Network of statistical distributions
Updated: 2025-02-28 11:55:02
The network of 76 probability distributions show how they are connected:
Solid lines…Tags: distributions- The network of 76 probability distributions show how they are connected:
Solid lines…Tags: distributions
USDA sued for removing climate data
Updated: 2025-02-28 10:18:33Farming and environmental groups are suing the U.S. Department of Agriculture for removing…Tags: climate, government, takedown, USDA✚ Visualization Tools, Datasets, and Resources — February 2025 Roundup
Updated: 2025-02-27 19:30:32
Here are tools you can use, data to play with, and resources to learn from that bubbled up in February.Tags: roundupRecent Data Graphics in Focus — DataViz Weekly
Updated: 2025-02-21 18:08:07Data holds valuable insights, and well-crafted visualizations help bring them to light. DataViz Weekly is all about demonstrating how that happens in practice, curating compelling recent data graphics from around the web. Check out what we have for you today: Corruption perceptions worldwide — Transparency International Bird strikes and aviation safety — Reuters Crops and […] The post Recent Data Graphics in Focus — DataViz Weekly appeared first on AnyChart News.Data Visualization in Action: Fresh Examples — DataViz Weekly
Updated: 2025-02-14 13:57:40We’re back with DataViz Weekly, where we showcase some of the best new data visualization examples — from individual charts and maps to full-scale visual stories and projects. Take a look at our latest picks: Swiss research funding — Colas Droin Education and voting patterns in U.S. presidential elections — Jon Boeckenstedt City walkability and improvement potential — […] The post Data Visualization in Action: Fresh Examples — DataViz Weekly appeared first on AnyChart News.Supervised Learning with Evolving Tasks and Performance Guarantees
Updated: 2025-02-12 20:41:46Home Page Papers Submissions News Editorial Board Special Issues Open Source Software Proceedings PMLR Data DMLR Transactions TMLR Search Statistics Login Frequently Asked Questions Contact Us Supervised Learning with Evolving Tasks and Performance Guarantees Verónica Álvarez , Santiago Mazuelas , Jose A . Lozano 26(17 1 59, 2025. Abstract Multiple supervised learning scenarios are composed by a sequence of classification tasks . For instance , multi-task learning and continual learning aim to learn a sequence of tasks that is either fixed or grows over time . Existing techniques for learning tasks that are in a sequence are tailored to specific scenarios , lacking adaptability to others . In addition , most of existing techniques consider situations in which the order of the tasks in theFrom Sparse to Dense Functional Data in High Dimensions: Revisiting Phase Transitions from a Non-Asymptotic Perspective
Updated: 2025-02-12 20:41:45: Home Page Papers Submissions News Editorial Board Special Issues Open Source Software Proceedings PMLR Data DMLR Transactions TMLR Search Statistics Login Frequently Asked Questions Contact Us From Sparse to Dense Functional Data in High Dimensions : Revisiting Phase Transitions from a Non-Asymptotic Perspective Shaojun Guo , Dong Li , Xinghao Qiao , Yizhu Wang 26(15 1 40, 2025. Abstract Nonparametric estimation of the mean and covariance functions is ubiquitous in functional data analysis and local linear smoothing techniques are most frequently used . Zhang and Wang 2016 explored different types of asymptotic properties of the estimation , which reveal interesting phase transition phenomena based on the relative order of the average sampling frequency per subject T$ to the number ofEstimating Network-Mediated Causal Effects via Principal Components Network Regression
Updated: 2025-02-12 20:41:45Home Page Papers Submissions News Editorial Board Special Issues Open Source Software Proceedings PMLR Data DMLR Transactions TMLR Search Statistics Login Frequently Asked Questions Contact Us Estimating Network-Mediated Causal Effects via Principal Components Network Regression Alex Hayes , Mark M . Fredrickson , Keith Levin 26(13 1 99, 2025. Abstract We develop a method to decompose causal effects on a social network into an indirect effect mediated by the network , and a direct effect independent of the social network . To handle the complexity of network structures , we assume that latent social groups act as causal mediators . We develop principal components network regression models to differentiate the social effect from the non-social effect . Fitting the regression models is asSelective Inference with Distributed Data
Updated: 2025-02-12 20:41:45Home Page Papers Submissions News Editorial Board Special Issues Open Source Software Proceedings PMLR Data DMLR Transactions TMLR Search Statistics Login Frequently Asked Questions Contact Us Selective Inference with Distributed Data Sifan Liu , Snigdha Panigrahi 26(12 1 44, 2025. Abstract When data are distributed across multiple sites or machines rather than centralized in one location , researchers face the challenge of extracting meaningful information without directly sharing individual data points . While there are many distributed methods for point estimation using sparse regression , few options are available for estimating uncertainties or conducting hypothesis tests based on the estimated sparsity . In this paper , we introduce a procedure for performing selective inference withAn Axiomatic Definition of Hierarchical Clustering
Updated: 2025-02-12 20:41:44In this paper, we take an axiomatic approach to defining a population hierarchical clustering for piecewise constant densities, and in a similar manner to Lebesgue integration, extend this definition to more general densities. When the density satisfies some mild conditions, e.g., when it has connected support, is continuous, and vanishes only at infinity, or when the connected components of the density satisfy these conditions, our axiomatic definition results in Hartigan's definition of cluster tree.Enhancing Graph Representation Learning with Localized Topological Features
Updated: 2025-02-12 20:41:43Home Page Papers Submissions News Editorial Board Special Issues Open Source Software Proceedings PMLR Data DMLR Transactions TMLR Search Statistics Login Frequently Asked Questions Contact Us Enhancing Graph Representation Learning with Localized Topological Features Zuoyu Yan , Qi Zhao , Ze Ye , Tengfei Ma , Liangcai Gao , Zhi Tang , Yusu Wang , Chao Chen 26(5 1 36, 2025. Abstract Representation learning on graphs is a fundamental problem that can be crucial in various tasks . Graph neural networks , the dominant approach for graph representation learning , are limited in their representation power . Therefore , it can be beneficial to explicitly extract and incorporate high-order topological and geometric information into these models . In this paper , we propose a principled approachDisC2o-HD: Distributed causal inference with covariates shift for analyzing real-world high-dimensional data
Updated: 2025-02-12 20:41:43: Home Page Papers Submissions News Editorial Board Special Issues Open Source Software Proceedings PMLR Data DMLR Transactions TMLR Search Statistics Login Frequently Asked Questions Contact Us DisC2o-HD : Distributed causal inference with covariates shift for analyzing real-world high-dimensional data Jiayi Tong , Jie Hu , George Hripcsak , Yang Ning , Yong Chen 26(3 1 50, 2025. Abstract High-dimensional healthcare data , such as electronic health records EHR data and claims data , present two primary challenges due to the large number of variables and the need to consolidate data from multiple clinical sites . The third key challenge is the potential existence of heterogeneity in terms of covariate shift . In this paper , we propose a distributed learning algorithm accounting forEfficiently Escaping Saddle Points in Bilevel Optimization
Updated: 2025-02-12 20:41:43Home Page Papers Submissions News Editorial Board Special Issues Open Source Software Proceedings PMLR Data DMLR Transactions TMLR Search Statistics Login Frequently Asked Questions Contact Us Efficiently Escaping Saddle Points in Bilevel Optimization Minhui Huang , Xuxing Chen , Kaiyi Ji , Shiqian Ma , Lifeng Lai 26(1 1 61, 2025. Abstract Bilevel optimization is one of the fundamental problems in machine learning and optimization . Recent theoretical developments in bilevel optimization focus on finding the first-order stationary points for nonconvex-strongly-convex cases . In this paper , we analyze algorithms that can escape saddle points in nonconvex-strongly-convex bilevel optimization . Specifically , we show that the perturbed approximate implicit differentiation AID with a warmQuadrant Chart with Custom Image Markers — JS Chart Tips
Updated: 2025-02-11 09:14:28Displaying unique images as markers can add a distinctive touch to your charts, making data visualization more engaging and informative. In this edition of JS Chart Tips, we will guide you through the process of using custom image markers for each data point in a quadrant chart with our JavaScript charting library. Question: How to […] The post Quadrant Chart with Custom Image Markers — JS Chart Tips appeared first on AnyChart News.Noteworthy New Visualizations to Explore — DataViz Weekly
Updated: 2025-02-07 17:54:44Continuing our regular DataViz Weekly, we’re glad to share the most interesting of all the new data visualizations we’ve recently come across, well worth a look. Here’s what we have lined up this time: PIN code popularity — ABC News Complexity of D.C. airspace amid the Potomac River midair collision — The New York Times […] The post Noteworthy New Visualizations to Explore — DataViz Weekly appeared first on AnyChart News.Chiqui Esteban: Insights from the Washington Post Graphics Team
Updated: 2025-02-06 05:19:20
Chiqui Esteban visual explaining the Electoral College 📣 New podcast alert! 📣 Chiqui Esteban is Design & Art Director at the Washington Post Opinion section. In the first episode with new co-host Scott Klein, he talks to us about the lessons he’s learned during his amazing career, from his early days as a student at … Continue reading →20+ Years of Advancing Data Visualization: Interview with Our CEO
Updated: 2025-02-05 08:42:37Unicorns Journal just published an interview with our CEO and co-founder, Anton Baranchuk, where he reflects on AnyChart’s journey from a small startup to a global leader in data visualization, trusted by enterprises worldwide. Anton talks about how we got started, the transition from Flash to JavaScript charting, our work with Qlik, and the vision […] The post 20+ Years of Advancing Data Visualization: Interview with Our CEO appeared first on AnyChart News.