• Insightful Data Visualizations You Need to See — DataViz Weekly

    Updated: 2025-03-07 09:42:25
    Data visualization is powerful — when done right, it conveys meaning clearly and helps insights emerge naturally. We continue DataViz Weekly, where we regularly highlight some excellent examples from around the web, offering a firsthand look at how graphical representation can effectively reveal and clarify information. This time, we think you just need to see […] The post Insightful Data Visualizations You Need to See — DataViz Weekly appeared first on AnyChart News.

  • ✚ Smell Test for Data

    Updated: 2025-03-06 19:30:38
    The first part of visualizing data usefully is making sure the data you're working with is not terrible.Tags: expectations, formatting, missing data, outlier

  • Pinch-to-Zoom in Stock Charts — JS Chart Tips

    Updated: 2025-03-05 10:08:30
    Stock charts are designed to handle large volumes of time-based data, and smooth navigation is key to working with them effectively. One common need is zooming — whether to focus on a specific time range in greater detail or to get a broader view of the data. In our JavaScript stock charts, zooming works out […] The post Pinch-to-Zoom in Stock Charts — JS Chart Tips appeared first on AnyChart News.

  • Accelerating optimization over the space of probability measures

    Updated: 2025-03-03 11:55:43
    The acceleration of gradient-based optimization methods is a subject of significant practical and theoretical importance, particularly within machine learning applications. While much attention has been directed towards optimizing within Euclidean space, the need to optimize over spaces of probability measures in machine learning motivates the exploration of accelerated gradient methods in this context, too. To this end, we introduce a Hamiltonian-flow approach analogous to momentum-based approaches in Euclidean space. We demonstrate that, in the continuous-time setting, algorithms based on this approach can achieve convergence rates of arbitrarily high order. We complement our findings with numerical examples.

  • Bayesian Multi-Group Gaussian Process Models for Heterogeneous Group-Structured Data

    Updated: 2025-03-03 11:55:43
    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 Bayesian Multi-Group Gaussian Process Models for Heterogeneous Group-Structured Data Didong Li , Andrew Jones , Sudipto Banerjee , Barbara E . Engelhardt 26(30 1 34, 2025. Abstract Gaussian processes are pervasive in functional data analysis , machine learning , and spatial statistics for modeling complex dependencies . Scientific data are often heterogeneous in their inputs and contain multiple known discrete groups of samples thus , it is desirable to leverage the similarity among groups while accounting for heterogeneity across groups . We propose multi-group Gaussian processes MGGPs defined over

  • Orthogonal Bases for Equivariant Graph Learning with Provable k-WL Expressive Power

    Updated: 2025-03-03 11:55:43
    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 Orthogonal Bases for Equivariant Graph Learning with Provable k-WL Expressive Power Jia He , Maggie Cheng 26(29 1 35, 2025. Abstract Graph neural network GNN models have been widely used for learning graph-structured data . Due to the permutation-invariant requirement of graph learning tasks , a basic element in graph neural networks is the invariant and equivariant linear layers . Previous work Maron et al . 2019b provided a maximal collection of invariant and equivariant linear layers and a simple deep neural network model , called k-IGN , for graph data defined on k-tuples of nodes . It is shown

  • depyf: Open the Opaque Box of PyTorch Compiler for Machine Learning Researchers

    Updated: 2025-03-03 11:55:42
    : 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 depyf : Open the Opaque Box of PyTorch Compiler for Machine Learning Researchers Kaichao You , Runsheng Bai , Meng Cao , Jianmin Wang , Ion Stoica , Mingsheng Long 26(25 1 18, 2025. Abstract PyTorch 2.x introduces a compiler designed to accelerate deep learning programs . However , for machine learning researchers , fully leveraging the PyTorch compiler can be challenging due to its operation at the Python bytecode level , making it appear as an opaque box . To address this , we introduce depyf , a tool designed to demystify the inner workings of the PyTorch compiler . depyf decompiles the bytecode

  • Improving Graph Neural Networks on Multi-node Tasks with the Labeling Trick

    Updated: 2025-03-03 11:55:42
    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 Improving Graph Neural Networks on Multi-node Tasks with the Labeling Trick Xiyuan Wang , Pan Li , Muhan Zhang 26(23 1 44, 2025. Abstract In this paper , we study using graph neural networks GNNs for multi-node representation learning , where a representation for a set of more than one node such as a link is to be learned . Existing GNNs are mainly designed to learn single-node representations . When used for multi-node representation learning , a common practice is to directly aggregate the single-node representations obtained by a GNN . In this paper , we show a fundamental limitation of such an

  • Directed Cyclic Graphs for Simultaneous Discovery of Time-Lagged and Instantaneous Causality from Longitudinal Data Using Instrumental Variables

    Updated: 2025-03-03 11:55:42
    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 Directed Cyclic Graphs for Simultaneous Discovery of Time-Lagged and Instantaneous Causality from Longitudinal Data Using Instrumental Variables Wei Jin , Yang Ni , Amanda B . Spence , Leah H . Rubin , Yanxun Xu 26(22 1 62, 2025. Abstract We consider the problem of causal discovery from longitudinal observational data . We develop a novel framework that simultaneously discovers the time-lagged causality and the possibly cyclic instantaneous causality . Under common causal discovery assumptions , combined with additional instrumental information typically available in longitudinal data , we prove the

  • Bayesian Sparse Gaussian Mixture Model for Clustering in High Dimensions

    Updated: 2025-03-03 11:55:42
    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 Bayesian Sparse Gaussian Mixture Model for Clustering in High Dimensions Dapeng Yao , Fangzheng Xie , Yanxun Xu 26(21 1 50, 2025. Abstract We study the sparse high-dimensional Gaussian mixture model when the number of clusters is allowed to grow with the sample size . A minimax lower bound for parameter estimation is established , and we show that a constrained maximum likelihood estimator achieves the minimax lower bound . However , this optimization-based estimator is computationally intractable because the objective function is highly nonconvex and the feasible set involves discrete structures .

  • New Selection of Significant Data Visualization Examples — DataViz Weekly

    Updated: 2025-02-28 20:49:30
    Our JavaScript charting library, Qlik Sense extensions, and other products give you the flexibility to visualize data how, where, and when you need. But making a chart or map truly effective — whether for exploration or explanation — is an art of its own. That is why we run DataViz Weekly: to share a selection […] The post New Selection of Significant Data Visualization Examples — DataViz Weekly appeared first on AnyChart News.

  • Heading to Qlik Connect 2025 — Join Us There!

    Updated: 2025-02-25 07:19:10
    We are going to Qlik Connect 2025! 🚀 Not packing just yet — May is still a bit away. But we are getting ready and looking forward to meeting the amazing Qlik community in Orlando, with lots of great presentations and conversations ahead. We will be there with our Qlik Sense extensions, bringing something special […] The post Heading to Qlik Connect 2025 — Join Us There! appeared first on AnyChart News.

  • Recent Data Graphics in Focus — DataViz Weekly

    Updated: 2025-02-21 18:08:07
    Data 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 13:57:40
    We’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 20:41:46
    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 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 the

  • From 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 of

  • Estimating Network-Mediated Causal Effects via Principal Components Network Regression

    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 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 as

  • Selective Inference with Distributed Data

    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 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 with

  • An Axiomatic Definition of Hierarchical Clustering

    Updated: 2025-02-12 20:41:44
    In 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 20:41:43
    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 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 approach

  • DisC2o-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 for

  • Efficiently Escaping Saddle Points in Bilevel Optimization

    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 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 warm

  • Quadrant Chart with Custom Image Markers — JS Chart Tips

    Updated: 2025-02-11 09:14:28
    Displaying 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.

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