• A General Framework for the Analysis of Kernel-based Tests

    Updated: 2024-04-30 18:09:10
    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 General Framework for the Analysis of Kernel-based Tests Tamara Fernández , Nicolás Rivera 25(95 1 40, 2024. Abstract Kernel-based tests provide a simple yet effective framework that uses the theory of reproducing kernel Hilbert spaces to design non-parametric testing procedures . In this paper , we propose new theoretical tools that can be used to study the asymptotic behaviour of kernel-based tests in various data scenarios and in different testing problems . Unlike current approaches , our methods avoid working with U and V-statistics expansions that usually lead to lengthy and tedious

  • Causal-learn: Causal Discovery in Python

    Updated: 2024-04-30 18:09:10
    Causal discovery aims at revealing causal relations from observational data, which is a fundamental task in science and engineering. We describe causal-learn, an open-source Python library for causal discovery. This library focuses on bringing a comprehensive collection of causal discovery methods to both practitioners and researchers. It provides easy-to-use APIs for non-specialists, modular building blocks for developers, detailed documentation for learners, and comprehensive methods for all. Different from previous packages in R or Java, causal-learn is fully developed in Python, which could be more in tune with the recent preference shift in programming languages within related communities. The library is available at https://github.com/py-why/causal-learn.

  • On Sufficient Graphical Models

    Updated: 2024-04-30 18:09:10
    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

  • Random Forest Weighted Local Fr{{\'e}}chet Regression with Random Objects

    Updated: 2024-04-30 18:09:10
    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 Random Forest Weighted Local Fréchet Regression with Random Objects Rui Qiu , Zhou Yu , Ruoqing Zhu 25(107 1 69, 2024. Abstract Statistical analysis is increasingly confronted with complex data from metric spaces . Petersen and Müller 2019 established a general paradigm of Fréchet regression with complex metric space valued responses and Euclidean predictors . However , the local approach therein involves nonparametric kernel smoothing and suffers from the curse of dimensionality . To address this issue , we in this paper propose a novel random forest weighted local Fréchet regression paradigm . The

  • Effect-Invariant Mechanisms for Policy Generalization

    Updated: 2024-04-30 18:09:10
    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

  • Tangential Wasserstein Projections

    Updated: 2024-04-30 18:09:10
    We develop a notion of projections between sets of probability measures using the geometric properties of the $2$-Wasserstein space. In contrast to existing methods, it is designed for multivariate probability measures that need not be regular, and is computationally efficient to implement via regression. The idea is to work on tangent cones of the Wasserstein space using generalized geodesics. Its structure and computational properties make the method applicable in a variety of settings where probability measures need not be regular, from causal inference to the analysis of object data. An application to estimating causal effects yields a generalization of the synthetic controls method for systems with general heterogeneity described via multivariate probability measures.

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

    Updated: 2024-04-30 18:09:10
    : 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

  • Sparse Representer Theorems for Learning in Reproducing Kernel Banach Spaces

    Updated: 2024-04-30 18:09:10
    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 Representer Theorems for Learning in Reproducing Kernel Banach Spaces Rui Wang , Yuesheng Xu , Mingsong Yan 25(93 1 45, 2024. Abstract Sparsity of a learning solution is a desirable feature in machine learning . Certain reproducing kernel Banach spaces RKBSs are appropriate hypothesis spaces for sparse learning methods . The goal of this paper is to understand what kind of RKBSs can promote sparsity for learning solutions . We consider two typical learning models in an RKBS : the minimum norm interpolation MNI problem and the regularization problem . We first establish an explicit representer

  • ptwt - The PyTorch Wavelet Toolbox

    Updated: 2024-04-30 18:09:10
    The fast wavelet transform is an essential workhorse in signal processing. Wavelets are local in the spatial- or temporal- and the frequency-domain. This property enables frequency domain analysis while preserving some spatiotemporal information. Until recently, wavelets rarely appeared in the machine learning literature. We provide the PyTorch Wavelet Toolbox to make wavelet methods more accessible to the deep learning community. Our PyTorch Wavelet Toolbox is well documented. A pip package is installable with `pip install ptwt`.

  • Adam-family Methods for Nonsmooth Optimization with Convergence Guarantees

    Updated: 2024-04-30 18:09:10
    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: 2024-04-30 18:09:10
    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.

  • Unsupervised Anomaly Detection Algorithms on Real-world Data: How Many Do We Need?

    Updated: 2024-04-30 18:09:10
    : 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 Unsupervised Anomaly Detection Algorithms on Real-world Data : How Many Do We Need Roel Bouman , Zaharah Bukhsh , Tom Heskes 25(105 1 34, 2024. Abstract In this study we evaluate 33 unsupervised anomaly detection algorithms on 52 real-world multivariate tabular data sets , performing the largest comparison of unsupervised anomaly detection algorithms to date . On this collection of data sets , the EIF Extended Isolation Forest algorithm significantly outperforms the most other algorithms . Visualizing and then clustering the relative performance of the considered algorithms on all data sets , we

  • Data Thinning for Convolution-Closed Distributions

    Updated: 2024-04-30 18:09:10
    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 Data Thinning for Convolution-Closed Distributions Anna Neufeld , Ameer Dharamshi , Lucy L . Gao , Daniela Witten 25(57 1 35, 2024. Abstract We propose data thinning , an approach for splitting an observation into two or more independent parts that sum to the original observation , and that follow the same distribution as the original observation , up to a known scaling of a parameter . This very general proposal is applicable to any convolution-closed distribution , a class that includes the Gaussian , Poisson , negative binomial , gamma , and binomial distributions , among others . Data thinning

  • Sample-efficient Adversarial Imitation Learning

    Updated: 2024-04-30 18:09:10
    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

  • Exploration of the Search Space of Gaussian Graphical Models for Paired Data

    Updated: 2024-04-30 18:09:10
    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 Exploration of the Search Space of Gaussian Graphical Models for Paired Data Alberto Roverato , Dung Ngoc Nguyen 25(92 1 41, 2024. Abstract We consider the problem of learning a Gaussian graphical model in the case where the observations come from two dependent groups sharing the same variables . We focus on a family of coloured Gaussian graphical models specifically suited for the paired data problem . Commonly , graphical models are ordered by the submodel relationship so that the search space is a lattice , called the model inclusion lattice . We introduce a novel order between models , named the

  • Multi-class Probabilistic Bounds for Majority Vote Classifiers with Partially Labeled Data

    Updated: 2024-04-30 18:09:10
    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 Multi-class Probabilistic Bounds for Majority Vote Classifiers with Partially Labeled Data Vasilii Feofanov , Emilie Devijver , Massih-Reza Amini 25(104 1 47, 2024. Abstract In this paper , we propose a probabilistic framework for analyzing a multi-class majority vote classifier in the case where training data is partially labeled . First , we derive a multi-class transductive bound over the risk of the majority vote classifier , which is based on the classifier's vote distribution over each class . Then , we introduce a mislabeling error model to analyze the error of the majority vote classifier in

  • The good, the bad and the ugly sides of data augmentation: An implicit spectral regularization perspective

    Updated: 2024-04-30 18:09:10
    , : 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 The good , the bad and the ugly sides of data augmentation : An implicit spectral regularization perspective Chi-Heng Lin , Chiraag Kaushik , Eva L . Dyer , Vidya Muthukumar 25(91 1 85, 2024. Abstract Data augmentation DA is a powerful workhorse for bolstering performance in modern machine learning . Specific augmentations like translations and scaling in computer vision are traditionally believed to improve generalization by generating new artificial data from the same distribution . However , this traditional viewpoint does not explain the success of prevalent augmentations in modern machine

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

    Updated: 2024-04-30 18:09:10
    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

  • Functional Directed Acyclic Graphs

    Updated: 2024-04-30 18:09:10
    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 Functional Directed Acyclic Graphs Kuang-Yao Lee , Lexin Li , Bing Li 25(78 1 48, 2024. Abstract In this article , we introduce a new method to estimate a directed acyclic graph DAG from multivariate functional data . We build on the notion of faithfulness that relates a DAG with a set of conditional independences among the random functions . We develop two linear operators , the conditional covariance operator and the partial correlation operator , to characterize and evaluate the conditional independence . Based on these operators , we adapt and extend the PC-algorithm to estimate the functional

  • Information Processing Equalities and the Information–Risk Bridge

    Updated: 2024-04-30 18:09:10
    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 Information Processing Equalities and the Information–Risk Bridge Robert C . Williamson , Zac Cranko 25(103 1 53, 2024. Abstract We introduce two new classes of measures of information for statistical experiments which generalise and subsume φ-divergences , integral probability metrics , N-distances MMD and f,Γ divergences between two or more distributions . This enables us to derive a simple geometrical relationship between measures of information and the Bayes risk of a statistical decision problem , thus extending the variational φ-divergence representation to multiple distributions in an entirely

  • Invariant and Equivariant Reynolds Networks

    Updated: 2024-04-30 18:09:10
    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

  • Unlabeled Principal Component Analysis and Matrix Completion

    Updated: 2024-04-30 18:09:10
    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 Unlabeled Principal Component Analysis and Matrix Completion Yunzhen Yao , Liangzu Peng , Manolis C . Tsakiris 25(77 1 38, 2024. Abstract We introduce robust principal component analysis from a data matrix in which the entries of its columns have been corrupted by permutations , termed Unlabeled Principal Component Analysis UPCA Using algebraic geometry , we establish that UPCA is a well-defined algebraic problem since we prove that the only matrices of minimal rank that agree with the given data are row-permutations of the ground-truth matrix , arising as the unique solutions of a polynomial system

  • Personalized PCA: Decoupling Shared and Unique Features

    Updated: 2024-04-30 18:09:10
    : 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

  • Polygonal Unadjusted Langevin Algorithms: Creating stable and efficient adaptive algorithms for neural networks

    Updated: 2024-04-30 18:09:10
    : 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 Polygonal Unadjusted Langevin Algorithms : Creating stable and efficient adaptive algorithms for neural networks Dong-Young Lim , Sotirios Sabanis 25(53 1 52, 2024. Abstract We present a new class of Langevin-based algorithms , which overcomes many of the known shortcomings of popular adaptive optimizers that are currently used for the fine tuning of deep learning models . Its underpinning theory relies on recent advances of Euler-Krylov polygonal approximations for stochastic differential equations SDEs with monotone coefficients . As a result , it inherits the stability properties of tamed

  • Nonparametric Regression for 3D Point Cloud Learning

    Updated: 2024-04-30 18:09:10
    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 Nonparametric Regression for 3D Point Cloud Learning Xinyi Li , Shan Yu , Yueying Wang , Guannan Wang , Li Wang , Ming-Jun Lai 25(102 1 56, 2024. Abstract In recent years , there has been an exponentially increased amount of point clouds collected with irregular shapes in various areas . Motivated by the importance of solid modeling for point clouds , we develop a novel and efficient smoothing tool based on multivariate splines over the triangulation to extract the underlying signal and build up a 3D solid model from the point cloud . The proposed method can denoise or deblur the point cloud

  • Survival Kernets: Scalable and Interpretable Deep Kernel Survival Analysis with an Accuracy Guarantee

    Updated: 2024-04-30 18:09:10
    : 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 Survival Kernets : Scalable and Interpretable Deep Kernel Survival Analysis with an Accuracy Guarantee George H . Chen 25(40 1 78, 2024. Abstract Kernel survival analysis models estimate individual survival distributions with the help of a kernel function , which measures the similarity between any two data points . Such a kernel function can be learned using deep kernel survival models . In this paper , we present a new deep kernel survival model called a survival kernet , which scales to large datasets in a manner that is amenable to model interpretation and also theoretical analysis .

  • AMLB: an AutoML Benchmark

    Updated: 2024-04-30 18:09:10
    : 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 AMLB : an AutoML Benchmark Pieter Gijsbers , Marcos L . P . Bueno , Stefan Coors , Erin LeDell , Sébastien Poirier , Janek Thomas , Bernd Bischl , Joaquin Vanschoren 25(101 1 65, 2024. Abstract Comparing different AutoML frameworks is notoriously challenging and often done incorrectly . We introduce an open and extensible benchmark that follows best practices and avoids common mistakes when comparing AutoML frameworks . We conduct a thorough comparison of 9 well-known AutoML frameworks across 71 classification and 33 regression tasks . The differences between the AutoML frameworks are explored with

  • Spatial meshing for general Bayesian multivariate models

    Updated: 2024-04-30 18:09:10
    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 Spatial meshing for general Bayesian multivariate models Michele Peruzzi , David B . Dunson 25(87 1 49, 2024. Abstract Quantifying spatial and or temporal associations in multivariate geolocated data of different types is achievable via spatial random effects in a Bayesian hierarchical model , but severe computational bottlenecks arise when spatial dependence is encoded as a latent Gaussian process GP in the increasingly common large scale data settings on which we focus . The scenario worsens in non-Gaussian models because the reduced analytical tractability leads to additional hurdles to

  • Differentially private methods for managing model uncertainty in linear regression

    Updated: 2024-04-30 18:09:10
    In this article, we propose differentially private methods for hypothesis testing, model averaging, and model selection for normal linear models. We propose Bayesian methods based on mixtures of $g$-priors and non-Bayesian methods based on likelihood-ratio statistics and information criteria. The procedures are asymptotically consistent and straightforward to implement with existing software. We focus on practical issues such as adjusting critical values so that hypothesis tests have adequate type I error rates and quantifying the uncertainty introduced by the privacy-ensuring mechanisms.

  • Semi-supervised Inference for Block-wise Missing Data without Imputation

    Updated: 2024-04-30 18:09:10
    We consider statistical inference for single or low-dimensional parameters in a high-dimensional linear model under a semi-supervised setting, wherein the data are a combination of a labelled block-wise missing data set of a relatively small size and a large unlabelled data set. The proposed method utilises both labelled and unlabelled data without any imputation or removal of the missing observations. The asymptotic properties of the estimator are established under regularity conditions. Hypothesis testing for low-dimensional coefficients are also studied. Extensive simulations are conducted to examine the theoretical results. The method is evaluated on the Alzheimer’s Disease Neuroimaging Initiative data.

  • Optimal First-Order Algorithms as a Function of Inequalities

    Updated: 2024-04-30 18:09:10
    In this work, we present a novel algorithm design methodology that finds the optimal algorithm as a function of inequalities. Specifically, we restrict convergence analyses of algorithms to use a prespecified subset of inequalities, rather than utilizing all true inequalities, and find the optimal algorithm subject to this restriction. This methodology allows us to design algorithms with certain desired characteristics. As concrete demonstrations of this methodology, we find new state-of-the-art accelerated first-order gradient methods using randomized coordinate updates and backtracking line searches.

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

    Updated: 2024-04-30 18:09:10
    : 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

  • A Semi-parametric Estimation of Personalized Dose-response Function Using Instrumental Variables

    Updated: 2024-04-30 18:09:10
    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 Semi-parametric Estimation of Personalized Dose-response Function Using Instrumental Variables Wei Luo , Yeying Zhu , Xuekui Zhang , Lin Lin 25(86 1 38, 2024. Abstract In the application of instrumental variable analysis that conducts causal inference in the presence of unmeasured confounding , invalid instrumental variables and weak instrumental variables often exist which complicate the analysis . In this paper , we propose a model-free dimension reduction procedure to select the invalid instrumental variables and refine them into lower-dimensional linear combinations . The procedure also

  • Data Summarization via Bilevel Optimization

    Updated: 2024-04-30 18:09:10
    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 Data Summarization via Bilevel Optimization Zalán Borsos , Mojmír Mutný , Marco Tagliasacchi , Andreas Krause 25(73 1 53, 2024. Abstract The increasing availability of massive data sets poses various challenges for machine learning . Prominent among these is learning models under hardware or human resource constraints . In such resource-constrained settings , a simple yet powerful approach is operating on small subsets of the data . Coresets are weighted subsets of the data that provide approximation guarantees for the optimization objective . However , existing coreset constructions are highly

  • Sparse NMF with Archetypal Regularization: Computational and Robustness Properties

    Updated: 2024-04-30 18:09:10
    : 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

  • Scaling the Convex Barrier with Sparse Dual Algorithms

    Updated: 2024-04-30 18:09:10
    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 Scaling the Convex Barrier with Sparse Dual Algorithms Alessandro De Palma , Harkirat Singh Behl , Rudy Bunel , Philip H.S . Torr , M . Pawan Kumar 25(61 1 51, 2024. Abstract Tight and efficient neural network bounding is crucial to the scaling of neural network verification systems . Many efficient bounding algorithms have been presented recently , but they are often too loose to verify more challenging properties . This is due to the weakness of the employed relaxation , which is usually a linear program of size linear in the number of neurons . While a tighter linear relaxation for

  • Learning Non-Gaussian Graphical Models via Hessian Scores and Triangular Transport

    Updated: 2024-04-30 18:09:10
    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 Learning Non-Gaussian Graphical Models via Hessian Scores and Triangular Transport Ricardo Baptista , Youssef Marzouk , Rebecca Morrison , Olivier Zahm 25(85 1 46, 2024. Abstract Undirected probabilistic graphical models represent the conditional dependencies , or Markov properties , of a collection of random variables . Knowing the sparsity of such a graphical model is valuable for modeling multivariate distributions and for efficiently performing inference . While the problem of learning graph structure from data has been studied extensively for certain parametric families of distributions , most

  • Pareto Smoothed Importance Sampling

    Updated: 2024-04-30 18:09:10
    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 Pareto Smoothed Importance Sampling Aki Vehtari , Daniel Simpson , Andrew Gelman , Yuling Yao , Jonah Gabry 25(72 1 58, 2024. Abstract Importance weighting is a general way to adjust Monte Carlo integration to account for draws from the wrong distribution , but the resulting estimate can be highly variable when the importance ratios have a heavy right tail . This routinely occurs when there are aspects of the target distribution that are not well captured by the approximating distribution , in which case more stable estimates can be obtained by modifying extreme importance ratios . We present a new

  • Resource-Efficient Neural Networks for Embedded Systems

    Updated: 2024-04-30 18:09:10
    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 Resource-Efficient Neural Networks for Embedded Systems Wolfgang Roth , Günther Schindler , Bernhard Klein , Robert Peharz , Sebastian Tschiatschek , Holger Fröning , Franz Pernkopf , Zoubin Ghahramani 25(50 1 51, 2024. Abstract While machine learning is traditionally a resource intensive task , embedded systems , autonomous navigation , and the vision of the Internet of Things fuel the interest in resource-efficient approaches . These approaches aim for a carefully chosen trade-off between performance and resource consumption in terms of computation and energy . The development of such approaches is

  • The Cool Grey City of Data: inside the San Francisco Chronicle’s data team

    Updated: 2024-04-29 21:24:11
    NEW PODCAST EPISODE: Dan Kopf⁠ and ⁠Nami Sumida⁠ join Simon and Alberto to discuss how the SF Chronicle tells data stories, such as Sumida’s ⁠recent exploration of the city’s Japantown⁠ (sub required) and the WW2 internment that nearly destroyed it. The team discuss what makes the Bay Area such a rich source of data journalism … Continue reading →

  • Understanding Complex Topics Through Data Graphics — DataViz Weekly

    Updated: 2024-04-26 10:55:35
    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 » Data Visualization Weekly » Understanding Complex Topics Through Data Graphics — DataViz Weekly Understanding Complex Topics Through Data Graphics — DataViz Weekly April 26th , 2024 by AnyChart Team DataViz Weekly is here Today , we spotlight cool new projects that illuminate how complex topics can be presented and explored in a comprehendible and impactful manner with the help of appropriate data graphics . Here’s a quick look at what we’re covering

  • ✚ Visualization Tools and Learning Resources, April 2024 Roundup

    Updated: 2024-04-25 18:30:04
    , Membership Projects Courses Tutorials Newsletter Become a Member Log in Members Only Visualization Tools and Learning Resources , April 2024 Roundup April 25, 2024 Topic The Process roundup I’m Nathan Yau , and this is The Process the newsletter for FlowingData members that looks closer at how the charts get made . Every month I collect tools and resources to help you make better charts . Here’s the good stuff for . April 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

  • Climate change in your lifetime and the next

    Updated: 2024-04-23 07:42:40
    Membership Projects Courses Chart Types Newsletter Become a Member Log in Climate change in your lifetime and the next April 23, 2024 Topic Infographics climate change future Tardigrade One of the challenges of understanding the weight of climate change is that it’s a slow process . You likely won’t see the full effect in your lifetime . So , for The Tardigrade , Julia Janicki and Daisy Chung placed your timeline against others to show how your future and others’ futures differ Projections are from the Intergovernmental Panel on Climate Change and show the timeline up until you turn 100 years old . You might recognize the visual form , which is based on Ed Hawkins’ climate stripes Related Climate change in 2020 Fires in the west and climate change Climate change postcards from every

  • New Data-Driven Stories Worth Exploring — DataViz Weekly

    Updated: 2024-04-19 15:35:15
    This week’s DataViz Weekly showcases a quartet of compelling data-driven stories, each powered by the adept use of data visualization techniques. These narratives not only inform but inspire, perhaps sparking creative concepts for your upcoming projects. Here are the visual stories featured in this issue: Unraveling the hold of historical mortgage rates in the U.S. […] The post New Data-Driven Stories Worth Exploring — DataViz Weekly appeared first on AnyChart News.

  • Creating Interactive Network Graph Using JavaScript with Ease

    Updated: 2024-04-18 09:29:51
    Creating a vibrant network graph in JavaScript might seem like crafting a digital cosmos: intricate, fascinating, yet entirely within reach. Whether you’re aiming to illustrate the complex interconnections within a galaxy, a social network, or the internal hierarchy of a multinational corporation, network graphs serve as a powerful tool to bring data to life, revealing […] The post Creating Interactive Network Graph Using JavaScript with Ease appeared first on AnyChart News.

  • Exploring Eclipse Impact, Nature Access, Marital Ages, Voter Profiles — DataViz Weekly

    Updated: 2024-04-12 21:53:29
    , , , 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 » Data Visualization Weekly » Exploring Eclipse Impact , Nature Access , Marital Ages , Voter Profiles — DataViz Weekly Exploring Eclipse Impact , Nature Access , Marital Ages , Voter Profiles — DataViz Weekly April 12th , 2024 by AnyChart Team Welcome back to DataViz Weekly We†re thrilled to restart our series , presenting each Friday a carefully curated selection of recent data visualizations that have caught our eye from around the web .

  • New Decomposition Tree Features: Secondary Measure, Contribution, Variance

    Updated: 2024-04-03 20:05:00
    Listening to our community is foundational to our development efforts. In response to feedback from some of our customers, we’ve just added new features to the Decomposition Tree extension for Qlik Sense, aimed at enriching data insights. Here’s a quick overview. Read more at qlik.anychart.com » The post New Decomposition Tree Features: Secondary Measure, Contribution, Variance appeared first on AnyChart News.

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