• On Sufficient Graphical Models

    Updated: 2024-02-27 06:57:04
    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: 2024-02-27 06:57:04
    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: 2024-02-27 06:57:04
    : 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

  • Pygmtools: A Python Graph Matching Toolkit

    Updated: 2024-02-27 06:57:04
    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.

  • Sample-efficient Adversarial Imitation Learning

    Updated: 2024-02-27 06:57:04
    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

  • Modeling Random Networks with Heterogeneous Reciprocity

    Updated: 2024-02-27 06:57:04
    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: 2024-02-27 06:57:04
    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: 2024-02-27 06:57:04
    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

  • Decorrelated Variable Importance

    Updated: 2024-02-27 06:57:04
    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

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

    Updated: 2024-02-27 06:57:04
    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: 2024-02-27 06:57:04
    : 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: 2024-02-27 06:57:04
    : , , 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

  • Feeling Rested with Age

    Updated: 2024-02-23 08:30:48
    How much you sleep each night matters, but more importantly, it's about the quality and if you feel rested when you wake up.Tags: age, rest, sleep, well-being

  • Data-driven running journal

    Updated: 2024-02-21 08:15:54
    Membership Projects Courses Tutorials Newsletter Become a Member Log in Data-driven running journal February 21, 2024 Topic Self-surveillance K.K . Rebecca Lai marathon K.K . Rebecca Lai ran her first marathon . She recounts her training and the day of the event with a series of maps and charts It reads like a data-driven journal entry , which I am always up . for Related Telling stories in visual , data-driven essays An open-access journal for visualization research Building a happy life , interpreted through data Become a . member Support an independent site . Make great charts . See what you get Projects by FlowingData See All Who Makes More Money Someone mentioned that 400,000+ per year was commonplace in American households . That seemed like an odd . comment How People Like You Spend

  • ✚ Better or Less Bad

    Updated: 2024-02-15 19:30:51
    Membership Courses Tutorials Projects Newsletter Become a Member Log in Members Only Better or Less Bad February 15, 2024 Topic The Process criticism Welcome to The Process the newsletter for FlowingData members that looks closer at how the charts get made . I’m Nathan Yau . People like to judge charts by pointing out all the things that are wrong . It’s an easy thing to do , because no visualization can do everything well . But this approach can be limiting in . practice 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

  • Sonified: how we made a data album

    Updated: 2024-02-04 20:20:27
    One of the greatest features of data is that pretty much anything can represent it. It can be great journalism; it could be glass sculptures representing sea level rises, paint representing a battle with long COVID – and it can even be music. And now we have turned that music into a playlist album: Sonified. … Continue reading →

  • Introducing Updates to Waterfall Charts for Qlik Sense

    Updated: 2024-02-01 08:35:42
    Sales : 1 888 845-1211 USA or 44 20 7193 9444 Europe customer login Toggle navigation Products AnyChart AnyStock AnyMap AnyGantt Mobile Qlik Extension Features Resources Business Solutions Technical Integrations Chartopedia Tutorials Support Company About Us Customers Success Stories More Testimonials News Download Buy Now Search News » Big Data » Introducing Updates to Waterfall Charts for Qlik Sense Introducing Updates to Waterfall Charts for Qlik Sense February 1st , 2024 by AnyChart Team Unveiling the latest updates to our Waterfall Charts for Qlik Sense In line with our commitment to empowering users to do more with Qlik , we’ve just added a set of new helpful features and improvements to our Waterfall Advanced and Waterfall Classic extensions . These enhancements are

  • Qlik Webinar: Fewer Sheets, More Insights

    Updated: 2024-01-30 13:16:33
    Hey Qlikkies! Ever feel overwhelmed by complex datasets, endless sheets, and constant requests from business users? The solution is here — join our exclusive webinar on February 8th and meet the Decomposition Tree, a new rockstar chart in Qlik Sense that’s worth a dozen! Enabling users to slice and dice metrics as they please, this […] The post Qlik Webinar: Fewer Sheets, More Insights appeared first on AnyChart News.

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