Tootfinder

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Neural reproducing kernel Banach spaces and representer theorems for deep networks
Francesca Bartolucci, Ernesto De Vito, Lorenzo Rosasco, Stefano Vigogna

Période : Š partir de 21h25
Dates : du lundi 18 au vendredi 29 août, sauf les samedis et dimanches.
Les trains missions Paris Saint-Lazare <> Nanterre Université sont supprimés.
Pour rejoindre les gares de Paris Saint-Lazare jusqu’Š Nanterre Université, nous vous conseillons d’emprunter les trains de la ligne L, axe Paris Saint-Lazare <> Maisons-Laffitte.
Les horaires du calculateur d'itinéraire tiennent compte des travaux.
Motif : travaux.

#pastpuzzle 374
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https://www.pastpuzzle.de

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Can synthetic data reproduce real-world findings in epidemiology? A replication study using tree-...
Jan Kapar, et al.

Le trafic est perturbé entre Paris Saint-Lazare et Maisons-Laffitte dans les 2 sens. Alerte de sécurité émise par le conducteur​
Pour plus d'informations sur cette perturbation, consultez le fil X de la ligne L.
🤖 22/08 22:17

Ach, Bahnfahren ist immer wieder toll, gerade gesehen, dass meine Verbindung nach Hannover morgen früh "nicht mehr fahrbar" ist, zum Glück schon eine Alternative gefunden ... allerdings mit sportlichen Umsteigzeiten in Ffm Hbf. Klappt hoffentlich alles.

Tree-like Pairwise Interaction Networks
Ronald Richman, Salvatore Scognamiglio, Mario V. W\"uthrich
https://arxiv.org/abs/2508.15678 https://arxiv.org…

Tree-like Pairwise Interaction Networks
Modeling feature interactions in tabular data remains a key challenge in predictive modeling, for example, as used for insurance pricing. This paper proposes the Tree-like Pairwise Interaction Network (PIN), a novel neural network architecture that explicitly captures pairwise feature interactions through a shared feed-forward neural network architecture that mimics the structure of decision trees. PIN enables intrinsic interpretability by design, allowing for direct inspection of interaction e…

Période : Š partir de 22h10 Dates : du lundi 1er septembre au vendredi 17 octobre, sauf les samedis et dimanches.
Le trafic est interrompu entre La Défense <> Versailles Rive Droite / Saint-Nom la Bretèche.
Un service de bus de remplacement est mis en place entre La Défense <> Versailles Rive Droite et La Défense <> Saint-Nom la Bretèche, avec desserte des gares intermédiaires. Les derniers trains Š circuler normalement :
- Paris Saint-Lazare vers Versailles …

SF has so many parking lots and other soft sites especially on the westside, we should be building housing there instead of demolishing rent-controlled homes. But #SB79 offers few protections & none for rent-controlled duplexes. Sign this petition to amend the bill to fix this!
#SFPol

Amend SB 79 to Protect Tenants and Communities
SB 79 puts thousands of rent-controlled homes and small businesses in San Francisco at risk by allowing demolition without strong affordability requirements or local oversight. It overrides years of community planning and opens the door to displacement in working-class neighborhoods across San Francisco. Sign the petition to demand smart, equitable amendments that protect tenants, preserve local plans, and ensure transit-oriented development works for everyone.

Bayesian Optimization with Expected Improvement: No Regret and the Choice of Incumbent
Jingyi Wang, Haowei Wang, Szu Hui Ng, Cosmin G. Petra
https://arxiv.org/abs/2508.15674 htt…

Bayesian Optimization with Expected Improvement: No Regret and the Choice of Incumbent
Expected improvement (EI) is one of the most widely used acquisition functions in Bayesian optimization (BO). Despite its proven empirical success in applications, the cumulative regret upper bound of EI remains an open question. In this paper, we analyze the classic noisy Gaussian process expected improvement (GP-EI) algorithm. We consider the Bayesian setting, where the objective is a sample from a GP. Three commonly used incumbents, namely the best posterior mean incumbent (BPMI), the best s…

Période : Š partir de 21h50
Dates : du lundi 22 septembre au vendredi 17 octobre, sauf les samedis et dimanches.
Le trafic est interrompu entre Sartrouville <> Maisons-Laffitte.
Un service de bus de remplacement est mis en place entre Sartrouville <> Cergy le Haut, avec desserte des gares intermédiaires.
Pour vous rendre Š la gare de Maisons-Laffitte, vous pouvez également emprunter le RER A direction Poissy.
De plus, le train de 00h29 au départ de P…

niemand weiß das
#pastpuzzle 354
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https://www.pastpuzzle.de

Bayesian Inference and Learning in Nonlinear Dynamical Systems: A Framework for Incorporating Explicit and Implicit Prior Knowledge
Bj\"orn Volkmann, Jan-Hendrik Ewering, Michael Meindl, Simon F. G. Ehlers, Thomas Seel
https://arxiv.org/abs/2508.15345

Bayesian Inference and Learning in Nonlinear Dynamical Systems: A Framework for Incorporating Explicit and Implicit Prior Knowledge
Accuracy and generalization capabilities are key objectives when learning dynamical system models. To obtain such models from limited data, current works exploit prior knowledge and assumptions about the system. However, the fusion of diverse prior knowledge, e. g. partially known system equations and smoothness assumptions about unknown model parts, with information contained in the data remains a challenging problem, especially in input-output settings with latent system state. In particular,…

Période : entre 12h30 et 13h30
Dates : du lundi 15 Septembre au vendredi 17 Octobre.
Le trafic est interrompu entre Saint-Cloud et Versailles Rive Droite sur la ligne L.
A partir du 22/09 un train supplémentaire circulera au départ de Versailles Rive Droite Š 12h59. Ce train desservira toute les gares jusqu'Š Paris-Saint-Lazare.
Pour vous rendre vers la ville de Versailles nous vous conseillons d'emprunter un train des lignes N,C et U.
Information complé…

D.D. Lewis, Durable Linebacker for the Dallas Cowboys, Dies at 79 https://www.nytimes.com/2025/09/21/sports/football/dd-lewis-dead.html

Legendary Cowboys Linebacker Dies at 79 https://heavy.com/sports/nfl/dallas-cowboys/legendary-cowboys-linebacker-dies-at-79/?adt_ei=[email]

Kernel-based Equalized Odds: A Quantification of Accuracy-Fairness Trade-off in Fair Representation Learning
Yijin Ni, Xiaoming Huo
https://arxiv.org/abs/2508.15084 https://

Kernel-based Equalized Odds: A Quantification of Accuracy-Fairness Trade-off in Fair Representation Learning
This paper introduces a novel kernel-based formulation of the Equalized Odds (EO) criterion, denoted as $EO_k$, for fair representation learning (FRL) in supervised settings. The central goal of FRL is to mitigate discrimination regarding a sensitive attribute $S$ while preserving prediction accuracy for the target variable $Y$. Our proposed criterion enables a rigorous and interpretable quantification of three core fairness objectives: independence (prediction $\hat{Y}$ is independent of $S$),…

https://rudhar.com/politics/election/2rnd3prt/en08.htm

Heute vor 79 Jahren: Am 24. Juli 1946 wurde im Rahmen der Operation #Crossroads am Bikini-Atoll der Baker-Test durchgeführt. Eine Atombombe detonierte 90 Fuß unter Wasser, wobei das Landungsboot LSM-60 verdampfte. 10 Schiffe sanken, darunter die Prinz Eugen.

Good " morning after FarmAid"
At 92, Willie Nelson
At 84, Bob Dylan
At 79, Neil Young
https://www.startribune.com/live-farm-aid-40-kicks-off-at-huntington-bank-stadium-in-minneapolis/601475512

Willie Nelson, Bob Dylan perform at Farm Aid 40
The Farm Aid founder and his friends closed out the night at Huntington Bank Stadium.

[2025-08-22 Fri (UTC), 4 new articles found for stat.ML Machine Learning]
toXiv_bot_toot

Le trafic est ralenti de Versailles Rive Droite vers Paris Saint-Lazare.
Motif : panne d'un train en gare de Versailles Rive Droite
🤖 22/08 05:38

Dallas Cowboys great D.D. Lewis dies at 79, credited with iconic line about Texas Stadium https://www.elpasotimes.com/story/sports/football/nfl/2025/09/17/dallas-cowboys-great-d-d-lewis-dies-at-age-79-cowb…

Le trafic est perturbé entre Paris Saint-Lazare et Cergy le Haut dans les 2 sens.
Pour plus d'informations sur cette perturbation, consultez le fil X de la ligne L.
Motif : affaires oubliées Š bord du train Š Cergy-le-Haut.
🤖 21/08 16:15

Gerade PDF Arranger entdeckt. Das ist ein gutes Werkzeug, um unter Linux PDFs zusammenzustellen oder Seiten umzusortieren, Auch gut, unter Eigenschaften ändern kann man auch fix die Metadaten von PDFs editieren.

Comparing Model-agnostic Feature Selection Methods through Relative Efficiency
Chenghui Zheng, Garvesh Raskutti
https://arxiv.org/abs/2508.14268 https://ar…

Comparing Model-agnostic Feature Selection Methods through Relative Efficiency
Feature selection and importance estimation in a model-agnostic setting is an ongoing challenge of significant interest. Wrapper methods are commonly used because they are typically model-agnostic, even though they are computationally intensive. In this paper, we focus on feature selection methods related to the Generalized Covariance Measure (GCM) and Leave-One-Covariate-Out (LOCO) estimation, and provide a comparison based on relative efficiency. In particular, we present a theoretical compar…

[2025-08-21 Thu (UTC), 3 new articles found for stat.ML Machine Learning]
toXiv_bot_toot

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Comparison of parallel SMC and MCMC for Bayesian deep learning
Liang, Lukens, Lohani, Kirby, Searles, Qiu, Law

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Noise Robust One-Class Intrusion Detection on Dynamic Graphs
Aleksei Liuliakov, Alexander Schulz, Luca Hermes, Barbara Hammer

Das war es also, der #Knall heute Morgen in Bad Schwalbach. Man hat den Schalldruck sogar gespürt und die Fenster haben leicht gebebt. #RheinMain
Lauter Knall über Rhein-Main-Gebiet: Kampfflugzeug durchbricht Schallmauer | hessenschau.de | Panorama

The C-index Multiverse
Bego\~na B. Sierra, Colin McLean, Peter S. Hall, Catalina A. Vallejos
https://arxiv.org/abs/2508.14821 https://arxiv.org/pdf/2508.14…

The C-index Multiverse
Quantifying out-of-sample discrimination performance for time-to-event outcomes is a fundamental step for model evaluation and selection in the context of predictive modelling. The concordance index, or C-index, is a widely used metric for this purpose, particularly with the growing development of machine learning methods. Beyond differences between proposed C-index estimators (e.g. Harrell's, Uno's and Antolini's), we demonstrate the existence of a C-index multiverse among available R and pyth…

Période : Š partir de 21h25
Dates : du lundi 18 au vendredi 29 août, sauf les samedis et dimanches.
Les trains missions Paris Saint-Lazare <> Nanterre Université sont supprimés.
Pour rejoindre les gares de Paris Saint-Lazare jusqu’Š Nanterre Université, nous vous conseillons d’emprunter les trains de la ligne L, axe Paris Saint-Lazare <> Maisons-Laffitte.
Les horaires du calculateur d'itinéraire tiennent compte des travaux.
Motif : travaux.

Evaluation and Optimization of Leave-one-out Cross-validation for the Lasso
Ryan Burn
https://arxiv.org/abs/2508.14368 https://arxiv.org/pdf/2508.14368

Evaluation and Optimization of Leave-one-out Cross-validation for the Lasso
I develop an algorithm to produce the piecewise quadratic that computes leave-one-out cross-validation for the lasso as a function of its hyperparameter. The algorithm can be used to find exact hyperparameters that optimize leave-one-out cross-validation either globally or locally, and its practicality is demonstrated on real-world data sets.

Le trafic est Perturbé entre Cergy le Haut et Paris Saint-Lazare dans les 2 sens.
Pour plus d'informations sur cette perturbation, consultez le fil X de la ligne L.
Motif : affaires oubliées en gare de Cergy le Haut
🤖 20/08 12:29

Période : Š partir de 21h25
Dates : du lundi 18 au vendredi 29 août, sauf les samedis et dimanches.
Les trains missions Paris Saint-Lazare <> Nanterre Université sont supprimés.
Pour rejoindre les gares de Paris Saint-Lazare jusqu’Š Nanterre Université, nous vous conseillons d’emprunter les trains de la ligne L, axe Paris Saint-Lazare <> Maisons-Laffitte.
Les horaires du calculateur d'itinéraire tiennent compte des travaux.
Motif : travaux.

Uncertainty-Aware PCA for Arbitrarily Distributed Data Modeled by Gaussian Mixture Models
Daniel Kl\"otzl, Ozan Tastekin, David H\"agele, Marina Evers, Daniel Weiskopf
https://arxiv.org/abs/2508.13990

Uncertainty-Aware PCA for Arbitrarily Distributed Data Modeled by Gaussian Mixture Models
Multidimensional data is often associated with uncertainties that are not well-described by normal distributions. In this work, we describe how such distributions can be projected to a low-dimensional space using uncertainty-aware principal component analysis (UAPCA). We propose to model multidimensional distributions using Gaussian mixture models (GMMs) and derive the projection from a general formulation that allows projecting arbitrary probability density functions. The low-dimensional proje…

Le trafic est fortement perturbé entre Paris Saint-Lazare et Cergy le Haut dans les 2 sens.
Pour plus d'informations sur cette perturbation, consultez le fil X de la ligne L.
Motif : accident de personne en gare de Clichy-Levallois.
🤖 19/08 16:12

A PC Algorithm for Max-Linear Bayesian Networks
Carlos Am\'endola, Benjamin Hollering, Francesco Nowell
https://arxiv.org/abs/2508.13967 https://arxiv.…

A PC Algorithm for Max-Linear Bayesian Networks
Max-linear Bayesian networks (MLBNs) are a relatively recent class of structural equation models which arise when the random variables involved have heavy-tailed distributions. Unlike most directed graphical models, MLBNs are typically not faithful to d-separation and thus classical causal discovery algorithms such as the PC algorithm or greedy equivalence search can not be used to accurately recover the true graph structure. In this paper, we begin the study of constraint-based discovery algor…

Generalisation and benign over-fitting for linear regression onto random functional covariates
Andrew Jones, Nick Whiteley
https://arxiv.org/abs/2508.13895 https://

Generalisation and benign over-fitting for linear regression onto random functional covariates
We study theoretical predictive performance of ridge and ridge-less least-squares regression when covariate vectors arise from evaluating $p$ random, means-square continuous functions over a latent metric space at $n$ random and unobserved locations, subject to additive noise. This leads us away from the standard assumption of i.i.d. data to a setting in which the $n$ covariate vectors are exchangeable but not independent in general. Under an assumption of independence across dimensions, $4$-th…

Online Conformal Selection with Accept-to-Reject Changes
Kangdao Liu, Huajun Xi, Chi-Man Vong, Hongxin Wei
https://arxiv.org/abs/2508.13838 https://arxiv.o…

Online Conformal Selection with Accept-to-Reject Changes
Selecting a subset of promising candidates from a large pool is crucial across various scientific and real-world applications. Conformal selection offers a distribution-free and model-agnostic framework for candidate selection with uncertainty quantification. While effective in offline settings, its application to online scenarios, where data arrives sequentially, poses challenges. Notably, conformal selection permits the deselection of previously selected candidates, which is incompatible with…

Smooth Flow Matching
Jianbin Tan, Anru R. Zhang
https://arxiv.org/abs/2508.13831 https://arxiv.org/pdf/2508.13831…

Smooth Flow Matching
Functional data, i.e., smooth random functions observed over a continuous domain, are increasingly available in areas such as biomedical research, health informatics, and epidemiology. However, effective statistical analysis for functional data is often hindered by challenges such as privacy constraints, sparse and irregular sampling, infinite dimensionality, and non-Gaussian structures. To address these challenges, we introduce a novel framework named Smooth Flow Matching (SFM), tailored for g…

Flow Matching-Based Generative Modeling for Efficient and Scalable Data Assimilation
Taos Transue, Bohan Chen, So Takao, Bao Wang
https://arxiv.org/abs/2508.13313 https://

Flow Matching-Based Generative Modeling for Efficient and Scalable Data Assimilation
Data assimilation (DA) is the problem of sequentially estimating the state of a dynamical system from noisy observations. Recent advances in generative modeling have inspired new approaches to DA in high-dimensional nonlinear settings, especially the ensemble score filter (EnSF). However, these come at a significant computational burden due to slow sampling. In this paper, we introduce a new filtering framework based on flow matching (FM) -- called the ensemble flow filter (EnFF) -- to accelera…

Structural Foundations for Leading Digit Laws: Beyond Probabilistic Mixtures
Vladimir Berman
https://arxiv.org/abs/2508.13237 https://arxiv.org/pdf/2508.13…

Structural Foundations for Leading Digit Laws: Beyond Probabilistic Mixtures
This article presents a modern deterministic framework for the study of leading significant digit distributions in numerical data. Rather than relying on traditional probabilistic or mixture-based explanations, we demonstrate that the observed frequencies of leading digits are determined by the underlying arithmetic, algorithmic, and structural properties of the data-generating process. Our approach centers on a shift-invariant functional equation, whose general solution is given by explicit af…

Période : Š partir de 21h25
Dates : du lundi 18 au vendredi 29 août, sauf les samedis et dimanches.
Les trains missions Paris Saint-Lazare <> Nanterre Université sont supprimés.
Pour rejoindre les gares de Paris Saint-Lazare jusqu’Š Nanterre Université, nous vous conseillons d’emprunter les trains de la ligne L, axe Paris Saint-Lazare <> Maisons-Laffitte.
Les horaires du calculateur d'itinéraire tiennent compte des travaux.
Motif : travaux.

Preference Models assume Proportional Hazards of Utilities
Chirag Nagpal
https://arxiv.org/abs/2508.13189 https://arxiv.org/pdf/2508.13189

Preference Models assume Proportional Hazards of Utilities
Approaches for estimating preferences from human annotated data typically involves inducing a distribution over a ranked list of choices such as the Plackett-Luce model. Indeed, modern AI alignment tools such as Reward Modelling and Direct Preference Optimization are based on the statistical assumptions posed by the Plackett-Luce model. In this paper, I will connect the Plackett-Luce model to another classical and well known statistical model, the Cox Proportional Hazards model and attempt to s…

Le trafic est perturbé entre Paris Saint-Lazare et Cergy le Haut dans les 2 sens.
Pour plus d'informations sur cette perturbation, consultez le fil X de la ligne L.
Motif : panne d'un train Š Sartrouville (train de la ligne A du RER).
🤖 18/08 17:30

[2025-08-20 Wed (UTC), 8 new articles found for stat.ML Machine Learning]
toXiv_bot_toot

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Contrastive Learning on Multimodal Analysis of Electronic Health Records
Tianxi Cai, Feiqing Huang, Ryumei Nakada, Linjun Zhang, Doudou Zhou

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- AlphaEval: A Comprehensive and Efficient Evaluation Framework for Formula Alpha Mining
Ding, Chen, Huang, Guo, Mao, Shao, Zou, Liu, Zhang

Le trafic est perturbé entre Paris Saint-Lazare et Maisons-Laffitte dans les 2 sens.
Pour plus d'informations sur cette perturbation, consultez le fil X de la ligne L.
Motif : affaires oubliées ( dans un train en gare de Maisons-Laffitte).
🤖 17/08 20:10

Période : Š partir de 22h10 Dates : du lundi 1er septembre au vendredi 17 octobre, sauf les samedis et dimanches.
Le trafic est interrompu entre La Défense <> Versailles Rive Droite / Saint-Nom la Bretèche.
Un service de bus de remplacement est mis en place entre La Défense <> Versailles Rive Droite et La Défense <> Saint-Nom la Bretèche, avec desserte des gares intermédiaires. Les derniers trains Š circuler normalement :
- Paris Saint-Lazare vers Versailles …

Période : Š partir de 22h00
Dates : du lundi 1er au vendredi 19 septembre, sauf les samedis et dimanches.
Le trafic est interrompu entre Bécon les Bruyères <> Nanterre Université / Maisons-Laffitte.
Un service de bus de remplacement est mis en place entre Bécon les Bruyères <> Sartrouville, avec desserte des gares intermédiaires.
Pour rejoindre les gares de Houilles Carrières sur Seine, Sartrouville et Maisons-Laffitte, nous vous conseillons d'empr…

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[2/2]:
- NoProp: Training Neural Networks without Full Back-propagation or Full Forward-propagation
Qinyu Li, Yee Whye Teh, Razvan Pascanu

Le trafic est perturbé entre Paris Saint-Lazare et St-Nom-la-Bretèche et entre Paris Saint-Lazare et Versailles Rive Droite dans les 2 sens.
Pour plus d'informations sur cette perturbation, consultez le fil X de la ligne L.
Motif : panne de signalisation (dysfonctionnement des feux de signalisation dans le secteur de Clichy-Levallois).
🤖 17/09 10:01

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/2]:
- A Consistent and Scalable Algorithm for Best Subset Selection in Single Index Models
Borui Tang, Jin Zhu, Junxian Zhu, Xueqin Wang, Heping Zhang

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Enhancing Corrosion Resistance of Aluminum Alloys Through AI and ML Modeling
Farnaz Kaboudvand, Maham Khalid, Nydia Assaf, Vardaan Sahgal, Jon P. Ruffley, Brian J. McDermott

Trafic perturbé le jeudi 18 septembre : prévoir 2 trains sur 3.
Pour préparer votre trajet, rendez-vous sur le calculateur d’itinéraires de l’appli Île-de-France Mobilités, le site Transilien.com, SNCF Connect ou votre appli de mobilité.
Avant de vous rendre en gare, nous vous conseillons de vérifier l’horaire de vos trains.
Motif : mouvement social national interprofessionnel.
🤖 18/09 05:00

Shapley Values: Paired-Sampling Approximations
Michael Mayer, Mario V. W\"uthrich
https://arxiv.org/abs/2508.12947 https://arxiv.org/pdf/2508.12947

Shapley Values: Paired-Sampling Approximations
Originally introduced in cooperative game theory, Shapley values have become a very popular tool to explain machine learning predictions. Based on Shapley's fairness axioms, every input (feature component) gets a credit how it contributes to an output (prediction). These credits are then used to explain the prediction. The only limitation in computing the Shapley values (credits) for many different predictions is of computational nature. There are two popular sampling approximations, sampling K…

Simulation-Based Inference: A Practical Guide
Michael Deistler, Jan Boelts, Peter Steinbach, Guy Moss, Thomas Moreau, Manuel Gloeckler, Pedro L. C. Rodrigues, Julia Linhart, Janne K. Lappalainen, Benjamin Kurt Miller, Pedro J. Gon\c{c}alves, Jan-Matthis Lueckmann, Cornelius Schr\"oder, Jakob H. Macke
https://arxiv.org/abs/2508.12939…

Simulation-Based Inference: A Practical Guide
A central challenge in many areas of science and engineering is to identify model parameters that are consistent with prior knowledge and empirical data. Bayesian inference offers a principled framework for this task, but can be computationally prohibitive when models are defined by stochastic simulators. Simulation-based Inference (SBI) is a suite of methods developed to overcome this limitation, which has enabled scientific discoveries in fields such as particle physics, astrophysics, and neu…

The path to a goal: Understanding soccer possessions via path signatures
David Hirnschall, Robert Bajons
https://arxiv.org/abs/2508.12930 https://arxiv.org…

The path to a goal: Understanding soccer possessions via path signatures
We present a novel framework for predicting next actions in soccer possessions by leveraging path signatures to encode their complex spatio-temporal structure. Unlike existing approaches, we do not rely on fixed historical windows and handcrafted features, but rather encode the entire recent possession, thereby avoiding the inclusion of potentially irrelevant or misleading historical information. Path signatures naturally capture the order and interaction of events, providing a mathematically g…

Optimal Condition for Initialization Variance in Deep Neural Networks: An SGD Dynamics Perspective
Hiroshi Horii (SU), Sothea Has (KHM)
https://arxiv.org/abs/2508.12834 https://…

Optimal Condition for Initialization Variance in Deep Neural Networks: An SGD Dynamics Perspective
Stochastic gradient descent (SGD), one of the most fundamental optimization algorithms in machine learning (ML), can be recast through a continuous-time approximation as a Fokker-Planck equation for Langevin dynamics, a viewpoint that has motivated many theoretical studies. Within this framework, we study the relationship between the quasi-stationary distribution derived from this equation and the initial distribution through the Kullback-Leibler (KL) divergence. As the quasi-steady-state distr…

Unfolded Laplacian Spectral Embedding: A Theoretically Grounded Approach to Dynamic Network Representation
Haruka Ezoe, Hiroki Matsumoto, Ryohei Hisano
https://arxiv.org/abs/2508.12674

Unfolded Laplacian Spectral Embedding: A Theoretically Grounded Approach to Dynamic Network Representation
Dynamic relational structures play a central role in many AI tasks, but their evolving nature presents challenges for consistent and interpretable representation. A common approach is to learn time-varying node embeddings, whose effectiveness depends on satisfying key stability properties. In this paper, we propose Unfolded Laplacian Spectral Embedding, a new method that extends the Unfolded Adjacency Spectral Embedding framework to normalized Laplacians while preserving both cross-sectional an…

On computing and the complexity of computing higher-order $U$-statistics, exactly
Xingyu Chen, Ruiqi Zhang, Lin Liu
https://arxiv.org/abs/2508.12627 https://

On computing and the complexity of computing higher-order $U$-statistics, exactly
Higher-order $U$-statistics abound in fields such as statistics, machine learning, and computer science, but are known to be highly time-consuming to compute in practice. Despite their widespread appearance, a comprehensive study of their computational complexity is surprisingly lacking. This paper aims to fill that gap by presenting several results related to the computational aspect of $U$-statistics. First, we derive a useful decomposition from an $m$-th order $U$-statistic to a linear combi…

Le trafic est perturbé de St-Nom-la-Bretèche vers Paris Saint-Lazare et de Versailles Rive Droite vers Paris Saint-Lazare.
Motif : prolongation des travaux sur le réseau ferroviaire.
🤖 17/09 04:46

An Introduction to Sliced Optimal Transport
Khai Nguyen
https://arxiv.org/abs/2508.12519 https://arxiv.org/pdf/2508.12519

An Introduction to Sliced Optimal Transport
Sliced Optimal Transport (SOT) is a rapidly developing branch of optimal transport (OT) that exploits the tractability of one-dimensional OT problems. By combining tools from OT, integral geometry, and computational statistics, SOT enables fast and scalable computation of distances, barycenters, and kernels for probability measures, while retaining rich geometric structure. This paper provides a comprehensive review of SOT, covering its mathematical foundations, methodological advances, computa…

Robust Data Fusion via Subsampling
Jing Wang, HaiYing Wang, Kun Chen
https://arxiv.org/abs/2508.12048 https://arxiv.org/pdf/2508.12048

Robust Data Fusion via Subsampling
Data fusion and transfer learning are rapidly growing fields that enhance model performance for a target population by leveraging other related data sources or tasks. The challenges lie in the various potential heterogeneities between the target and external data, as well as various practical concerns that prevent a naïve data integration. We consider a realistic scenario where the target data is limited in size while the external data is large but contaminated with outliers; such data contami…

Période : Š partir de 22h10 Dates : du lundi 1er septembre au vendredi 17 octobre, sauf les samedis et dimanches.
Le trafic est interrompu entre La Défense <> Versailles Rive Droite / Saint-Nom la Bretèche.
Un service de bus de remplacement est mis en place entre La Défense <> Versailles Rive Droite et La Défense <> Saint-Nom la Bretèche, avec desserte des gares intermédiaires. Les derniers trains Š circuler normalement :
- Paris Saint-Lazare vers Versailles …

Dropping Just a Handful of Preferences Can Change Top Large Language Model Rankings
Jenny Y. Huang, Yunyi Shen, Dennis Wei, Tamara Broderick
https://arxiv.org/abs/2508.11847 htt…

Dropping Just a Handful of Preferences Can Change Top Large Language Model Rankings
We propose a method for evaluating the robustness of a widely used LLM ranking system -- the Bradley--Terry ranking system -- to dropping a worst-case very small fraction of evaluation data. Our approach is computationally fast and easy to adopt. When we apply our method to matchups from two popular human-preference platforms, Chatbot Arena and MT-Bench, we find that the Bradley--Terry rankings of top-performing models are remarkably sensitive to the removal of a small fraction of evaluations. …

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BaMANI: Bayesian Multi-Algorithm causal Network Inference
Habibolla Latifizadeh, Anika C. Pirkey, Alanna Gould, David J. Klinke II
https://arxiv.org/abs/2508.11741 https://

BaMANI: Bayesian Multi-Algorithm causal Network Inference
Improved computational power has enabled different disciplines to predict causal relationships among modeled variables using Bayesian network inference. While many alternative algorithms have been proposed to improve the efficiency and reliability of network prediction, the predicted causal networks reflect the generative process but also bear an opaque imprint of the specific computational algorithm used. Following a ``wisdom of the crowds" strategy, we developed an ensemble learning approach …

[2025-08-19 Tue (UTC), 10 new articles found for stat.ML Machine Learning]
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Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Modeling Sampling Distributions of Test Statistics with Autograd
Ali Al Kadhim, Harrison B. Prosper

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Holistic Bioprocess Development Across Scales Using Multi-Fidelity Batch Bayesian Optimization
Martens, Neufang, Butt\'e, von Stosch, del Rio Chanona, Helleckes

Nonparametric learning of stochastic differential equations from sparse and noisy data
Arnab Ganguly, Riten Mitra, Jinpu Zhou
https://arxiv.org/abs/2508.11597 https://

Nonparametric learning of stochastic differential equations from sparse and noisy data
The paper proposes a systematic framework for building data-driven stochastic differential equation (SDE) models from sparse, noisy observations. Unlike traditional parametric approaches, which assume a known functional form for the drift, our goal here is to learn the entire drift function directly from data without strong structural assumptions, making it especially relevant in scientific disciplines where system dynamics are partially understood or highly complex. We cast the estimation prob…

ADMIRE-BayesOpt: Accelerated Data MIxture RE-weighting for Language Models with Bayesian Optimization
Shengzhuang Chen, Xu Ouyang, Michael Arthur Leopold Pearce, Thomas Hartvigsen, Jonathan Richard Schwarz
https://arxiv.org/abs/2508.11551

ADMIRE-BayesOpt: Accelerated Data MIxture RE-weighting for Language Models with Bayesian Optimization
Determining the optimal data mixture for large language model training remains a challenging problem with an outsized impact on performance. In practice, language model developers continue to rely on heuristic exploration since no learning-based approach has emerged as a reliable solution. In this work, we propose to view the selection of training data mixtures as a black-box hyperparameter optimization problem, for which Bayesian Optimization is a well-established class of appropriate algorith…

Uniform convergence for Gaussian kernel ridge regression
Paul Dommel, Rajmadan Lakshmanan
https://arxiv.org/abs/2508.11274 https://arxiv.org/pdf/2508.11274…

Uniform convergence for Gaussian kernel ridge regression
This paper establishes the first polynomial convergence rates for Gaussian kernel ridge regression (KRR) with a fixed hyperparameter in both the uniform and the $L^{2}$-norm. The uniform convergence result closes a gap in the theoretical understanding of KRR with the Gaussian kernel, where no such rates were previously known. In addition, we prove a polynomial $L^{2}$-convergence rate in the case, where the Gaussian kernel's width parameter is fixed. This also contributes to the broader underst…

Counterfactual Survival Q Learning for Longitudinal Randomized Trials via Buckley James Boosting
Jeongjin Lee, Jong-Min Kim
https://arxiv.org/abs/2508.11060 https://

Counterfactual Survival Q Learning for Longitudinal Randomized Trials via Buckley James Boosting
We propose a Buckley James (BJ) Boost Q learning framework for estimating optimal dynamic treatment regimes under right censored survival data, tailored for longitudinal randomized clinical trial settings. The method integrates accelerated failure time models with iterative boosting techniques, including componentwise least squares and regression trees, within a counterfactual Q learning framework. By directly modeling conditional survival time, BJ Boost Q learning avoids the restrictive propor…

Non-asymptotic convergence bound of conditional diffusion models
Mengze Li
https://arxiv.org/abs/2508.10944 https://arxiv.org/pdf/2508.10944

Non-asymptotic convergence bound of conditional diffusion models
Learning and generating various types of data based on conditional diffusion models has been a research hotspot in recent years. Although conditional diffusion models have made considerable progress in improving acceleration algorithms and enhancing generation quality, the lack of non-asymptotic properties has hindered theoretical research. To address this gap, we focus on a conditional diffusion model within the domains of classification and regression (CARD), which aims to learn the original …

[2025-08-18 Mon (UTC), 5 new articles found for stat.ML Machine Learning]
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[2025-09-18 Thu (UTC), 1 new article found for stat.ML Machine Learning]
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Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Jackknife Variance Estimation for H\'ajek-Dominated Generalized U-Statistics
Jakob R. Juergens

[2025-09-17 Wed (UTC), 2 new articles found for stat.ML Machine Learning]
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The Morgan-Pitman Test of Equality of Variances and its Application to Machine Learning Model Evaluation and Selection
Argimiro Arratia, Alejandra Caba\~na, Ernesto Mordecki, Gerard Rovira-Parra
https://arxiv.org/abs/2509.12185

The Morgan-Pitman Test of Equality of Variances and its Application to Machine Learning Model Evaluation and Selection
Model selection in non-linear models often prioritizes performance metrics over statistical tests, limiting the ability to account for sampling variability. We propose the use of a statistical test to assess the equality of variances in forecasting errors. The test builds upon the classic Morgan-Pitman approach, incorporating enhancements to ensure robustness against data with heavy-tailed distributions or outliers with high variance, plus a strategy to make residuals from machine learning mode…

MMM: Clustering Multivariate Longitudinal Mixed-type Data
Francesco Amato, Julien Jacques
https://arxiv.org/abs/2509.12166 https://arxiv.org/pdf/2509.12166…

MMM: Clustering Multivariate Longitudinal Mixed-type Data
Multivariate longitudinal data of mixed-type are increasingly collected in many science domains. However, algorithms to cluster this kind of data remain scarce, due to the challenge to simultaneously model the within- and between-time dependence structures for multivariate data of mixed kind. We introduce the Mixture of Mixed-Matrices (MMM) model: reorganizing the data in a three-way structure and assuming that the non-continuous variables are observations of underlying latent continuous variab…

Identifiable Autoregressive Variational Autoencoders for Nonlinear and Nonstationary Spatio-Temporal Blind Source Separation
Mika Sipil\"a, Klaus Nordhausen, Sara Taskinen
https://arxiv.org/abs/2509.11962

Identifiable Autoregressive Variational Autoencoders for Nonlinear and Nonstationary Spatio-Temporal Blind Source Separation
The modeling and prediction of multivariate spatio-temporal data involve numerous challenges. Dimension reduction methods can significantly simplify this process, provided that they account for the complex dependencies between variables and across time and space. Nonlinear blind source separation has emerged as a promising approach, particularly following recent advances in identifiability results. Building on these developments, we introduce the identifiable autoregressive variational autoenco…

SpaPool: Soft Partition Assignment Pooling for__Graph Neural Networks
Rodrigue Govan (ISEA), Romane Scherrer (ISEA), Philippe Fournier-Viger (ISEA), Nazha Selmaoui-Folcher (ISEA)
https://arxiv.org/abs/2509.11675

SpaPool: Soft Partition Assignment Pooling for__Graph Neural Networks
This paper introduces SpaPool, a novel pooling method that combines the strengths of both dense and sparse techniques for a graph neural network. SpaPool groups vertices into an adaptive number of clusters, leveraging the benefits of both dense and sparse approaches. It aims to maintain the structural integrity of the graph while reducing its size efficiently. Experimental results on several datasets demonstrate that SpaPool achieves competitive performance compared to existing pooling techniqu…

E-ROBOT: a dimension-free method for robust statistics and machine learning via Schr\"odinger bridge
Davide La Vecchia, Hang Liu
https://arxiv.org/abs/2509.11532 https://…

E-ROBOT: a dimension-free method for robust statistics and machine learning via Schrödinger bridge
We propose the Entropic-regularized Robust Optimal Transport (E-ROBOT) framework, a novel method that combines the robustness of ROBOT with the computational and statistical benefits of entropic regularization. We show that, rooted in the Schrödinger bridge problem theory, E-ROBOT defines the robust Sinkhorn divergence $\overline{W}_{\varepsilon,λ}$, where the parameter $λ$ controls robustness and $\varepsilon$ governs the regularization strength. Letting $n\in \mathbb{N}$ denote the sample …

Learning Majority-to-Minority Transformations with MMD and Triplet Loss for Imbalanced Classification
Suman Cha, Hyunjoong Kim
https://arxiv.org/abs/2509.11511 https://

Learning Majority-to-Minority Transformations with MMD and Triplet Loss for Imbalanced Classification
Class imbalance in supervised classification often degrades model performance by biasing predictions toward the majority class, particularly in critical applications such as medical diagnosis and fraud detection. Traditional oversampling techniques, including SMOTE and its variants, generate synthetic minority samples via local interpolation but fail to capture global data distributions in high-dimensional spaces. Deep generative models based on GANs offer richer distribution modeling yet suffe…

A Particle-Flow Algorithm for Free-Support Wasserstein Barycenters
Kisung You
https://arxiv.org/abs/2509.11435 https://arxiv.org/pdf/2509.11435

A Particle-Flow Algorithm for Free-Support Wasserstein Barycenters
The Wasserstein barycenter extends the Euclidean mean to the space of probability measures by minimizing the weighted sum of squared 2-Wasserstein distances. We develop a free-support algorithm for computing Wasserstein barycenters that avoids entropic regularization and instead follows the formal Riemannian geometry of Wasserstein space. In our approach, barycenter atoms evolve as particles advected by averaged optimal-transport displacements, with barycentric projections of optimal transport …

Some Robustness Properties of Label Cleaning
Chen Cheng, John Duchi
https://arxiv.org/abs/2509.11379 https://arxiv.org/pdf/2509.11379

Some Robustness Properties of Label Cleaning
We demonstrate that learning procedures that rely on aggregated labels, e.g., label information distilled from noisy responses, enjoy robustness properties impossible without data cleaning. This robustness appears in several ways. In the context of risk consistency -- when one takes the standard approach in machine learning of minimizing a surrogate (typically convex) loss in place of a desired task loss (such as the zero-one mis-classification error) -- procedures using label aggregation obtai…

Next-Generation Reservoir Computing for Dynamical Inference
Rok Cestnik, Erik A. Martens
https://arxiv.org/abs/2509.11338 https://arxiv.org/pdf/2509.11338

Next-Generation Reservoir Computing for Dynamical Inference
We present a simple and scalable implementation of next-generation reservoir computing for modeling dynamical systems from time series data. Our approach uses a pseudorandom nonlinear projection of time-delay embedded input, allowing an arbitrary dimension of the feature space, thus providing a flexible alternative to the polynomial-based projections used in previous next-generation reservoir computing variants. We apply the method to benchmark tasks -- including attractor reconstruction and bi…

Contrastive Network Representation Learning
Zihan Dong, Xin Zhou, Ryumei Nakada, Lexin Li, Linjun Zhang
https://arxiv.org/abs/2509.11316 https://arxiv.org/…

Contrastive Network Representation Learning
Network representation learning seeks to embed networks into a low-dimensional space while preserving the structural and semantic properties, thereby facilitating downstream tasks such as classification, trait prediction, edge identification, and community detection. Motivated by challenges in brain connectivity data analysis that is characterized by subject-specific, high-dimensional, and sparse networks that lack node or edge covariates, we propose a novel contrastive learning-based statistic…

Predictable Compression Failures: Why Language Models Actually Hallucinate
Leon Chlon, Ahmed Karim, Maggie Chlon
https://arxiv.org/abs/2509.11208 https://a…

Predictable Compression Failures: Why Language Models Actually Hallucinate
Large language models perform near-Bayesian inference yet violate permutation invariance on exchangeable data. We resolve this by showing transformers minimize expected conditional description length (cross-entropy) over orderings, $\mathbb{E}_π[\ell(Y \mid Γ_π(X))]$, which admits a Kolmogorov-complexity interpretation up to additive constants, rather than the permutation-invariant description length $\ell(Y \mid X)$. This makes them Bayesian in expectation, not in realization. We derive (i)…

Maximum diversity, weighting and invariants of time series
Byungchang So
https://arxiv.org/abs/2509.11146 https://arxiv.org/pdf/2509.11146

Maximum diversity, weighting and invariants of time series
Magnitude, obtained as a special case of Euler characteristic of enriched category, represents a sense of the size of metric spaces and is related to classical notions such as cardinality, dimension, and volume. While the studies have explained the meaning of magnitude from various perspectives, continuity also gives a valuable view of magnitude. Based on established results about continuity of magnitude and maximum diversity, this article focuses on continuity of weighting, a distribution whos…

Kernel-based Stochastic Approximation Framework for Nonlinear Operator Learning
Jia-Qi Yang, Lei Shi
https://arxiv.org/abs/2509.11070 https://arxiv.org/pdf…

Kernel-based Stochastic Approximation Framework for Nonlinear Operator Learning
We develop a stochastic approximation framework for learning nonlinear operators between infinite-dimensional spaces utilizing general Mercer operator-valued kernels. Our framework encompasses two key classes: (i) compact kernels, which admit discrete spectral decompositions, and (ii) diagonal kernels of the form $K(x,x')=k(x,x')T$, where $k$ is a scalar-valued kernel and $T$ is a positive operator on the output space. This broad setting induces expressive vector-valued reproducing kernel Hilbe…

Variable Selection Using Relative Importance Rankings
Tien-En Chang, Argon Chen
https://arxiv.org/abs/2509.10853 https://arxiv.org/pdf/2509.10853

Variable Selection Using Relative Importance Rankings
Although conceptually related, variable selection and relative importance (RI) analysis have been treated quite differently in the literature. While RI is typically used for post-hoc model explanation, this paper explores its potential for variable ranking and filter-based selection before model creation. Specifically, we anticipate strong performance from the RI measures because they incorporate both direct and combined effects of predictors, addressing a key limitation of marginal correlation…