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This paper introduces a new methodology based on the field of Topological Data Analysis for detecting structural anomalies in dependent sequences of complex data. A motivating example is that of multivariate time series, for which our method allows to detect global changes in the dependence structure between channels. The proposed approach is lean enough to handle large scale data sets, and extensive numerical experiments back the intuition that it is more suitable for detecting global changes of correlation structures than existing methods. Some theoretical guarantees for quantization algorithms based on dependent sequences are also provided.
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: 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 Aequitas Flow : Streamlining Fair ML Experimentation Sérgio Jesus , Pedro Saleiro , Inês Oliveira e Silva , Beatriz M . Jorge , Rita P . Ribeiro , João Gama , Pedro Bizarro , Rayid Ghani 25(354 1 7, 2024. Abstract Aequitas Flow is an open-source framework and toolkit for end-to-end Fair Machine Learning ML experimentation , and benchmarking in Python . This package fills integration gaps that exist in other fair ML packages . In addition to the existing audit capabilities in Aequitas , the Aequitas Flow module provides a pipeline for fairness-aware model training , hyperparameter optimization , and
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: 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 TopoX : A Suite of Python Packages for Machine Learning on Topological Domains Mustafa Hajij , Mathilde Papillon , Florian Frantzen , Jens Agerberg , Ibrahem AlJabea , Rubén Ballester , Claudio Battiloro , Guillermo Bernárdez , Tolga Birdal , Aiden Brent , Peter Chin , Sergio Escalera , Simone Fiorellino , Odin Hoff Gardaa , Gurusankar Gopalakrishnan , Devendra Govil , Josef Hoppe , Maneel Reddy Karri , Jude Khouja , Manuel Lecha , Neal Livesay , Jan Meißner , Soham Mukherjee , Alexander Nikitin , Theodore Papamarkou , Jaro Prílepok , Karthikeyan Natesan Ramamurthy , Paul Rosen , Aldo Guzmán-Sáenz
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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 DAG-Informed Structure Learning from Multi-Dimensional Point Processes Chunming Zhang , Muhong Gao , Shengji Jia 25(352 1 56, 2024. Abstract Motivated by inferring causal relationships among neurons using ensemble spike train data , this paper introduces a new technique for learning the structure of a directed acyclic graph DAG within a large network of events , applicable to diverse multi-dimensional temporal point process MuTPP data . At the core of MuTPP lie the conditional intensity functions , for which we construct a generative model parameterized by the graph parameters of a DAG and develop an
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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 Line Graph Vietoris-Rips Persistence Diagram for Topological Graph Representation Learning Jaesun Shin , Eunjoo Jeon , Taewon Cho , Namkyeong Cho , Youngjune Gwon 25(362 1 36, 2024. Abstract While message passing graph neural networks result in informative node embeddings , they may suffer from describing the topological properties of graphs . To this end , node filtration has been widely used as an attempt to obtain the topological information of a graph using persistence diagrams . However , these attempts have faced the problem of losing node embedding information , which in turn prevents them
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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 Optimal Decision Tree and Adaptive Submodular Ranking with Noisy Outcomes Su Jia , Fatemeh Navidi , Viswanath Nagarajan , R . Ravi 25(382 1 42, 2024. Abstract In pool-based active learning , the learner is given an unlabeled data set and aims to efficiently learn the unknown hypothesis by querying the labels of the data points . This can be formulated as the classical Optimal Decision Tree ODT problem : Given a set of tests , a set of hypotheses , and an outcome for each pair of test and hypothesis , our objective is to find a low-cost testing procedure i.e . decision tree that identifies the true
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Motivated by several examples, we consider a general framework of learning with linear loss functions. In this context, we provide excess risk and estimation bounds that hold with large probability for four estimators: ERM, minmax MOM and their regularized versions. These general bounds are applied for the problem of robustness in sparse PCA. In particular, we improve the state of the art result for this this problems, obtain results under weak moment assumptions as well as for adversarial contaminated data.
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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 Causal Discovery with Generalized Linear Models through Peeling Algorithms Minjie Wang , Xiaotong Shen , Wei Pan 25(310 1 49, 2024. Abstract This article presents a novel method for causal discovery with generalized structural equation models suited for analyzing diverse types of outcomes , including discrete , continuous , and mixed data . Causal discovery often faces challenges due to unmeasured confounders that hinder the identification of causal relationships . The proposed approach addresses this issue by developing two peeling algorithms bottom-up and top-down to ascertain causal relationships
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: 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 PROMISE : Preconditioned Stochastic Optimization Methods by Incorporating Scalable Curvature Estimates Zachary Frangella , Pratik Rathore , Shipu Zhao , Madeleine Udell 25(346 1 57, 2024. Abstract Ill-conditioned problems are ubiquitous in large-scale machine learning : as a data set grows to include more and more features correlated with the labels , the condition number increases . Yet traditional stochastic gradient methods converge slowly on these ill-conditioned problems , even with careful hyperparameter tuning . This paper introduces PROMISE Preconditioned Stochastic Optimization Methods by
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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 Robust Principal Component Analysis using Density Power Divergence Subhrajyoty Roy , Ayanendranath Basu , Abhik Ghosh 25(324 1 40, 2024. Abstract Principal component analysis PCA is a widely employed statistical tool used primarily for dimensionality reduction . However , it is known to be adversely affected by the presence of outlying observations in the sample , which is quite common . Robust PCA methods using M-estimators have theoretical benefits , but their robustness drop substantially for high dimensional data . On the other end of the spectrum , robust PCA algorithms solving principal
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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 Graphical Dirichlet Process for Clustering Non-Exchangeable Grouped Data Arhit Chakrabarti , Yang Ni , Ellen Ruth A . Morris , Michael L . Salinas , Robert S . Chapkin , Bani K . Mallick 25(323 1 56, 2024. Abstract We consider the problem of clustering grouped data with possibly non-exchangeable groups whose dependencies can be characterized by a known directed acyclic graph . To allow the sharing of clusters among the non-exchangeable groups , we propose a Bayesian nonparametric approach , termed graphical Dirichlet process , that jointly models the dependent group-specific random measures by
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PGMax is an open-source Python/ JAX package for (a) easily specifying discrete Probabilistic Graphical Models (PGMs) as factor graphs; and (b) automatically running efficient and scalable differentiable Loopy Belief Propagation (LBP). PGMax supports general factor graphs with tractable factors, and leverages modern accelerators like GPUs for inference. Compared with alternative libraries, PGMax obtains higher-quality inference results with up to three orders-of-magnitude inference time speedups. PGMax interacts seamlessly with the growing JAX ecosystem, opening up new research possibilities. Our source code, examples and documentation are available at https://github.com/google-deepmind/PGMax
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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 Distributed Kernel-Driven Data Clustering Ioannis Schizas 25(359 1 39, 2024. Abstract A novel fully distributed joint kernel learning and clustering framework is derived which is capable of determining clustering configurations in an unsupervised manner . Semidefinite programming is utilized to quantify closeness of candidate kernel similarity matrices to a block diagonal structure of certain rank . Utilizing difference of convex functions and block coordinate descent a recursive algorithm is derived that determines jointly a proper kernel similarity matrix and clustering factors . Reformulating the
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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 minimax optimal approach to high-dimensional double sparse linear regression Yanhang Zhang , Zhifan Li , Shixiang Liu , Jianxin Yin 25(369 1 66, 2024. Abstract In this paper , we focus our attention on the high-dimensional double sparse linear regression , that is , a combination of element-wise and group-wise sparsity . To address this problem , we propose an IHT-style iterative hard thresholding procedure that dynamically updates the threshold at each step . We establish the matching upper and lower bounds for parameter estimation , showing the optimality of our proposal in the minimax sense .
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We study statistical estimation and inference for the ranking problems based on pairwise comparisons with additional covariate information. In specific, in this paper, we study a Covariate-Assisted Ranking Estimation (CARE) model in a systematic way, that extends the well-known Bradley-Terry-Luce (BTL) model by incorporating the covariate information. We impose natural identifiability conditions, derive the statistical rates for the MLE under a sparse comparison graph, and obtain its asymptotic distribution. Moreover, we validate our theoretical results through large-scale numerical studies.
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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 Robust Spectral Clustering with Rank Statistics Joshua Cape , Xianshi Yu , Jonquil Z . Liao 25(398 1 81, 2024. Abstract This paper analyzes the statistical performance of a robust spectral clustering method for latent structure recovery in noisy data matrices . We consider eigenvector-based clustering applied to a matrix of nonparametric rank statistics that is derived entrywise from the raw , original data matrix . This approach is robust in the sense that , unlike traditional spectral clustering procedures , it can provably recover population-level latent block structure even when the observed data
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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 Localisation of Regularised and Multiview Support Vector Machine Learning Aurelian Gheondea , Cankat Tilki 25(368 1 47, 2024. Abstract We prove some representer theorems for a localised version of a semisupervised , manifold regularised and multiview support vector machine learning problem introduced by H.Q . Minh , L . Bazzani , and V . Murino , Journal of Machine Learning Research , 17(2016 1-72, that involves operator valued positive semidefinite kernels and their reproducing kernel Hilbert spaces . The results concern general cases when convex or nonconvex lossfunctions and finite or infinite
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: , , 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 Random Projection Approach to Personalized Federated Learning : Enhancing Communication Efficiency , Robustness , and Fairness Yuze Han , Xiang Li , Shiyun Lin , Zhihua Zhang 25(380 1 88, 2024. Abstract Personalized Federated Learning FL faces many challenges such as expensive communication costs , training-time adversarial attacks , and performance unfairness across devices . Recent developments witness a trade-off between a reference model and local models to achieve personalization . Following the avenue , we propose a personalized FL method toward the three goals . When it is time to
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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 Empirical Design in Reinforcement Learning Andrew Patterson , Samuel Neumann , Martha White , Adam White 25(318 1 63, 2024. Abstract Empirical design in reinforcement learning is no small task . Running good experiments requires attention to detail and at times significant computational resources . While compute resources available per dollar have continued to grow rapidly , so have the scale of typical experiments in reinforcement learning . It is now common to benchmark agents with millions of parameters against dozens of tasks , each using the equivalent of 30 days of experience . The scale of
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: 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 Dynamic Mechanisms in Unknown Environments : A Reinforcement Learning Approach Shuang Qiu , Boxiang Lyu , Qinglin Meng , Zhaoran Wang , Zhuoran Yang , Michael I . Jordan 25(397 1 73, 2024. Abstract Dynamic mechanism design studies how mechanism designers should allocate resources among agents in a time-varying environment . We consider the problem where the agents interact with the mechanism designer according to an unknown Markov Decision Process MDP where agent rewards and the mechanism designer's state evolve according to an episodic MDP with unknown reward functions and transition
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: 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 Structural Learning with Parametric Marginals for Count Data : An Application to Microbiota Systems Veronica Vinciotti , Pariya Behrouzi , Reza Mohammadi 25(339 1 26, 2024. Abstract High dimensional and heterogeneous count data are collected in various applied fields . In this paper , we look closely at high-resolution sequencing data on the microbiome , which have enabled researchers to study the genomes of entire microbial communities . Revealing the underlying interactions between these communities is of vital importance to learn how microbes influence human health . To perform
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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 Pure Differential Privacy for Functional Summaries with a Laplace-like Process Haotian Lin , Matthew Reimherr 25(305 1 50, 2024. Abstract Many existing mechanisms for achieving differential privacy DP on infinite-dimensional functional summaries typically involve embedding these functional summaries into finite-dimensional subspaces and applying traditional multivariate DP techniques . These mechanisms generally treat each dimension uniformly and struggle with complex , structured summaries . This work introduces a novel mechanism to achieve pure DP for functional summaries in a separable
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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 Transfer learning for tensor Gaussian graphical models Mingyang Ren , Yaoming Zhen , Junhui Wang 25(396 1 40, 2024. Abstract Tensor Gaussian graphical models GGMs interpreting conditional independence structures within tensor data , have important applications in numerous areas . Yet , the available tensor data in one single study is often limited due to high acquisition costs . Although relevant studies can provide additional data , it remains an open question how to pool such heterogeneous data . In this paper , we propose a transfer learning framework for tensor GGMs , which takes full advantage
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: 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 Recovery With Multiple Data Streams : An Adaptive Sequential Testing Approach Weinan Wang , Bowen Gang , Wenguang Sun 25(304 1 59, 2024. Abstract Multistage design has been utilized across a variety of scientific fields , enabling the adaptive allocation of sensing resources to effectively eliminate null locations and localize signals . We present a decision-theoretic framework for multi-stage adaptive testing that minimizes the total number of measurements while ensuring pre-specified constraints on both the false positive rate FPR and the missed discovery rate MDR Our method , SMART ,
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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 Consistent Multiclass Algorithms for Complex Metrics and Constraints Harikrishna Narasimhan , Harish G . Ramaswamy , Shiv Kumar Tavker , Drona Khurana , Praneeth Netrapalli , Shivani Agarwal 25(367 1 81, 2024. Abstract We present consistent algorithms for multiclass learning with complex performance metrics and constraints , where the objective and constraints are defined by arbitrary functions of the confusion matrix . This setting includes many common performance metrics such as the multiclass G-mean and micro F-measure , and constraints such as those on the classifier's precision and recall and
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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 Inference on High-dimensional Single-index Models with Streaming Data Dongxiao Han , Jinhan Xie , Jin Liu , Liuquan Sun , Jian Huang , Bei Jiang , Linglong Kong 25(337 1 68, 2024. Abstract Traditional statistical methods are faced with new challenges due to streaming data . The major challenge is the rapidly growing volume and velocity of data , which makes storing such huge data sets in memory impossible . The paper presents an online inference framework for regression parameters in high-dimensional semiparametric single-index models with unknown link functions . The proposed online procedure
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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 Instrumental Variable Value Iteration for Causal Offline Reinforcement Learning Luofeng Liao , Zuyue Fu , Zhuoran Yang , Yixin Wang , Dingli Ma , Mladen Kolar , Zhaoran Wang 25(303 1 56, 2024. Abstract In offline reinforcement learning RL an optimal policy is learned solely from a priori collected observational data . However , in observational data , actions are often confounded by unobserved variables . Instrumental variables IVs in the context of RL , are the variables whose influence on the state variables is all mediated by the action . When a valid instrument is present , we can recover the
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: , , , 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 ENNS : Variable Selection , Regression , Classification , and Deep Neural Network for High-Dimensional Data Kaixu Yang , Arkaprabha Ganguli , Tapabrata Maiti 25(335 1 45, 2024. Abstract High-dimensional , low-sample-size HDLSS data have been attracting people's attention for a long time . Many studies have proposed different approaches to dealing with this situation , among which variable selection is a significant idea . However , neural networks have been used to model complicated relationships . This paper discusses current variable selection techniques with neural networks . We showed
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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 Gaussian DAGs from Network Data Hangjian Li , Oscar Hernan Madrid Padilla , Qing Zhou 25(377 1 52, 2024. Abstract Structural learning of directed acyclic graphs DAGs or Bayesian networks has been studied extensively under the assumption that the data are independent . We propose a new Gaussian DAG model for dependent data which assumes the observations are correlated according to an undirected network . Under this model , we develop a method to estimate the DAG structure given a topological ordering of the nodes . The proposed method jointly estimates the Bayesian network and the
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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-Efficient Policy Evaluation Through Behavior Policy Search Josiah P . Hanna , Yash Chandak , Philip S . Thomas , Martha White , Peter Stone , Scott Niekum 25(313 1 58, 2024. Abstract We consider the task of evaluating a policy for a Markov decision process MDP The standard unbiased technique for evaluating a policy is to deploy the policy and observe its performance . We show that the data collected from deploying a different policy , commonly called the behavior policy , can be used to produce unbiased estimates with lower mean squared error than this standard technique . We derive an analytic
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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 Fréchet Random Forests for Metric Space Valued Regression with Non Euclidean Predictors Louis Capitaine , Jérémie Bigot , Rodolphe Thiébaut , Robin Genuer 25(355 1 41, 2024. Abstract Random forests are a statistical learning method widely used in many areas of scientific research because of its ability to learn complex relationships between input and output variables and also its capacity to handle high-dimensional data . However , current random forest approaches are not flexible enough to handle heterogeneous data such as curves , images and shapes . In this paper , we introduce Fréchet trees and
Updated: 2025-01-30 19:30:21
Here are things you can use, poke at, and learn from that bubbled up this past month.Tags: roundup
Updated: 2025-01-30 10:06:39
Grouping data points into appropriate time intervals is essential for making stock charts more readable and insightful. While raw data may be collected at a high frequency, such as every minute, financial analysts and traders often need to view broader trends, where grouping data into 5-minute, 15-minute, 1-hour, 1-day, and other time frames can provide […]
The post Stock Chart with Custom Time Intervals — JS Chart Tips appeared first on AnyChart News.
Updated: 2025-01-24 23:55:36
Welcome to the first regular edition of DataViz Weekly in 2025! Last Friday, we wrapped up the year with a special Best Data Visualizations of 2024 post. Now we’re back to our usual format, highlighting some of the most compelling new visuals that caught our attention recently. Here are the first featured projects of the […]
The post Compelling Fresh Data Visualizations Not to Miss — DataViz Weekly appeared first on AnyChart News.
Updated: 2025-01-09 10:20:21
Another remarkable year is officially in the books! Here at AnyChart, 2024 was a truly rewarding journey marked by innovation and impact: Named Best Tech in Data Analytics & Visualization — once again! Enhanced our JavaScript charting library with exciting new features and tweaks, particularly in Timeline, Waterfall, and Circle Packing charts — plus multiple […]
The post Reflecting on 2024 and Embracing 2025 — Happy New Year! appeared first on AnyChart News.
