http://jmlr.org/papers/v25/23-0567.html

#regularization #regularized #estimation

'On Regularized Radon-Nikodym Differentiation', by Duc Hoan Nguyen, Werner Zellinger, Sergei Pereverzyev.

http://jmlr.org/papers/v25/23-0567.html

#regularization #regularized #estimation

'Variance estimation in graphs with the fused lasso', by Oscar Hernan Madrid Padilla.

http://jmlr.org/papers/v25/23-1061.html

#lasso #regularization #graphs

'Variance estimation in graphs with the fused lasso', by Oscar Hernan Madrid Padilla.

http://jmlr.org/papers/v25/23-1061.html

#lasso #regularization #graphs

'Label Alignment Regularization for Distribution Shift', by Ehsan Imani, Guojun Zhang, Runjia Li, Jun Luo, Pascal Poupart, Philip H.S. Torr, Yangchen Pan.

http://jmlr.org/papers/v25/23-0899.html

#regularization #regularizing #label

'Label Alignment Regularization for Distribution Shift', by Ehsan Imani, Guojun Zhang, Runjia Li, Jun Luo, Pascal Poupart, Philip H.S. Torr, Yangchen Pan.

http://jmlr.org/papers/v25/23-0899.html

#regularization #regularizing #label

'The Loss Landscape of Deep Linear Neural Networks: a Second-order Analysis', by El Mehdi Achour, François Malgouyres, Sébastien Gerchinovitz.

http://jmlr.org/papers/v25/23-0493.html

#minimizers #regularization #optimization

'The Loss Landscape of Deep Linear Neural Networks: a Second-order Analysis', by El Mehdi Achour, François Malgouyres, Sébastien Gerchinovitz.

http://jmlr.org/papers/v25/23-0493.html

#minimizers #regularization #optimization

'Variation Spaces for Multi-Output Neural Networks: Insights on Multi-Task Learning and Network Compression', by Joseph Shenouda, Rahul Parhi, Kangwook Lee, Robert D. Nowak.

http://jmlr.org/papers/v25/23-0677.html

#regularization #lasso #representations

'Variation Spaces for Multi-Output Neural Networks: Insights on Multi-Task Learning and Network Compression', by Joseph Shenouda, Rahul Parhi, Kangwook Lee, Robert D. Nowak.

http://jmlr.org/papers/v25/23-0677.html

#regularization #lasso #representations

'Understanding Entropic Regularization in GANs', by Daria Reshetova, Yikun Bai, Xiugang Wu, Ayfer Özgür.

http://jmlr.org/papers/v25/21-1295.html

#regularization #adversarial #gans

'Understanding Entropic Regularization in GANs', by Daria Reshetova, Yikun Bai, Xiugang Wu, Ayfer Özgür.

http://jmlr.org/papers/v25/21-1295.html

#regularization #adversarial #gans

'Dropout Regularization Versus l2-Penalization in the Linear Model', by Gabriel Clara, Sophie Langer, Johannes Schmidt-Hieber.

http://jmlr.org/papers/v25/23-0803.html

#regularization #dropout #penalization

'Dropout Regularization Versus l2-Penalization in the Linear Model', by Gabriel Clara, Sophie Langer, Johannes Schmidt-Hieber.

http://jmlr.org/papers/v25/23-0803.html

#regularization #dropout #penalization

'Blessings and Curses of Covariate Shifts: Adversarial Learning Dynamics, Directional Convergence, and Equilibria', by Tengyuan Liang.

http://jmlr.org/papers/v25/23-0651.html

#adversarial #learned #regularization

'Blessings and Curses of Covariate Shifts: Adversarial Learning Dynamics, Directional Convergence, and Equilibria', by Tengyuan Liang.

http://jmlr.org/papers/v25/23-0651.html

#adversarial #learned #regularization

'Sparse Representer Theorems for Learning in Reproducing Kernel Banach Spaces', by Rui Wang, Yuesheng Xu, Mingsong Yan.

http://jmlr.org/papers/v25/23-0645.html

#regularization #sparse #regularized

'Sparse Representer Theorems for Learning in Reproducing Kernel Banach Spaces', by Rui Wang, Yuesheng Xu, Mingsong Yan.

http://jmlr.org/papers/v25/23-0645.html

#regularization #sparse #regularized

'The good, the bad and the ugly sides of data augmentation: An implicit spectral regularization perspective', by Chi-Heng Lin, Chiraag Kaushik, Eva L. Dyer, Vidya Muthukumar.

http://jmlr.org/papers/v25/22-1312.html

#regularization #overparameterized #augmentation

'The good, the bad and the ugly sides of data augmentation: An implicit spectral regularization perspective', by Chi-Heng Lin, Chiraag Kaushik, Eva L. Dyer, Vidya Muthukumar.

http://jmlr.org/papers/v25/22-1312.html

#regularization #overparameterized #augmentation

'Improving Lipschitz-Constrained Neural Networks by Learning Activation Functions', by Stanislas Ducotterd et al.

http://jmlr.org/papers/v25/22-1347.html

#regularization #optimization #lipschitz

'Improving Lipschitz-Constrained Neural Networks by Learning Activation Functions', by Stanislas Ducotterd et al.

http://jmlr.org/papers/v25/22-1347.html

#regularization #optimization #lipschitz

Линейная регрессия. Основная идея, модификации и реализация с нуля на Python

В машинном и глубоком обучении линейная регрессия занимает особое место, являясь не просто статистическим инструментом, но а также фундаментальным компонентом для многих более сложных концепций. В данной статье рассмотрен не только принцип работы линейной регрессии с реализацией с нуля на Python, но а также описаны её модификации и проведён небольшой сравнительный анализ основных методов регуляризации. Помимо этого, в конце указаны дополнительные источники для более глубокого ознакомления.

https://habr.com/ru/articles/804135/

#линейная_регрессия #linear_regression #polynomial #ridge #lasso #elasticnet #regularization #реализация_с_нуля #python #data_science

'Sparse NMF with Archetypal Regularization: Computational and Robustness Properties', by Kayhan Behdin, Rahul Mazumder.

http://jmlr.org/papers/v25/21-0233.html

#sparse #regularization #robustness

'Sparse NMF with Archetypal Regularization: Computational and Robustness Properties', by Kayhan Behdin, Rahul Mazumder.

http://jmlr.org/papers/v25/21-0233.html

#sparse #regularization #robustness

'Optimal Bump Functions for Shallow ReLU networks: Weight Decay, Depth Separation, Curse of Dimensionality', by Stephan Wojtowytsch.

http://jmlr.org/papers/v25/22-1296.html

#regularization #minimizer #neuron

'Optimal Bump Functions for Shallow ReLU networks: Weight Decay, Depth Separation, Curse of Dimensionality', by Stephan Wojtowytsch.

http://jmlr.org/papers/v25/22-1296.html

#regularization #minimizer #neuron

Here is another #tutorial that briefly introduces L1 and L2 #regularization in #MachineLearning:

🌍 https://www.fabriziomusacchio.com/blog/2023-03-28-l1_l2_regularization/

Here is another #tutorial that briefly introduces L1 and L2 #regularization in #MachineLearning:

🌍 https://www.fabriziomusacchio.com/blog/2023-03-28-l1_l2_regularization/

'Post-Regularization Confidence Bands for Ordinary Differential Equations', by Xiaowu Dai, Lexin Li.

http://jmlr.org/papers/v25/22-0487.html

#regularization #kernel #regulatory

'Post-Regularization Confidence Bands for Ordinary Differential Equations', by Xiaowu Dai, Lexin Li.

http://jmlr.org/papers/v25/22-0487.html

#regularization #kernel #regulatory

'On the Effect of Initialization: The Scaling Path of 2-Layer Neural Networks', by Sebastian Neumayer, Lénaïc Chizat, Michael Unser.

http://jmlr.org/papers/v25/23-0549.html

#regularization #gradient #optimization

'On the Effect of Initialization: The Scaling Path of 2-Layer Neural Networks', by Sebastian Neumayer, Lénaïc Chizat, Michael Unser.

http://jmlr.org/papers/v25/23-0549.html

#regularization #gradient #optimization

'Fast Policy Extragradient Methods for Competitive Games with Entropy Regularization', by Shicong Cen, Yuting Wei, Yuejie Chi.

http://jmlr.org/papers/v25/21-1205.html

#regularization #reinforcement #regularized

'On the Dynamics Under the Unhinged Loss and Beyond', by Xiong Zhou, Xianming Liu, Hanzhang Wang, Deming Zhai, Jiangjunjun, Xiangyang Ji.

http://jmlr.org/papers/v24/23-0771.html

#regularization #deep #normalization

'Optimal Parameter-Transfer Learning by Semiparametric Model Averaging', by Xiaonan Hu, Xinyu Zhang.

http://jmlr.org/papers/v24/23-0030.html

#regularization #models #semiparametric