• On Sufficient Graphical Models

    Updated: 2024-03-31 18:45:49
    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 On Sufficient Graphical Models Bing Li , Kyongwon Kim 25(17 1 64, 2024. Abstract We introduce a sufficient graphical model by applying the recently developed nonlinear sufficient dimension reduction techniques to the evaluation of conditional independence . The graphical model is nonparametric in nature , as it does not make distributional assumptions such as the Gaussian or copula Gaussian assumptions . However , unlike a fully nonparametric graphical model , which relies on the high-dimensional kernel to characterize conditional independence , our graphical model is based on conditional

  • Effect-Invariant Mechanisms for Policy Generalization

    Updated: 2024-03-31 18:45:49
    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 Effect-Invariant Mechanisms for Policy Generalization Sorawit Saengkyongam , Niklas Pfister , Predrag Klasnja , Susan Murphy , Jonas Peters 25(34 1 36, 2024. Abstract Policy learning is an important component of many real-world learning systems . A major challenge in policy learning is how to adapt efficiently to unseen environments or tasks . Recently , it has been suggested to exploit invariant conditional distributions to learn models that generalize better to unseen environments . However , assuming invariance of entire conditional distributions which we call full invariance may be too strong of

  • Localized Debiased Machine Learning: Efficient Inference on Quantile Treatment Effects and Beyond

    Updated: 2024-03-31 18:45:49
    : 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 Localized Debiased Machine Learning : Efficient Inference on Quantile Treatment Effects and Beyond Nathan Kallus , Xiaojie Mao , Masatoshi Uehara 25(16 1 59, 2024. Abstract We consider estimating a low-dimensional parameter in an estimating equation involving high-dimensional nuisance functions that depend on the target parameter as an input . A central example is the efficient estimating equation for the local quantile treatment effect L QTE in causal inference , which involves the covariate-conditional cumulative distribution function evaluated at the quantile to be estimated . Existing

  • Adam-family Methods for Nonsmooth Optimization with Convergence Guarantees

    Updated: 2024-03-31 18:45:49
    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 Adam-family Methods for Nonsmooth Optimization with Convergence Guarantees Nachuan Xiao , Xiaoyin Hu , Xin Liu , Kim-Chuan Toh 25(48 1 53, 2024. Abstract In this paper , we present a comprehensive study on the convergence properties of Adam-family methods for nonsmooth optimization , especially in the training of nonsmooth neural networks . We introduce a novel two-timescale framework that adopts a two-timescale updating scheme , and prove its convergence properties under mild assumptions . Our proposed framework encompasses various popular Adam-family methods , providing convergence guarantees for

  • Pygmtools: A Python Graph Matching Toolkit

    Updated: 2024-03-31 18:45:49
    Graph matching aims to find node-to-node matching among multiple graphs, which is a fundamental yet challenging problem. To facilitate graph matching in scientific research and industrial applications, pygmtools is released, which is a Python graph matching toolkit that implements a comprehensive collection of two-graph matching and multi-graph matching solvers, covering both learning-free solvers as well as learning-based neural graph matching solvers. Our implementation supports numerical backends including Numpy, PyTorch, Jittor, Paddle, runs on Windows, MacOS and Linux, and is friendly to install and configure. Comprehensive documentations covering beginner's guide, API reference and examples are available online. pygmtools is open-sourced under Mulan PSL v2 license.

  • Efficient Modality Selection in Multimodal Learning

    Updated: 2024-03-31 18:45:49
    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 Efficient Modality Selection in Multimodal Learning Yifei He , Runxiang Cheng , Gargi Balasubramaniam , Yao-Hung Hubert Tsai , Han Zhao 25(47 1 39, 2024. Abstract Multimodal learning aims to learn from data of different modalities by fusing information from heterogeneous sources . Although it is beneficial to learn from more modalities , it is often infeasible to use all available modalities under limited computational resources . Modeling with all available modalities can also be inefficient and unnecessary when information across input modalities overlaps . In this paper , we study the modality

  • Sample-efficient Adversarial Imitation Learning

    Updated: 2024-03-31 18:45:49
    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 Sample-efficient Adversarial Imitation Learning Dahuin Jung , Hyungyu Lee , Sungroh Yoon 25(31 1 32, 2024. Abstract Imitation learning , in which learning is performed by demonstration , has been studied and advanced for sequential decision-making tasks in which a reward function is not predefined . However , imitation learning methods still require numerous expert demonstration samples to successfully imitate an expert's behavior . To improve sample efficiency , we utilize self-supervised representation learning , which can generate vast training signals from the given data . In this study , we

  • A Multilabel Classification Framework for Approximate Nearest Neighbor Search

    Updated: 2024-03-31 18:45:49
    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 A Multilabel Classification Framework for Approximate Nearest Neighbor Search Ville Hyvönen , Elias Jääsaari , Teemu Roos 25(46 1 51, 2024. Abstract To learn partition-based index structures for approximate nearest neighbor ANN search , both supervised and unsupervised machine learning algorithms have been used . Existing supervised algorithms select all the points that belong to the same partition element as the query point as nearest neighbor candidates . Consequently , they formulate the learning task as finding a partition in which the nearest neighbors of a query point belong to the same

  • Multiple Descent in the Multiple Random Feature Model

    Updated: 2024-03-31 18:45:49
    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 Multiple Descent in the Multiple Random Feature Model Xuran Meng , Jianfeng Yao , Yuan Cao 25(44 1 49, 2024. Abstract Recent works have demonstrated a double descent phenomenon in over-parameterized learning . Although this phenomenon has been investigated by recent works , it has not been fully understood in theory . In this paper , we investigate the multiple descent phenomenon in a class of multi-component prediction models . We first consider a double random feature model DRFM concatenating two types of random features , and study the excess risk achieved by the DRFM in ridge regression . We

  • Modeling Random Networks with Heterogeneous Reciprocity

    Updated: 2024-03-31 18:45:49
    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 Modeling Random Networks with Heterogeneous Reciprocity Daniel Cirkovic , Tiandong Wang 25(10 1 40, 2024. Abstract Reciprocity , or the tendency of individuals to mirror behavior , is a key measure that describes information exchange in a social network . Users in social networks tend to engage in different levels of reciprocal behavior . Differences in such behavior may indicate the existence of communities that reciprocate links at varying rates . In this paper , we develop methodology to model the diverse reciprocal behavior in growing social networks . In particular , we present a preferential

  • Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees

    Updated: 2024-03-31 18:45:49
    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 Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees Alexander Terenin , David R . Burt , Artem Artemev , Seth Flaxman , Mark van der Wilk , Carl Edward Rasmussen , Hong Ge 25(26 1 36, 2024. Abstract Gaussian processes are frequently deployed as part of larger machine learning and decision-making systems , for instance in geospatial modeling , Bayesian optimization , or in latent Gaussian models . Within a system , the Gaussian process model needs to perform in a stable and reliable manner to ensure it interacts correctly with other parts of the system . In this work

  • On Efficient and Scalable Computation of the Nonparametric Maximum Likelihood Estimator in Mixture Models

    Updated: 2024-03-31 18:45:49
    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 On Efficient and Scalable Computation of the Nonparametric Maximum Likelihood Estimator in Mixture Models Yangjing Zhang , Ying Cui , Bodhisattva Sen , Kim-Chuan Toh 25(8 1 46, 2024. Abstract In this paper , we focus on the computation of the nonparametric maximum likelihood estimator NPMLE in multivariate mixture models . Our approach discretizes this infinite dimensional convex optimization problem by setting fixed support points for the NPMLE and optimizing over the mixing proportions . We propose an efficient and scalable semismooth Newton based augmented Lagrangian method ALM Our algorithm

  • Invariant and Equivariant Reynolds Networks

    Updated: 2024-03-31 18:45:49
    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 Invariant and Equivariant Reynolds Networks Akiyoshi Sannai , Makoto Kawano , Wataru Kumagai 25(42 1 36, 2024. Abstract Various data exhibit symmetry , including permutations in graphs and point clouds . Machine learning methods that utilize this symmetry have achieved considerable success . In this study , we explore learning models for data exhibiting group symmetry . Our focus is on transforming deep neural networks using Reynolds operators , which average over the group to convert a function into an invariant or equivariant form . While learning methods based on Reynolds operators are

  • Personalized PCA: Decoupling Shared and Unique Features

    Updated: 2024-03-31 18:45:49
    : 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 Personalized PCA : Decoupling Shared and Unique Features Naichen Shi , Raed Al Kontar 25(41 1 82, 2024. Abstract In this paper , we tackle a significant challenge in PCA : heterogeneity . When data are collected from different sources with heterogeneous trends while still sharing some congruency , it is critical to extract shared knowledge while retaining the unique features of each source . To this end , we propose personalized PCA PerPCA which uses mutually orthogonal global and local principal components to encode both unique and shared features . We show that , under mild conditions , both

  • Decorrelated Variable Importance

    Updated: 2024-03-31 18:45:49
    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 Decorrelated Variable Importance Isabella Verdinelli , Larry Wasserman 25(7 1 27, 2024. Abstract Because of the widespread use of black box prediction methods such as random forests and neural nets , there is renewed interest in developing methods for quantifying variable importance as part of the broader goal of interpretable prediction . A popular approach is to define a variable importance parameter known as LOCO Leave Out COvariates based on dropping covariates from a regression model . This is essentially a nonparametric version of R^2$ . This parameter is very general and can be estimated

  • Deep Nonparametric Estimation of Operators between Infinite Dimensional Spaces

    Updated: 2024-03-31 18:45:49
    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 Deep Nonparametric Estimation of Operators between Infinite Dimensional Spaces Hao Liu , Haizhao Yang , Minshuo Chen , Tuo Zhao , Wenjing Liao 25(24 1 67, 2024. Abstract Learning operators between infinitely dimensional spaces is an important learning task arising in machine learning , imaging science , mathematical modeling and simulations , etc . This paper studies the nonparametric estimation of Lipschitz operators using deep neural networks . Non-asymptotic upper bounds are derived for the generalization error of the empirical risk minimizer over a properly chosen network class . Under the

  • Model-Free Representation Learning and Exploration in Low-Rank MDPs

    Updated: 2024-03-31 18:45:49
    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 Model-Free Representation Learning and Exploration in Low-Rank MDPs Aditya Modi , Jinglin Chen , Akshay Krishnamurthy , Nan Jiang , Alekh Agarwal 25(6 1 76, 2024. Abstract The low-rank MDP has emerged as an important model for studying representation learning and exploration in reinforcement learning . With a known representation , several model-free exploration strategies exist . In contrast , all algorithms for the unknown representation setting are model-based , thereby requiring the ability to model the full dynamics . In this work , we present the first model-free representation learning

  • Pursuit of the Cluster Structure of Network Lasso: Recovery Condition and Non-convex Extension

    Updated: 2024-03-31 18:45:49
    : 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 Pursuit of the Cluster Structure of Network Lasso : Recovery Condition and Non-convex Extension Shotaro Yagishita , Jun-ya Gotoh 25(21 1 42, 2024. Abstract Network lasso NL for short is a technique for estimating models by simultaneously clustering data samples and fitting the models to them . It often succeeds in forming clusters thanks to the geometry of the sum of ell_2$ norm employed therein , but there may be limitations due to the convexity of the regularizer . This paper focuses on clustering generated by NL and strengthens it by creating a non-convex extension , called network trimmed lasso

  • Power of knockoff: The impact of ranking algorithm, augmented design, and symmetric statistic

    Updated: 2024-03-31 18:45:49
    : , , 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 Power of knockoff : The impact of ranking algorithm , augmented design , and symmetric statistic Zheng Tracy Ke , Jun S . Liu , Yucong Ma 25(3 1 67, 2024. Abstract The knockoff filter is a recent false discovery rate FDR control method for high-dimensional linear models . We point out that knockoff has three key components : ranking algorithm , augmented design , and symmetric statistic , and each component admits multiple choices . By considering various combinations of the three components , we obtain a collection of variants of knockoff . All these variants guarantee finite-sample FDR

  • Sparse NMF with Archetypal Regularization: Computational and Robustness Properties

    Updated: 2024-03-31 18:45:49
    : 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 Sparse NMF with Archetypal Regularization : Computational and Robustness Properties Kayhan Behdin , Rahul Mazumder 25(36 1 62, 2024. Abstract We consider the problem of sparse nonnegative matrix factorization NMF using archetypal regularization . The goal is to represent a collection of data points as nonnegative linear combinations of a few nonnegative sparse factors with appealing geometric properties , arising from the use of archetypal regularization . We generalize the notion of robustness studied in Javadi and Montanari 2019 without sparsity to the notions of a strong robustness that implies

  • ✚ Visualization Tools and Learning Resources, March 2024 Roundup

    Updated: 2024-03-28 18:30:39
    , Membership Projects Courses Tutorials Newsletter Become a Member Log in Members Only Visualization Tools and Learning Resources , March 2024 Roundup March 28, 2024 Topic The Process roundup I collect visualization tools and learning resources and then round them up at the end of each month . Here’s the good stuff for . March To access this issue of The Process , you must be a . member If you are already a member , log in here See What You Get The Process is a weekly newsletter on how visualization tools , rules , and guidelines work in practice . I publish every Thursday . Get it in your inbox or read it on FlowingData . You also gain unlimited access to hundreds of hours worth of step-by-step visualization courses and tutorials which will help you make sense of data for insight and

  • Interactive timeline of notable people throughout history

    Updated: 2024-03-27 07:30:28
    Membership Projects Courses Tutorials Newsletter Become a Member Log in Interactive timeline of notable people throughout history March 27, 2024 Topic Infographics history Jan Willem Tulp timeline This is a fun project by Jan Willem Tulp Based on data from a cross-verified database of notable people Tulp scrolls through history to show when these people enter and leave the world based on their age . Start in 3500 BC and scroll from . there Related Most notable person , everywhere in the world Mapping the boundaries of history Visual history of Yahoo Pipes Become a . member Support an independent site . Make great charts . See what you get Projects by FlowingData See All Daily Routine , 2020 After looking at how much time we spent on daily activities in 2020, let’s look at when we spent our

  • Visualizing the statistical connections behind ChatGPT

    Updated: 2024-03-26 07:12:17
    Membership Projects Courses Tutorials Newsletter Become a Member Log in Visualizing the statistical connections behind ChatGPT March 26, 2024 Topic Network Visualization AI ChatGPT Santiago Ortiz To gain a better understanding of how ChatGPT works under the hood , Santiago Ortiz repeatedly passed the prompt Intelligence is” to the chatbot . Then he visualized the statistical paths to get to a response using a 3-D network . If you squint , the network kind of looks like a computer’s brain Related Friend simulation system , with ChatGPT Visualizing 16th century letter correspondence of the Tudor government An AI chatbot to talk to the dead Become a . member Support an independent site . Make great charts . See what you get Projects by FlowingData See All A Day in the Life of Americans I

  • How to Create Decomposition Tree in Qlik Sense: Transform Root Cause Analysis [Video Tutorial]

    Updated: 2024-03-25 19:37:29
    An important note for Qlikkies: Are you ready to elevate your dashboards? Dive into our latest Qlik tutorial — discover the power of the Decomposition Tree and learn how to create it in Qlik Sense in about no time! Learn more and watch the tutorial at qlik.anychart.com » The post How to Create Decomposition Tree in Qlik Sense: Transform Root Cause Analysis [Video Tutorial] appeared first on AnyChart News.

  • ✚ Misleading or Not? A Chart About How Couples Meet

    Updated: 2024-03-21 18:30:46
    Membership Projects Courses Tutorials Newsletter Become a Member Log in Members Only Misleading or Not A Chart About How Couples Meet March 21, 2024 Topic The Process misleading If a chart is seen by enough people , someone will call it misleading . Many will agree that it is misleading , and therefore , the chart is terrible and the maker is up to no good . This is the law of the land . There are no . exceptions To access this issue of The Process , you must be a . member If you are already a member , log in here See What You Get The Process is a weekly newsletter on how visualization tools , rules , and guidelines work in practice . I publish every Thursday . Get it in your inbox or read it on FlowingData . You also gain unlimited access to hundreds of hours worth of step-by-step

  • Birding and data visualization

    Updated: 2024-03-21 15:46:22
    Jer Thorp has combined birding and data visualization into a unique course called…Tags: birds, Jer Thorp, learning

  • Free Qlik Webinar: Qlik Sense Revolution — One Sheet Is Enough

    Updated: 2024-03-06 22:31:58
    Hey Qlikkies! Still burying insights under layers of charts and sheets? It’s time to break free from wrestling with complex datasets and win the hearts of your business users! Join our exclusive webinar and meet the Decomposition Tree — a new powerhouse visualization that’s taken Power BI by storm and now graces Qlik Sense. This […] The post Free Qlik Webinar: Qlik Sense Revolution — One Sheet Is Enough appeared first on AnyChart News.

  • Creating Calendar Charts with JavaScript

    Updated: 2024-03-04 08:57:11
    Building an interactive calendar chart from scratch may initially seem daunting and time-consuming. However, I'm here to show you it's not only manageable but straightforward once you know the steps. Whether you're developing a scheduling application, tracking events, or looking to enhance your web page with a sleek calendar graphic, this tutorial is designed specifically […] The post Creating Calendar Charts with JavaScript appeared first on AnyChart News.

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