mailitics

Tag: learning

  • Physics constrained learning of stochastic characteristics

    Physics constrained learning of stochastic characteristics arXiv:2507.12661v1 Announce Type: new Abstract: Accurate state estimation requires careful consideration of uncertainty surrounding the process and measurement models; these characteristics are usually not well-known and need an experienced designer to select the covariance matrices. An error in the selection of covariance matrices could impact the accuracy of the…

  • Exploring Prompt Learning: Using English Feedback to Optimize LLM Systems

    Exploring Prompt Learning: Using English Feedback to Optimize LLM Systems Prompt learning presents a compelling approach for continuous improvement of AI applications The post Exploring Prompt Learning: Using English Feedback to Optimize LLM Systems appeared first on Towards Data Science. Aparna Dhinakaran Go to original source

  • How does Labeling Error Impact Contrastive Learning? A Perspective from Data Dimensionality Reduction

    How does Labeling Error Impact Contrastive Learning? A Perspective from Data Dimensionality Reduction arXiv:2507.11161v1 Announce Type: new Abstract: In recent years, contrastive learning has achieved state-of-the-art performance in the territory of self-supervised representation learning. Many previous works have attempted to provide the theoretical understanding underlying the success of contrastive learning. Almost all of them rely…

  • The Bayesian Approach to Continual Learning: An Overview

    The Bayesian Approach to Continual Learning: An Overview arXiv:2507.08922v1 Announce Type: new Abstract: Continual learning is an online paradigm where a learner continually accumulates knowledge from different tasks encountered over sequential time steps. Importantly, the learner is required to extend and update its knowledge without forgetting about the learning experience acquired from the past, and…

  • Temporal Conformal Prediction (TCP): A Distribution-Free Statistical and Machine Learning Framework for Adaptive Risk Forecasting

    Temporal Conformal Prediction (TCP): A Distribution-Free Statistical and Machine Learning Framework for Adaptive Risk Forecasting arXiv:2507.05470v1 Announce Type: new Abstract: We propose Temporal Conformal Prediction (TCP), a novel framework for constructing prediction intervals in financial time-series with guaranteed finite-sample validity. TCP integrates quantile regression with a conformal calibration layer that adapts online via a decaying…

  • My Honest Advice for Aspiring Machine Learning Engineers

    My Honest Advice for Aspiring Machine Learning Engineers What it really takes to become a machine learning engineer The post My Honest Advice for Aspiring Machine Learning Engineers appeared first on Towards Data Science. Egor Howell Go to original source

  • Active Learning for Manifold Gaussian Process Regression

    Active Learning for Manifold Gaussian Process Regression arXiv:2506.20928v1 Announce Type: new Abstract: This paper introduces an active learning framework for manifold Gaussian Process (GP) regression, combining manifold learning with strategic data selection to improve accuracy in high-dimensional spaces. Our method jointly optimizes a neural network for dimensionality reduction and a Gaussian process regressor in the…

  • Scalable Machine Learning Algorithms using Path Signatures

    Scalable Machine Learning Algorithms using Path Signatures arXiv:2506.17634v1 Announce Type: new Abstract: The interface between stochastic analysis and machine learning is a rapidly evolving field, with path signatures – iterated integrals that provide faithful, hierarchical representations of paths – offering a principled and universal feature map for sequential and structured data. Rooted in rough path…

  • Reinforcement Learning from Human Feedback, Explained Simply

    Reinforcement Learning from Human Feedback, Explained Simply The one technique that made ChatGPT so smart The post Reinforcement Learning from Human Feedback, Explained Simply appeared first on Towards Data Science. Vyacheslav Efimov Go to original source

  • Double Machine Learning for Conditional Moment Restrictions: IV regression, Proximal Causal Learning and Beyond

    Double Machine Learning for Conditional Moment Restrictions: IV regression, Proximal Causal Learning and Beyond arXiv:2506.14950v1 Announce Type: new Abstract: Solving conditional moment restrictions (CMRs) is a key problem considered in statistics, causal inference, and econometrics, where the aim is to solve for a function of interest that satisfies some conditional moment equalities. Specifically, many techniques…

  • Rademacher learning rates for iterated random functions

    Rademacher learning rates for iterated random functions arXiv:2506.13946v1 Announce Type: new Abstract: Most existing literature on supervised machine learning assumes that the training dataset is drawn from an i.i.d. sample. However, many real-world problems exhibit temporal dependence and strong correlations between the marginal distributions of the data-generating process, suggesting that the i.i.d. assumption is often…

  • Distributionally-Constrained Adversaries in Online Learning

    Distributionally-Constrained Adversaries in Online Learning arXiv:2506.10293v1 Announce Type: new Abstract: There has been much recent interest in understanding the continuum from adversarial to stochastic settings in online learning, with various frameworks including smoothed settings proposed to bridge this gap. We consider the more general and flexible framework of distributionally constrained adversaries in which instances are…

  • Regret-Optimal Q-Learning with Low Cost for Single-Agent and Federated Reinforcement Learning

    Regret-Optimal Q-Learning with Low Cost for Single-Agent and Federated Reinforcement Learning arXiv:2506.04626v1 Announce Type: new Abstract: Motivated by real-world settings where data collection and policy deployment — whether for a single agent or across multiple agents — are costly, we study the problem of on-policy single-agent reinforcement learning (RL) and federated RL (FRL) with a…

  • Enabling Probabilistic Learning on Manifolds through Double Diffusion Maps

    Enabling Probabilistic Learning on Manifolds through Double Diffusion Maps arXiv:2506.02254v1 Announce Type: new Abstract: We present a generative learning framework for probabilistic sampling based on an extension of the Probabilistic Learning on Manifolds (PLoM) approach, which is designed to generate statistically consistent realizations of a random vector in a finite-dimensional Euclidean space, informed by a…

  • Boosting In-Context Learning in LLMs Through the Lens of Classical Supervised Learning

    Boosting In-Context Learning in LLMs Through the Lens of Classical Supervised Learning arXiv:2505.23783v1 Announce Type: new Abstract: In-Context Learning (ICL) allows Large Language Models (LLMs) to adapt to new tasks with just a few examples, but their predictions often suffer from systematic biases, leading to unstable performances in classification. While calibration techniques are proposed to…

  • A Mathematical Perspective On Contrastive Learning

    A Mathematical Perspective On Contrastive Learning arXiv:2505.24134v1 Announce Type: new Abstract: Multimodal contrastive learning is a methodology for linking different data modalities; the canonical example is linking image and text data. The methodology is typically framed as the identification of a set of encoders, one for each modality, that align representations within a common latent…

  • Reinforcement Learning Made Simple: Build a Q-Learning Agent in Python

    Reinforcement Learning Made Simple: Build a Q-Learning Agent in Python Inspired by AlphaGo’s Move 37 — learn how agents explore, exploit, and win The post Reinforcement Learning Made Simple: Build a Q-Learning Agent in Python appeared first on Towards Data Science. Sarah Schürch Go to original source

  • Learning Probabilities of Causation from Finite Population Data

    Learning Probabilities of Causation from Finite Population Data arXiv:2505.17133v1 Announce Type: new Abstract: Probabilities of causation play a crucial role in modern decision-making. This paper addresses the challenge of predicting probabilities of causation for subpopulations with textbf{insufficient} data using machine learning models. Tian and Pearl first defined and derived tight bounds for three fundamental probabilities…

  • Top Machine Learning Jobs and How to Prepare For Them

    Top Machine Learning Jobs and How to Prepare For Them These days, job titles like data scientist, machine learning engineer, and Ai Engineer are everywhere — and if you were anything like me, it can be hard to understand what each of them actually does if you are not working within the field. And then there are titles…

  • What the Most Detailed Peer-Reviewed Study on AI in the Classroom Taught Us

    What the Most Detailed Peer-Reviewed Study on AI in the Classroom Taught Us The rapid proliferation and superb capabilities of widely available LLMs has ignited intense debate within the educational sector. On one side they offer students a 24/7 tutor who is always available to help; but then of course students can use LLMs to…

  • Minimax learning rates for estimating binary classifiers under margin conditions

    Minimax learning rates for estimating binary classifiers under margin conditions arXiv:2505.10628v1 Announce Type: new Abstract: We study classification problems using binary estimators where the decision boundary is described by horizon functions and where the data distribution satisfies a geometric margin condition. We establish upper and lower bounds for the minimax learning rate over broad function…

  • How to Learn the Math Needed for Machine Learning

    How to Learn the Math Needed for Machine Learning Maths can be a scary topic for people. Many of you want to work in machine learning, but the maths skills needed may seem overwhelming. I am here to tell you that it’s nowhere as intimidating as you may think and to give you a roadmap, resources,…

  • Learning Guarantee of Reward Modeling Using Deep Neural Networks

    Learning Guarantee of Reward Modeling Using Deep Neural Networks arXiv:2505.06601v1 Announce Type: new Abstract: In this work, we study the learning theory of reward modeling with pairwise comparison data using deep neural networks. We establish a novel non-asymptotic regret bound for deep reward estimators in a non-parametric setting, which depends explicitly on the network architecture.…

  • Mixed-Integer Optimization for Responsible Machine Learning

    Mixed-Integer Optimization for Responsible Machine Learning arXiv:2505.05857v1 Announce Type: cross Abstract: In the last few decades, Machine Learning (ML) has achieved significant success across domains ranging from healthcare, sustainability, and the social sciences, to criminal justice and finance. But its deployment in increasingly sophisticated, critical, and sensitive areas affecting individuals, the groups they belong to,…

  • Generalization Analysis for Contrastive Representation Learning under Non-IID Settings

    Generalization Analysis for Contrastive Representation Learning under Non-IID Settings arXiv:2505.04937v1 Announce Type: new Abstract: Contrastive Representation Learning (CRL) has achieved impressive success in various domains in recent years. Nevertheless, the theoretical understanding of the generalization behavior of CRL is limited. Moreover, to the best of our knowledge, the current literature only analyzes generalization bounds under…

  • A Symbolic and Statistical Learning Framework to Discover Bioprocessing Regulatory Mechanism: Cell Culture Example

    A Symbolic and Statistical Learning Framework to Discover Bioprocessing Regulatory Mechanism: Cell Culture Example arXiv:2505.03177v1 Announce Type: new Abstract: Bioprocess mechanistic modeling is essential for advancing intelligent digital twin representation of biomanufacturing, yet challenges persist due to complex intracellular regulation, stochastic system behavior, and limited experimental data. This paper introduces a symbolic and statistical learning…

  • Learning the Simplest Neural ODE

    Learning the Simplest Neural ODE arXiv:2505.02019v1 Announce Type: new Abstract: Since the advent of the “Neural Ordinary Differential Equation (Neural ODE)” paper, learning ODEs with deep learning has been applied to system identification, time-series forecasting, and related areas. Exploiting the diffeomorphic nature of ODE solution maps, neural ODEs has also enabled their use in generative…

  • Reinforcement Learning with Continuous Actions Under Unmeasured Confounding

    Reinforcement Learning with Continuous Actions Under Unmeasured Confounding arXiv:2505.00304v1 Announce Type: new Abstract: This paper addresses the challenge of offline policy learning in reinforcement learning with continuous action spaces when unmeasured confounders are present. While most existing research focuses on policy evaluation within partially observable Markov decision processes (POMDPs) and assumes discrete action spaces, we…

  • Statistical Learning for Heterogeneous Treatment Effects: Pretraining, Prognosis, and Prediction

    Statistical Learning for Heterogeneous Treatment Effects: Pretraining, Prognosis, and Prediction arXiv:2505.00310v1 Announce Type: new Abstract: Robust estimation of heterogeneous treatment effects is a fundamental challenge for optimal decision-making in domains ranging from personalized medicine to educational policy. In recent years, predictive machine learning has emerged as a valuable toolbox for causal estimation, enabling more flexible…

  • Beyond Glorified Curve Fitting: Exploring the Probabilistic Foundations of Machine Learning

    Beyond Glorified Curve Fitting: Exploring the Probabilistic Foundations of Machine Learning You see a math formula you don’t immediately understand. Your instinct? Stop reading. Don’t. That’s exactly what I told myself when I started reading Probabilistic Machine Learning – An Introduction by Kevin P. Murphy. And it was absolutely worth it. It changed how I…

  • Learning and Generalization with Mixture Data

    Learning and Generalization with Mixture Data arXiv:2504.20651v1 Announce Type: new Abstract: In many, if not most, machine learning applications the training data is naturally heterogeneous (e.g. federated learning, adversarial attacks and domain adaptation in neural net training). Data heterogeneity is identified as one of the major challenges in modern day large-scale learning. A classical way…

  • Foundations of Safe Online Reinforcement Learning in the Linear Quadratic Regulator: $sqrt{T}$-Regret

    Foundations of Safe Online Reinforcement Learning in the Linear Quadratic Regulator: $sqrt{T}$-Regret arXiv:2504.18657v1 Announce Type: new Abstract: Understanding how to efficiently learn while adhering to safety constraints is essential for using online reinforcement learning in practical applications. However, proving rigorous regret bounds for safety-constrained reinforcement learning is difficult due to the complex interaction between safety,…

  • If I Wanted to Become a Machine Learning Engineer, I’d Do This

    If I Wanted to Become a Machine Learning Engineer, I’d Do This If I wanted to become a machine learning engineer again, this is the exact process I would follow. Let’s get into it! First become a data scientist or software engineer I’ve said it before, but a machine learning engineer is not exactly an entry-level position.…

  • Post-Transfer Learning Statistical Inference in High-Dimensional Regression

    Post-Transfer Learning Statistical Inference in High-Dimensional Regression arXiv:2504.18212v1 Announce Type: new Abstract: Transfer learning (TL) for high-dimensional regression (HDR) is an important problem in machine learning, particularly when dealing with limited sample size in the target task. However, there currently lacks a method to quantify the statistical significance of the relationship between features and the…

  • Physics-informed features in supervised machine learning

    Physics-informed features in supervised machine learning arXiv:2504.17112v1 Announce Type: new Abstract: Supervised machine learning involves approximating an unknown functional relationship from a limited dataset of features and corresponding labels. The classical approach to feature-based machine learning typically relies on applying linear regression to standardized features, without considering their physical meaning. This may limit model explainability,…

  • Transfer Learning for High-dimensional Reduced Rank Time Series Models

    Transfer Learning for High-dimensional Reduced Rank Time Series Models arXiv:2504.15691v1 Announce Type: new Abstract: The objective of transfer learning is to enhance estimation and inference in a target data by leveraging knowledge gained from additional sources. Recent studies have explored transfer learning for independent observations in complex, high-dimensional models assuming sparsity, yet research on time…

  • Towards Interpretable Deep Generative Models via Causal Representation Learning

    Towards Interpretable Deep Generative Models via Causal Representation Learning arXiv:2504.11609v1 Announce Type: new Abstract: Recent developments in generative artificial intelligence (AI) rely on machine learning techniques such as deep learning and generative modeling to achieve state-of-the-art performance across wide-ranging domains. These methods’ surprising performance is due in part to their ability to learn implicit “representations”…

  • Sparsified-Learning for Heavy-Tailed Locally Stationary Processes

    Sparsified-Learning for Heavy-Tailed Locally Stationary Processes arXiv:2504.06477v1 Announce Type: new Abstract: Sparsified Learning is ubiquitous in many machine learning tasks. It aims to regularize the objective function by adding a penalization term that considers the constraints made on the learned parameters. This paper considers the problem of learning heavy-tailed LSP. We develop a flexible and…

  • Deep Fair Learning: A Unified Framework for Fine-tuning Representations with Sufficient Networks

    Deep Fair Learning: A Unified Framework for Fine-tuning Representations with Sufficient Networks arXiv:2504.06470v1 Announce Type: new Abstract: Ensuring fairness in machine learning is a critical and challenging task, as biased data representations often lead to unfair predictions. To address this, we propose Deep Fair Learning, a framework that integrates nonlinear sufficient dimension reduction with deep…

  • Actuarial Learning for Pension Fund Mortality Forecasting

    Actuarial Learning for Pension Fund Mortality Forecasting arXiv:2504.05881v1 Announce Type: new Abstract: For the assessment of the financial soundness of a pension fund, it is necessary to take into account mortality forecasting so that longevity risk is consistently incorporated into future cash flows. In this article, we employ machine learning models applied to actuarial science…

  • Analytical Discovery of Manifold with Machine Learning

    Analytical Discovery of Manifold with Machine Learning arXiv:2504.02511v1 Announce Type: new Abstract: Understanding low-dimensional structures within high-dimensional data is crucial for visualization, interpretation, and denoising in complex datasets. Despite the advancements in manifold learning techniques, key challenges-such as limited global insight and the lack of interpretable analytical descriptions-remain unresolved. In this work, we introduce a…

  • Fair Sufficient Representation Learning

    Fair Sufficient Representation Learning arXiv:2504.01030v1 Announce Type: new Abstract: The main objective of fair statistical modeling and machine learning is to minimize or eliminate biases that may arise from the data or the model itself, ensuring that predictions and decisions are not unjustly influenced by sensitive attributes such as race, gender, age, or other protected…

  • Learning a Single Index Model from Anisotropic Data with vanilla Stochastic Gradient Descent

    Learning a Single Index Model from Anisotropic Data with vanilla Stochastic Gradient Descent arXiv:2503.23642v1 Announce Type: new Abstract: We investigate the problem of learning a Single Index Model (SIM)- a popular model for studying the ability of neural networks to learn features – from anisotropic Gaussian inputs by training a neuron using vanilla Stochastic Gradient…

  • CAE: Repurposing the Critic as an Explorer in Deep Reinforcement Learning

    CAE: Repurposing the Critic as an Explorer in Deep Reinforcement Learning arXiv:2503.18980v1 Announce Type: new Abstract: Exploration remains a critical challenge in reinforcement learning, as many existing methods either lack theoretical guarantees or fall short of practical effectiveness. In this paper, we introduce CAE, a lightweight algorithm that repurposes the value networks in standard deep…

  • Universal Architectures for the Learning of Polyhedral Norms and Convex Regularization Functionals

    Universal Architectures for the Learning of Polyhedral Norms and Convex Regularization Functionals arXiv:2503.19190v1 Announce Type: new Abstract: This paper addresses the task of learning convex regularizers to guide the reconstruction of images from limited data. By imposing that the reconstruction be amplitude-equivariant, we narrow down the class of admissible functionals to those that can be…

  • A Statistical Theory of Contrastive Learning via Approximate Sufficient Statistics

    A Statistical Theory of Contrastive Learning via Approximate Sufficient Statistics arXiv:2503.17538v1 Announce Type: new Abstract: Contrastive learning — a modern approach to extract useful representations from unlabeled data by training models to distinguish similar samples from dissimilar ones — has driven significant progress in foundation models. In this work, we develop a new theoretical framework…

  • SNPL: Simultaneous Policy Learning and Evaluation for Safe Multi-Objective Policy Improvement

    SNPL: Simultaneous Policy Learning and Evaluation for Safe Multi-Objective Policy Improvement arXiv:2503.12760v1 Announce Type: new Abstract: To design effective digital interventions, experimenters face the challenge of learning decision policies that balance multiple objectives using offline data. Often, they aim to develop policies that maximize goal outcomes, while ensuring there are no undesirable changes in guardrail…

  • On the Identifiability of Causal Abstractions

    On the Identifiability of Causal Abstractions arXiv:2503.10834v1 Announce Type: new Abstract: Causal representation learning (CRL) enhances machine learning models’ robustness and generalizability by learning structural causal models associated with data-generating processes. We focus on a family of CRL methods that uses contrastive data pairs in the observable space, generated before and after a random, unknown…

  • Technical Insights and Legal Considerations for Advancing Federated Learning in Bioinformatics

    Technical Insights and Legal Considerations for Advancing Federated Learning in Bioinformatics arXiv:2503.09649v1 Announce Type: cross Abstract: Federated learning leverages data across institutions to improve clinical discovery while complying with data-sharing restrictions and protecting patient privacy. As the evolution of biobanks in genetics and systems biology has proved, accessing more extensive and varied data pools leads…

  • Learning Pareto manifolds in high dimensions: How can regularization help?

    Learning Pareto manifolds in high dimensions: How can regularization help? arXiv:2503.08849v1 Announce Type: new Abstract: Simultaneously addressing multiple objectives is becoming increasingly important in modern machine learning. At the same time, data is often high-dimensional and costly to label. For a single objective such as prediction risk, conventional regularization techniques are known to improve generalization…

  • Personalized Convolutional Dictionary Learning of Physiological Time Series

    Personalized Convolutional Dictionary Learning of Physiological Time Series arXiv:2503.07687v1 Announce Type: new Abstract: Human physiological signals tend to exhibit both global and local structures: the former are shared across a population, while the latter reflect inter-individual variability. For instance, kinetic measurements of the gait cycle during locomotion present common characteristics, although idiosyncrasies may be observed…

  • Learning Causal Response Representations through Direct Effect Analysis

    Learning Causal Response Representations through Direct Effect Analysis arXiv:2503.04358v1 Announce Type: new Abstract: We propose a novel approach for learning causal response representations. Our method aims to extract directions in which a multidimensional outcome is most directly caused by a treatment variable. By bridging conditional independence testing with causal representation learning, we formulate an optimisation…

  • PAC Learning with Improvements

    PAC Learning with Improvements arXiv:2503.03184v1 Announce Type: new Abstract: One of the most basic lower bounds in machine learning is that in nearly any nontrivial setting, it takes $textit{at least}$ $1/epsilon$ samples to learn to error $epsilon$ (and more, if the classifier being learned is complex). However, suppose that data points are agents who have…

  • Online Inference for Quantiles by Constant Learning-Rate Stochastic Gradient Descent

    Online Inference for Quantiles by Constant Learning-Rate Stochastic Gradient Descent arXiv:2503.02178v1 Announce Type: new Abstract: This paper proposes an online inference method of the stochastic gradient descent (SGD) with a constant learning rate for quantile loss functions with theoretical guarantees. Since the quantile loss function is neither smooth nor strongly convex, we view such SGD…

  • Transfer Learning through Enhanced Sufficient Representation: Enriching Source Domain Knowledge with Target Data

    Transfer Learning through Enhanced Sufficient Representation: Enriching Source Domain Knowledge with Target Data arXiv:2502.20414v1 Announce Type: new Abstract: Transfer learning is an important approach for addressing the challenges posed by limited data availability in various applications. It accomplishes this by transferring knowledge from well-established source domains to a less familiar target domain. However, traditional transfer…

  • Learning Dynamics of Deep Linear Networks Beyond the Edge of Stability

    Learning Dynamics of Deep Linear Networks Beyond the Edge of Stability arXiv:2502.20531v1 Announce Type: new Abstract: Deep neural networks trained using gradient descent with a fixed learning rate $eta$ often operate in the regime of “edge of stability” (EOS), where the largest eigenvalue of the Hessian equilibrates about the stability threshold $2/eta$. In this work,…

  • How LLMs Work: Reinforcement Learning, RLHF, DeepSeek R1, OpenAI o1, AlphaGo

    How LLMs Work: Reinforcement Learning, RLHF, DeepSeek R1, OpenAI o1, AlphaGo Welcome to part 2 of my LLM deep dive. If you’ve not read Part 1, I highly encourage you to check it out first.  Previously, we covered the first two major stages of training an LLM: Pre-training — Learning from massive datasets to form a base…

  • Applications of Statistical Field Theory in Deep Learning

    Applications of Statistical Field Theory in Deep Learning arXiv:2502.18553v1 Announce Type: new Abstract: Deep learning algorithms have made incredible strides in the past decade yet due to the complexity of these algorithms, the science of deep learning remains in its early stages. Being an experimentally driven field, it is natural to seek a theory of…

  • Conformal Prediction Under Generalized Covariate Shift with Posterior Drift

    Conformal Prediction Under Generalized Covariate Shift with Posterior Drift arXiv:2502.17744v1 Announce Type: new Abstract: In many real applications of statistical learning, collecting sufficiently many training data is often expensive, time-consuming, or even unrealistic. In this case, a transfer learning approach, which aims to leverage knowledge from a related source domain to improve the learning performance…

  • Statistical Inference in Reinforcement Learning: A Selective Survey

    Statistical Inference in Reinforcement Learning: A Selective Survey arXiv:2502.16195v1 Announce Type: new Abstract: Reinforcement learning (RL) is concerned with how intelligence agents take actions in a given environment to maximize the cumulative reward they receive. In healthcare, applying RL algorithms could assist patients in improving their health status. In ride-sharing platforms, applying RL algorithms could…

  • Model selection for behavioral learning data and applications to contextual bandits

    Model selection for behavioral learning data and applications to contextual bandits arXiv:2502.13186v1 Announce Type: new Abstract: Learning for animals or humans is the process that leads to behaviors better adapted to the environment. This process highly depends on the individual that learns and is usually observed only through the individual’s actions. This article presents ways…

  • Graph Signal Inference by Learning Narrowband Spectral Kernels

    Graph Signal Inference by Learning Narrowband Spectral Kernels arXiv:2502.13686v1 Announce Type: new Abstract: While a common assumption in graph signal analysis is the smoothness of the signals or the band-limitedness of their spectrum, in many instances the spectrum of real graph data may be concentrated at multiple regions of the spectrum, possibly including mid-to-high-frequency components.…

  • Learning How to Play Atari Games Through Deep Neural Networks

    Learning How to Play Atari Games Through Deep Neural Networks In July 1959, Arthur Samuel developed one of the first agents to play the game of checkers. What constitutes an agent that plays checkers can be best described in Samuel’s own words, “…a computer [that] can be programmed so that it will learn to play…

  • On-Device Machine Learning in Spatial Computing

    On-Device Machine Learning in Spatial Computing The landscape of computing is undergoing a profound transformation with the emergence of spatial computing platforms(VR and AR). As we step into this new era, the intersection of virtual reality, Augmented Reality, and on-device machine learning presents unprecedented opportunities for developers to create experiences that seamlessly blend digital content…

  • Estimation of the Learning Coefficient Using Empirical Loss

    Estimation of the Learning Coefficient Using Empirical Loss arXiv:2502.09998v1 Announce Type: new Abstract: The learning coefficient plays a crucial role in analyzing the performance of information criteria, such as the Widely Applicable Information Criterion (WAIC) and the Widely Applicable Bayesian Information Criterion (WBIC), which Sumio Watanabe developed to assess model generalization ability. In regular statistical…

  • Roadmap to Becoming a Data Scientist, Part 4: Advanced Machine Learning

    Roadmap to Becoming a Data Scientist, Part 4: Advanced Machine Learning Introduction Data science is undoubtedly one of the most fascinating fields today. Following significant breakthroughs in machine learning about a decade ago, data science has surged in popularity within the tech community. Each year, we witness increasingly powerful tools that once seemed unimaginable. Innovations such as the Transformer…

  • A Differentiable Rank-Based Objective For Better Feature Learning

    A Differentiable Rank-Based Objective For Better Feature Learning arXiv:2502.09445v1 Announce Type: new Abstract: In this paper, we leverage existing statistical methods to better understand feature learning from data. We tackle this by modifying the model-free variable selection method, Feature Ordering by Conditional Independence (FOCI), which is introduced in cite{azadkia2021simple}. While FOCI is based on a…

  • On the Convergence and Stability of Upside-Down Reinforcement Learning, Goal-Conditioned Supervised Learning, and Online Decision Transformers

    On the Convergence and Stability of Upside-Down Reinforcement Learning, Goal-Conditioned Supervised Learning, and Online Decision Transformers arXiv:2502.05672v1 Announce Type: new Abstract: This article provides a rigorous analysis of convergence and stability of Episodic Upside-Down Reinforcement Learning, Goal-Conditioned Supervised Learning and Online Decision Transformers. These algorithms performed competitively across various benchmarks, from games to robotic tasks,…

  • TD(0) Learning converges for Polynomial mixing and non-linear functions

    TD(0) Learning converges for Polynomial mixing and non-linear functions arXiv:2502.05706v1 Announce Type: new Abstract: Theoretical work on Temporal Difference (TD) learning has provided finite-sample and high-probability guarantees for data generated from Markov chains. However, these bounds typically require linear function approximation, instance-dependent step sizes, algorithmic modifications, and restrictive mixing rates. We present theoretical findings for…

  • PhyloVAE: Unsupervised Learning of Phylogenetic Trees via Variational Autoencoders

    PhyloVAE: Unsupervised Learning of Phylogenetic Trees via Variational Autoencoders arXiv:2502.04730v1 Announce Type: new Abstract: Learning informative representations of phylogenetic tree structures is essential for analyzing evolutionary relationships. Classical distance-based methods have been widely used to project phylogenetic trees into Euclidean space, but they are often sensitive to the choice of distance metric and may lack…

  • Training Large Language Models: From TRPO to GRPO

    Training Large Language Models: From TRPO to GRPO Deepseek has recently made quite a buzz in the AI community, thanks to its impressive performance at relatively low costs. I think this is a perfect opportunity to dive deeper into how Large Language Models (LLMs) are trained. In this article, we will focus on the Reinforcement Learning…

  • A Survey on Cluster-based Federated Learning

    A Survey on Cluster-based Federated Learning arXiv:2501.17512v1 Announce Type: new Abstract: As the industrial and commercial use of Federated Learning (FL) has expanded, so has the need for optimized algorithms. In settings were FL clients’ data is non-independently and identically distributed (non-IID) and with highly heterogeneous distributions, the baseline FL approach seems to fall short.…

  • Machine Learning Incidents in AdTech

    Machine Learning Incidents in AdTech Source: https://unsplash.com/photos/a-couple-of-signs-that-are-on-a-fence-xXbQIrWH2_A Challenges with deep learning in production One of the biggest challenges I encountered in my career as a data scientist was migrating the core algorithms in a mobile AdTech platform from classic machine learning models to deep learning. I worked on a Demand Side Platform (DSP) for user…

  • Towards the Generalization of Multi-view Learning: An Information-theoretical Analysis

    Towards the Generalization of Multi-view Learning: An Information-theoretical Analysis arXiv:2501.16768v1 Announce Type: new Abstract: Multiview learning has drawn widespread attention for its efficacy in leveraging cross-view consensus and complementarity information to achieve a comprehensive representation of data. While multi-view learning has undergone vigorous development and achieved remarkable success, the theoretical understanding of its generalization behavior…

  • Deep Learning for Click Prediction in Mobile AdTech

    Deep Learning for Click Prediction in Mobile AdTech Source: https://pixabay.com/illustrations/rays-stars-light-explosion-galaxy-9350519/ Machine Learning for Real-Time Bidding The past few years were a revolution for the mobile advertising and gaming industries, with the broad adoption of neural networks for advertising tasks, including click prediction. This migration occurred prior to the success of Large Language Models (LLMs) and…

  • Bayesian Model Parameter Learning in Linear Inverse Problems with Application in EEG Focal Source Imaging

    Bayesian Model Parameter Learning in Linear Inverse Problems with Application in EEG Focal Source Imaging arXiv:2501.13109v1 Announce Type: cross Abstract: Inverse problems can be described as limited-data problems in which the signal of interest cannot be observed directly. A physics-based forward model that relates the signal with the observations is typically needed. Unfortunately, unknown model…

  • Singular leaning coefficients and efficiency in learning theory

    Singular leaning coefficients and efficiency in learning theory arXiv:2501.12747v1 Announce Type: new Abstract: Singular learning models with non-positive Fisher information matrices include neural networks, reduced-rank regression, Boltzmann machines, normal mixture models, and others. These models have been widely used in the development of learning machines. However, theoretical analysis is still in its early stages. In…

  • Model-Robust and Adaptive-Optimal Transfer Learning for Tackling Concept Shifts in Nonparametric Regression

    Model-Robust and Adaptive-Optimal Transfer Learning for Tackling Concept Shifts in Nonparametric Regression arXiv:2501.10870v1 Announce Type: new Abstract: When concept shifts and sample scarcity are present in the target domain of interest, nonparametric regression learners often struggle to generalize effectively. The technique of transfer learning remedies these issues by leveraging data or pre-trained models from similar…

  • Learning from Machine Learning | Sebastian Raschka: Mastering ML and Pushing AI Forward Responsibly

    Learning from Machine Learning | Sebastian Raschka: Mastering ML and Pushing AI Forward Responsibly Sebastian Raschka has helped demystify deep learning for thousands through his books, tutorials and teachings Sebastian Raschka has helped shape how thousands of data scientists and machine learning engineers learn their craft. As a passionate coder and proponent of open-source software,…

  • Learnings from a Machine Learning Engineer — Part 3: The Evaluation

    Learnings from a Machine Learning Engineer — Part 3: The Evaluation Practical insights for a data-driven approach to model optimization Continue reading on Towards Data Science » David Martin Go to original source

  • Counterfactually Fair Reinforcement Learning via Sequential Data Preprocessing

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  • 2024 Survival Guide for Machine Learning Engineer Interviews

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