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Tag: algorithm

  • The Greedy Boruta Algorithm: Faster Feature Selection Without Sacrificing Recall

    The Greedy Boruta Algorithm: Faster Feature Selection Without Sacrificing Recall A modification to the Boruta algorithm that dramatically reduces computation while maintaining high sensitivity The post The Greedy Boruta Algorithm: Faster Feature Selection Without Sacrificing Recall appeared first on Towards Data Science. Nicolas Vana Go to original source

  • Instance-Optimal Matrix Multiplicative Weight Update and Its Quantum Applications

    Instance-Optimal Matrix Multiplicative Weight Update and Its Quantum Applications arXiv:2509.08911v1 Announce Type: cross Abstract: The Matrix Multiplicative Weight Update (MMWU) is a seminal online learning algorithm with numerous applications. Applied to the matrix version of the Learning from Expert Advice (LEA) problem on the $d$-dimensional spectraplex, it is well known that MMWU achieves the minimax-optimal…

  • The Hungarian Algorithm and Its Applications in Computer Vision

    The Hungarian Algorithm and Its Applications in Computer Vision Introduction Multi-object tracking (MOT) is a task in which an algorithm must detect and track multiple objects in a video. Most known algorithms are based on using simple detectors (e.g. YOLO) designed for processing individual images. The overall method involves separately using a detector on consecutive video…

  • Underdamped Langevin MCMC with third order convergence

    Underdamped Langevin MCMC with third order convergence arXiv:2508.16485v1 Announce Type: new Abstract: In this paper, we propose a new numerical method for the underdamped Langevin diffusion (ULD) and present a non-asymptotic analysis of its sampling error in the 2-Wasserstein distance when the $d$-dimensional target distribution $p(x)propto e^{-f(x)}$ is strongly log-concave and has varying degrees of…

  • Dijkstra defeated: New Shortest Path Algorithm revealed

    Dijkstra defeated: New Shortest Path Algorithm revealed Dijkstra, the goto shortest path algorithm (time complexity nlogn) has now been outperformed by a new algorithm by top Chinese University which looks like a hybrid of bellman ford+ dijsktra algorithm. Paper : https://arxiv.org/abs/2504.17033 Algorithm explained with example : https://youtu.be/rXFtoXzZTF8?si=OiB6luMslndUbTrz submitted by /u/Technical-Love-8479 [link] [comments] /u/Technical-Love-8479 Go to…

  • An Iterative Algorithm for Differentially Private $k$-PCA with Adaptive Noise

    An Iterative Algorithm for Differentially Private $k$-PCA with Adaptive Noise arXiv:2508.10879v1 Announce Type: new Abstract: Given $n$ i.i.d. random matrices $A_i in mathbb{R}^{d times d}$ that share a common expectation $Sigma$, the objective of Differentially Private Stochastic PCA is to identify a subspace of dimension $k$ that captures the largest variance directions of $Sigma$, while…

  • Choosing the Better Bandit Algorithm under Data Sharing: When Do A/B Experiments Work?

    Choosing the Better Bandit Algorithm under Data Sharing: When Do A/B Experiments Work? arXiv:2507.11891v1 Announce Type: new Abstract: We study A/B experiments that are designed to compare the performance of two recommendation algorithms. Prior work has shown that the standard difference-in-means estimator is biased in estimating the global treatment effect (GTE) due to a particular…

  • Build Algorithm-Agnostic ML Pipelines in a Breeze

    Build Algorithm-Agnostic ML Pipelines in a Breeze The framework is now an open-source Python package for streamlined ML workflows The post Build Algorithm-Agnostic ML Pipelines in a Breeze appeared first on Towards Data Science. Mena Wang Go to original source

  • Adaptive Iterative Soft-Thresholding Algorithm with the Median Absolute Deviation

    Adaptive Iterative Soft-Thresholding Algorithm with the Median Absolute Deviation arXiv:2507.02084v1 Announce Type: new Abstract: The adaptive Iterative Soft-Thresholding Algorithm (ISTA) has been a popular algorithm for finding a desirable solution to the LASSO problem without explicitly tuning the regularization parameter $lambda$. Despite that the adaptive ISTA is a successful practical algorithm, few theoretical results exist.…

  • Modification of a Numerical Method Using FIR Filters in a Time-dependent SIR Model for COVID-19

    Modification of a Numerical Method Using FIR Filters in a Time-dependent SIR Model for COVID-19 arXiv:2506.21739v1 Announce Type: new Abstract: Authors Yi-Cheng Chen, Ping-En Lu, Cheng-Shang Chang, and Tzu-Hsuan Liu use the Finite Impulse Response (FIR) linear system filtering method to track and predict the number of people infected and recovered from COVID-19, in a…

  • An Observation on Lloyd’s k-Means Algorithm in High Dimensions

    An Observation on Lloyd’s k-Means Algorithm in High Dimensions arXiv:2506.14952v1 Announce Type: new Abstract: Clustering and estimating cluster means are core problems in statistics and machine learning, with k-means and Expectation Maximization (EM) being two widely used algorithms. In this work, we provide a theoretical explanation for the failure of k-means in high-dimensional settings with…

  • Can SGD Select Good Fishermen? Local Convergence under Self-Selection Biases and Beyond

    Can SGD Select Good Fishermen? Local Convergence under Self-Selection Biases and Beyond arXiv:2504.07133v1 Announce Type: new Abstract: We revisit the problem of estimating $k$ linear regressors with self-selection bias in $d$ dimensions with the maximum selection criterion, as introduced by Cherapanamjeri, Daskalakis, Ilyas, and Zampetakis [CDIZ23, STOC’23]. Our main result is a $operatorname{poly}(d,k,1/varepsilon) + {k}^{O(k)}$…

  • Communication-Efficient l_0 Penalized Least Square

    Communication-Efficient l_0 Penalized Least Square arXiv:2504.00722v1 Announce Type: new Abstract: In this paper, we propose a communication-efficient penalized regression algorithm for high-dimensional sparse linear regression models with massive data. This approach incorporates an optimized distributed system communication algorithm, named CESDAR algorithm, based on the Enhanced Support Detection and Root finding algorithm. The CESDAR algorithm leverages…

  • Algorithm Protection in the Context of Federated Learning 

    Algorithm Protection in the Context of Federated Learning  While working at a biotech company, we aim to advance ML & AI Algorithms to enable, for example, brain lesion segmentation to be executed at the hospital/clinic location where patient data resides, so it is processed in a secure manner. This, in essence, is guaranteed by federated…

  • Learning and Computation of $Phi$-Equilibria at the Frontier of Tractability

    Learning and Computation of $Phi$-Equilibria at the Frontier of Tractability arXiv:2502.18582v1 Announce Type: new Abstract: $Phi$-equilibria — and the associated notion of $Phi$-regret — are a powerful and flexible framework at the heart of online learning and game theory, whereby enriching the set of deviations $Phi$ begets stronger notions of rationality. Recently, Daskalakis, Farina, Fishelson,…

  • Transfer Neyman-Pearson Algorithm for Outlier Detection

    Transfer Neyman-Pearson Algorithm for Outlier Detection arXiv:2501.01525v1 Announce Type: cross Abstract: We consider the problem of transfer learning in outlier detection where target abnormal data is rare. While transfer learning has been considered extensively in traditional balanced classification, the problem of transfer in outlier detection and more generally in imbalanced classification settings has received less…

  • Different thresholding methods on Nearest Shrunken Centroid algorithm

    Different thresholding methods on Nearest Shrunken Centroid algorithm arXiv:2501.00632v1 Announce Type: new Abstract: This article considers the impact of different thresholding methods to the Nearest Shrunken Centroid algorithm, which is popularly referred as the Prediction Analysis of Microarrays (PAM) for high-dimensional classification. PAM uses soft thresholding to achieve high computational efficiency and high classification accuracy…

  • The Algorithm That Made Google Google

    The Algorithm That Made Google Google How PageRank transformed how we searched the internet, and why it’s still playing an important role in LLMs with Graph RAG. Continue reading on Towards Data Science » Cristian Leo Go to original source