AI Blogs
Evaluating and Enhancing Equity in AI Systems
Fairlearn: Assessing and Improving Fairness of AI Systems Hilde Weerts, Miroslav Dudík, Richard Edgar, Adrin Jalali, Roman Lutz, Michael Madaio; 24(257):1−8, 2023. Abstract Fairlearn is a project aimed at assisting practitioners in evaluating and enhancing the...
Enhancing Small-Footprint Few-Shot Keyword Spotting through Auxiliary Data Supervision
arXivLabs is a platform that enables collaborators to develop and share new features for arXiv directly on our website. Both individuals and organizations that work with arXivLabs have fully embraced and accepted our values of openness, community, excellence, and user...
Comparing Average Linkage, Bisecting K-means, and Local Search
Approximation Bounds for Hierarchical Clustering: Average Linkage, Bisecting K-means, and Local Search Benjamin Moseley, Joshua R. Wang; 24(1):1−36, 2023. Abstract This paper aims to provide an analytical framework for understanding hierarchical clustering, a widely...
Multidimensional Settings and Impure Training Data: Theoretical Framework and Practical Implications
arXivLabs is a platform that enables collaborators to create and share new features for arXiv directly on our website. Both individuals and organizations that work with arXivLabs share our core values of openness, community, excellence, and user data privacy. We are...
Exploring the Carbon Footprint of Federated Learning: An Initial Analysis
Examining the Environmental Impact of Federated Learning Xinchi Qiu, Titouan Parcollet, Javier Fernandez-Marques, Pedro P. B. Gusmao, Yan Gao, Daniel J. Beutel, Taner Topal, Akhil Mathur, Nicholas D. Lane; 24(129):1−23, 2023. Abstract Although deep learning-based...
A Combined Approach of Variable Mode Decomposition (VMD) and Time Synchronous Averaging (TSA) for Modified Lagrangian Formulation in Gear Tooth Crack Analysis
[Submitted on 29 Aug 2023] Click here to download a PDF of the paper titled "Modified Lagrangian Formulation of Gear Tooth Crack Analysis using Combined Approach of Variable Mode Decomposition (VMD) and Time Synchronous Averaging (TSA)" by Subrata Mukherjee and 1...
Bayesian Spike-Laplacian Graphs: Exploring Bayesian Approaches for Spike-Laplacian Graphical Models
Bayesian Spiked Laplacian Graphs Authors: Leo L Duan, George Michailidis, Mingzhou Ding; Volume 24(3):1−35, 2023. Abstract In the field of network analysis, it is common to encounter a collection of graphs that exhibit heterogeneity. For instance, there is an...
A Token-Level Autoregressive Generative Model of Message Flow Using Deep State Space Network: Exploring Generative AI for End-to-End Limit Order Book Modelling (arXiv:2309.00638v1 [q-fin.TR])
Developing a realistic generative model for order flow in financial markets is a complex problem with various potential applications for market participants. In this study, we propose an innovative autoregressive generative model that can produce tokenized limit order...
Graph Neural Networks for Combinatorial Optimization and Reasoning
Combinatorial Optimization and Reasoning with Graph Neural Networks Quentin Cappart, Didier Chételat, Elias B. Khalil, Andrea Lodi, Christopher Morris, Petar Velickovic; 24(130):1−61, 2023. Abstract Combinatorial optimization, a well-established field in operations...
Designing Carbon Fiber Organosheet Battery Enclosures for Crashworthiness using Finite Element Analysis and Machine Learning (arXiv:2309.00637v1 [cs.LG])
The content discusses the potential use of carbon fiber composite as a replacement for metal-based battery enclosures in electric vehicles (E.V.s). The advantages of carbon fiber, such as its strength-to-weight ratio and corrosion resistance, make it a suitable...
Multi-Task Gaussian Processes for Predicting Cluster-Specific Outcomes
Cluster-Specific Predictions with Multi-Task Gaussian Processes Arthur Leroy, Pierre Latouche, Benjamin Guedj, Servane Gey; 24(5):1−49, 2023. Abstract This study introduces a model that utilizes Gaussian processes (GPs) to handle multitask learning, clustering, and...
Deep Policy Gradient Methods for Commodities Trading (arXiv:2309.00630v1 [q-fin.TR])
This article examines the potential of deep reinforcement learning (DRL) methods in algorithmic commodities trading. The study presents a novel time-discretization scheme that adjusts to market volatility, improving the statistical properties of financial time series....
Defining Redundancy and Relevancy in Feature Selection Through a Comprehensive Information-Theoretic Approach Utilizing (Partial) Information Decomposition
A Comprehensive Definition of Redundancy and Relevance in Feature Selection Based on Information Decomposition Patricia Wollstadt, Sebastian Schmitt, Michael Wibral; 24(131):1−44, 2023. Abstract In machine learning and statistics, the selection of a minimal set of...
Automated Cryptocurrency Trading: An Ensemble Approach using Deep Reinforcement Learning
We present a new approach to enhance the performance of trading strategies developed through deep reinforcement learning algorithms in the highly unpredictable environment of intraday cryptocurrency portfolio trading. Our method involves using an ensemble technique to...
The Influence of Distance and Kernel Measures on Conditional Dependence
On the Relationship Between Distance and Kernel Measures of Conditional Dependence Tianhong Sheng, Bharath K. Sriperumbudur; 24(7):1−16, 2023. Abstract Measuring conditional dependence is a crucial task in statistical inference and plays a fundamental role in various...
Using exogenous variables and machine learning algorithms to forecast short-term stock prices.
In the realm of finance, accurately predicting stock market trends has always been a formidable challenge. However, with the emergence of machine learning as a powerful tool for forecasting, this research paper undertakes a comparative analysis of four machine...
Applying Generalized Linear Models to Public Data in Non-interactive Local Differential Privacy
Generalized Linear Models in Non-interactive Local Differential Privacy with Public Data Di Wang, Lijie Hu, Huanyu Zhang, Marco Gaboardi, Jinhui Xu; 24(132):1−57, 2023. Abstract This paper examines the estimation of smooth Generalized Linear Models (GLMs) in the...
Applications of Random Graph Homomorphisms Sampling in Network Data Analysis
Sampling Random Graph Homomorphisms and Its Applications in Network Data Analysis Hanbaek Lyu, Facundo Memoli, David Sivakoff; 24(9):1−79, 2023. Abstract A graph homomorphism refers to a mapping between two graphs that preserves adjacency relations. This study focuses...
Leveraging Regression Discontinuities to Reduce Bias in Conditioned-on-Observable Estimators
Exploiting Discovered Regression Discontinuities to Debias Conditioned-on-observable Estimators Benjamin Jakubowski, Sriram Somanchi, Edward McFowland III, Daniel B. Neill; 24(133):1−57, 2023. Abstract This study introduces a novel method for addressing the...
Optimal Approaches for Classifiers with Reject Options
Optimal Strategies for Reject Option Classifiers Vojtech Franc, Daniel Prusa, Vaclav Voracek; 24(11):1−49, 2023. Abstract In the context of classification with a reject option, classifiers have the ability to abstain from making predictions in uncertain cases....
A Reduction Algorithm for Estimating High-Dimensional Sparse Precision Matrices with Second-Order Accuracy
MARS: A Second-Order Reduction Algorithm for High-Dimensional Sparse Precision Matrices Estimation Qian Li, Binyan Jiang, Defeng Sun; 24(134):1−44, 2023. Abstract The estimation of precision matrices, also known as inverse covariance matrices, is crucial in...
Adjusting to the Variety in Multi-Armed Bandits
Adaptation to the Range in K-Armed Bandits Authors: Hédi Hadiji, Gilles Stoltz; Publication Date: 2023; Pages: 1-33 Abstract This study focuses on stochastic bandit problems involving K arms, each associated with a distribution supported on a given finite range [m,...
Sparse Gradient Component Analysis and Thresholded Gradient Descent
Sparse GCA and Thresholded Gradient Descent Authors: Sheng Gao, Zongming Ma; Published: 2023; Volume: 24(135); Pages: 1-61. Abstract Sparse GCA and Thresholded Gradient Descent is a study on uncovering linear relationships across multiple data sets. It extends...
Expanding Adversarial Attacks for Generating Adversarial Class Probability Distributions
Extending Adversarial Attacks to Generate Adversarial Class Probability Distributions Authors: Jon Vadillo, Roberto Santana, Jose A. Lozano; Published in 2023, Volume 24, Pages 1-42. Abstract Deep learning models have shown remarkable performance and generalization...
Nonparametric Variable Selection for Dimension Reduction in Contextual Online Learning
Dimension Reduction in Contextual Online Learning via Nonparametric Variable Selection Wenhao Li, Ningyuan Chen, L. Jeff Hong; 24(136):1−84, 2023. Abstract This study addresses the problem of dimension reduction in contextual online learning (multi-armed bandit) with...
Understanding Mean-Field Games using Discounted and Average Costs
Learning Mean-Field Games with Discounted and Average Costs Berkay Anahtarci, Can Deha Kariksiz, Naci Saldi; 24(17):1−59, 2023. Abstract This study focuses on learning approximate Nash equilibria for discrete-time mean-field games with stochastic nonlinear state...
Examining the Implicit Bias of Gradient Descent for Mean Squared Error Regression with Two-Layer Wide Neural Networks
Implicit Bias of Gradient Descent for Mean Squared Error Regression with Two-Layer Wide Neural Networks Hui Jin, Guido Montufar; Volume 24, Issue 137, Pages 1-97, 2023. Abstract This study focuses on investigating the training of wide neural networks using gradient...
Regularization of Joint Mixture Models
Regularized Joint Mixture Models Konstantinos Perrakis, Thomas Lartigue, Frank Dondelinger, Sach Mukherjee; 24(19):1−47, 2023. Abstract Regularized regression models have been extensively studied and, when certain conditions are met, they provide fast and...
Subsampling-Based Network Embeddings: Exploring Asymptotic Properties
Asymptotics of Network Embeddings Learned via Subsampling Authors: Andrew Davison, Morgane Austern; Publication Date: 2023; Journal: 24(138):1−120 Abstract Network data plays a crucial role in modern machine learning, with various tasks such as node classification,...
Predicting HVAC faults with AWS Glue and Amazon SageMaker for Carrier
In 1902, Willis Carrier invented air conditioning, which revolutionized the way we control indoor environments. Today, Carrier products are trusted for creating comfortable spaces, ensuring food safety, and transporting medical supplies. At Carrier, we prioritize...
Online Multi-Kernel Learning Enhanced with Graph-Based Techniques
Graph-Aided Online Multi-Kernel Learning Authors: Pouya M. Ghari, Yanning Shen; 24(21):1−44, 2023. Abstract Multi-kernel learning (MKL) has gained popularity in function learning tasks. Unlike single kernel learning, which relies on a pre-selected kernel, MKL combines...
Develop a content moderation solution using Amazon SageMaker JumpStart with generative AI
Content moderation is crucial for maintaining online safety and upholding the standards of websites and social media platforms. It protects users from inappropriate content and ensures their well-being in digital spaces. For advertisers, content moderation helps...
N-player General-sum Linear-quadratic Games: Policy Gradient Methods Successfully Identify the Nash Equilibrium
Policy Gradient Methods and the Nash Equilibrium in N-player General-sum Linear-quadratic Games Authors: Ben Hambly, Renyuan Xu, Huining Yang; Journal of Machine Learning Research, 24(139):1−56, 2023. Abstract This study focuses on the convergence of the natural...
Optimizing Deployment Cost of Amazon SageMaker JumpStart Foundation Models using Asynchronous Endpoints
The success of generative AI applications in various industries has caught the attention of companies worldwide. These companies are interested in replicating and surpassing the achievements of their competitors or finding new and exciting use cases. To power their...
Selecting Data using Bayesian Methods
Bayesian Data Selection Eli N. Weinstein, Jeffrey W. Miller; 24(23):1−72, 2023. Abstract To gain insights into complex, high-dimensional data, it is important to identify features of the data that either match or do not match a given model of interest. In order to...
Individualized Decision Making with Continuous Treatments: An Exploration of Jump Interval-Learning
Jump Interval-Learning for Individualized Decision Making with Continuous Treatments Authors: Hengrui Cai, Chengchun Shi, Rui Song, Wenbin Lu; 24(140):1−92, 2023. Abstract This paper introduces jump interval-learning, a method for developing an individualized...
Acceleration of Optimization through Discrete Variational Calculus
Discrete Variational Calculus for Accelerated Optimization Cédric M. Campos, Alejandro Mahillo, David Martín de Diego; 24(25):1−33, 2023. Abstract The field of machine learning has seen significant advancements in gradient-based optimization methods. A recent approach...
Achieving the Best Convergence Rates for Distributed Nystroem Approximation
Optimal Convergence Rates for Distributed Nystroem Approximation Jian Li, Yong Liu, Weiping Wang; 24(141):1−39, 2023. Abstract The distributed kernel ridge regression (DKRR) has demonstrated significant potential in handling complex tasks. However, DKRR only relies on...
Efficient Variable Selection and Nonlinear Interaction Discovery in High-Dimensional Data
The SKIM-FA Kernel: High-Dimensional Variable Selection and Nonlinear Interaction Discovery in Linear Time Authors: Raj Agrawal, Tamara Broderick; Published in 2023, 24(27):1−60. Abstract Identifying a small set of covariates associated with a target response and...
Theoretical Foundations and Practical Applications of Loss Tilt in Machine Learning
On Tilted Losses in Machine Learning: Theory and Applications Tian Li, Ahmad Beirami, Maziar Sanjabi, Virginia Smith; 24(142):1−79, 2023. Abstract Exponential tilting is a technique commonly employed in statistics, probability, information theory, and optimization to...
Scikit-learn Compatible Python Library for Local Hierarchical Classification
HiClass: a Python Library for Local Hierarchical Classification Compatible with Scikit-learn Authors: Fábio M. Miranda, Niklas Köhnecke, Bernhard Y. Renard; Publication Date: 24(29):1−17, 2023. Abstract HiClass is a Python library for local hierarchical classification...
Graph-based multi-manifold clustering: Spectral analysis on a large dataset
Large sample spectral analysis of graph-based multi-manifold clustering Authors: Nicolas Garcia Trillos, Pengfei He, Chenghui Li; Journal of Machine Learning Research (JMLR), 24(143):1−71, 2023. Abstract This study focuses on the statistical properties of graph-based...
Maximizing Learning Efficiency with Adequate Labels
Efficient Learning Using Sufficient Labels: Labels, Information, and Computation Authors: Shiyu Duan, Spencer Chang, Jose C. Principe; Published in 2023, Volume 24(31):1−35. Abstract Supervised learning often requires a large amount of fully-labeled training data,...
Overcoming the Curse of Dimensionality in Bayesian Model-Based Clustering
Overcoming the Curse of Dimensionality in Bayesian Model-Based Clustering Noirrit Kiran Chandra, Antonio Canale, David B. Dunson; 24(144):1−42, 2023. Abstract When clustering high-dimensional data, Bayesian mixture models are commonly used to provide uncertainty...
Reducing Gaps in Knowledge Sharing and Transfer
Minimizing the Gap for Knowledge Sharing and Transfer Authors: Boyu Wang, Jorge A. Mendez, Changjian Shui, Fan Zhou, Di Wu, Gezheng Xu, Christian Gagné, Eric Eaton; Published in 2023; Volume 24, Issue 33, Pages 1-57. Abstract Over the past few decades, there has been...
Enhancing Model-Agnostic Meta-Learning and Personalized Federated Learning through Memory-Based Optimization Techniques
Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and Personalized Federated Learning Bokun Wang, Zhuoning Yuan, Yiming Ying, Tianbao Yang; 24(145):1−46, 2023. Abstract The field of model-agnostic meta-learning (MAML) has gained popularity in recent...
Reinforcement Learning’s Capability in Discovering Stackelberg-Nash Equilibria in General-Sum Markov Games with Rational Followers Acting Myopically
Can Reinforcement Learning Find Stackelberg-Nash Equilibria in General-Sum Markov Games with Myopically Rational Followers? Authors: Han Zhong, Zhuoran Yang, Zhaoran Wang, Michael I. Jordan; Published: 24(35):1−52, 2023. Abstract This study focuses on multi-player...
Emphasizing Weightings in Off-Policy Actor-Critic Methods
Off-Policy Actor-Critic with Emphatic Weightings Eric Graves, Ehsan Imani, Raksha Kumaraswamy, Martha White; 24(146):1−63, 2023. Abstract A variety of policy gradient algorithms have been developed for the on-policy setting based on the policy gradient theorem, which...
The Radon Transform: Exploring Ridges and Neural Networks
Ridges, Neural Networks, and the Radon Transform By Michael Unser; Volume 24, Issue 37: Pages 1-33, 2023. Abstract A ridge is a function characterized by a one-dimensional profile (activation) and a multidimensional direction vector. Ridges are relevant in neural...
Optimizing Stochastic Systems amidst Distributional Drift
Stochastic Optimization under Distributional Drift Authors: Joshua Cutler, Dmitriy Drusvyatskiy, Zaid Harchaoui; Volume 24, Issue 147, Pages 1-56, 2023. Abstract This study addresses the problem of minimizing a convex function that undergoes unknown and potentially...
Sensing Principles for Linear Inverse Problems in Unsupervised Learning
Sensing Theorems for Unsupervised Learning in Linear Inverse Problems Julián Tachella, Dongdong Chen, Mike Davies; 24(39):1−45, 2023. Abstract Learning the underlying signal model in an ill-posed linear inverse problem is necessary for solving it. However, when the...
Convergence of Objective and Duality Gap for Non-Convex Strongly-Concave Min-Max Problems with PL Condition at High Speed
Fast Convergence of Non-Convex Strongly-Concave Min-Max Problems with PL Condition Zhishuai Guo, Yan Yan, Zhuoning Yuan, Tianbao Yang; 24(148):1−63, 2023. Abstract This paper presents a study on stochastic methods for efficiently solving smooth non-convex...
A Spatio-Temporal Data Dimensionality Reduction Paradigm Independent of Meshes
Neural Implicit Flow: A Mesh-Agnostic Dimensionality Reduction Paradigm for Spatio-Temporal Data Shaowu Pan, Steven L. Brunton, J. Nathan Kutz; 24(41):1−60, 2023. Abstract High-dimensional spatio-temporal dynamics often have a low-dimensional subspace representation....
Using Kernel Norms to Control Wasserstein Distances and its Application in Compressive Statistical Learning
Controlling Wasserstein Distances by Kernel Norms in Compressive Statistical Learning In the field of machine learning, comparing probability distributions is a crucial task. Two popular methods for measuring the distance between probability distributions are Maximum...
Comparing Performance of Graph Neural Networks: A Benchmark Study
Benchmarking Graph Neural Networks - Rewrite Vijay Prakash Dwivedi, Chaitanya K. Joshi, Anh Tuan Luu, Thomas Laurent, Yoshua Bengio, Xavier Bresson; 24(43):1−48, 2023. Abstract Over the past few years, graph neural networks (GNNs) have become the standard tool for...
A Framework for Simultaneous Population-based Multi-agent Reinforcement Learning
MALib: A Parallel Framework for Population-based Multi-agent Reinforcement Learning Ming Zhou, Ziyu Wan, Hanjing Wang, Muning Wen, Runzhe Wu, Ying Wen, Yaodong Yang, Yong Yu, Jun Wang, Weinan Zhang; 24(150):1−12, 2023. Abstract Population-based multi-agent...
Adversarial Multiclass Classification: An Approach based on the Multimarginal Optimal Transport Formulation
The Multimarginal Optimal Transport Formulation of Adversarial Multiclass Classification Nicolás García Trillos, Matt Jacobs, Jakwang Kim; 24(45):1−56, 2023. Abstract This paper explores a range of adversarial multiclass classification problems and presents...
Bounds on Generalization Error for Sparse Linear Classifiers in Multiclass Classification
Generalization error bounds for multiclass sparse linear classifiers Authors: Tomer Levy, Felix Abramovich; Published in: Journal of Machine Learning Research, 24(151):1−35, 2023. Abstract This study focuses on high-dimensional multiclass classification using sparse...
Using Group Theory for Computational Abstraction: Employing Symmetry-Driven Hierarchical Clustering
A Group-Theoretic Approach to Computational Abstraction: Symmetry-Driven Hierarchical Clustering Authors: Haizi Yu, Igor Mineyev, Lav R. Varshney; 24(47):1−61, 2023. Abstract This theory paper presents a mathematical formulation for computationally emulating...
Selective inference applied to k-means clustering
Selective Inference for K-means Clustering Yiqun T. Chen, Daniela M. Witten; 24(152):1−41, 2023. Abstract We examine the issue of testing for a difference in means between clusters of observations identified through k-means clustering. Traditional hypothesis tests in...
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