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Category: aimldsaimlds

  • How Agent Handoffs Work in Multi-Agent Systems

    How Agent Handoffs Work in Multi-Agent Systems Understanding how LLM agents transfer control to each other in a multi-agent system with LangGraph The post How Agent Handoffs Work in Multi-Agent Systems appeared first on Towards Data Science. Kenneth Leung Go to original source

  • Online Inference of Constrained Optimization: Primal-Dual Optimality and Sequential Quadratic Programming

    Online Inference of Constrained Optimization: Primal-Dual Optimality and Sequential Quadratic Programming arXiv:2512.08948v1 Announce Type: new Abstract: We study online statistical inference for the solutions of stochastic optimization problems with equality and inequality constraints. Such problems are prevalent in statistics and machine learning, encompassing constrained $M$-estimation, physics-informed models, safe reinforcement learning, and algorithmic fairness. We develop…

  • WTNN: Weibull-Tailored Neural Networks for survival analysis

    WTNN: Weibull-Tailored Neural Networks for survival analysis arXiv:2512.09163v1 Announce Type: new Abstract: The Weibull distribution is a commonly adopted choice for modeling the survival of systems subject to maintenance over time. When only proxy indicators and censored observations are available, it becomes necessary to express the distribution’s parameters as functions of time-dependent covariates. Deep neural…

  • Robust and Sparse Estimation of Unbounded Density Ratio under Heavy Contamination

    Robust and Sparse Estimation of Unbounded Density Ratio under Heavy Contamination arXiv:2512.09266v1 Announce Type: new Abstract: We examine the non-asymptotic properties of robust density ratio estimation (DRE) in contaminated settings. Weighted DRE is the most promising among existing methods, exhibiting doubly strong robustness from an asymptotic perspective. This study demonstrates that Weighted DRE achieves sparse…

  • Impact of Positional Encoding: Clean and Adversarial Rademacher Complexity for Transformers under In-Context Regression

    Impact of Positional Encoding: Clean and Adversarial Rademacher Complexity for Transformers under In-Context Regression arXiv:2512.09275v1 Announce Type: new Abstract: Positional encoding (PE) is a core architectural component of Transformers, yet its impact on the Transformer’s generalization and robustness remains unclear. In this work, we provide the first generalization analysis for a single-layer Transformer under in-context…

  • Estimation of Stochastic Optimal Transport Maps

    Estimation of Stochastic Optimal Transport Maps arXiv:2512.09499v1 Announce Type: new Abstract: The optimal transport (OT) map is a geometry-driven transformation between high-dimensional probability distributions which underpins a wide range of tasks in statistics, applied probability, and machine learning. However, existing statistical theory for OT map estimation is quite restricted, hinging on Brenier’s theorem (quadratic cost,…

  • The Machine Learning “Advent Calendar” Day 10: DBSCAN in Excel

    The Machine Learning “Advent Calendar” Day 10: DBSCAN in Excel DBSCAN shows how far we can go with a very simple idea: count how many neighbors live close to each point. It finds clusters and marks anomalies without any probabilistic model, and it works beautifully in Excel. But because it relies on one fixed radius,…

  • How to Maximize Agentic Memory for Continual Learning

    How to Maximize Agentic Memory for Continual Learning Learn how to become an effective engineer with continual learning LLMs The post How to Maximize Agentic Memory for Continual Learning appeared first on Towards Data Science. Eivind Kjosbakken Go to original source

  • Don’t Build an ML Portfolio Without These Projects

    Don’t Build an ML Portfolio Without These Projects What recruiters are looking for in machine learning portfolios The post Don’t Build an ML Portfolio Without These Projects appeared first on Towards Data Science. Egor Howell Go to original source

  • Optimizing PyTorch Model Inference on AWS Graviton

    Optimizing PyTorch Model Inference on AWS Graviton Tips for accelerating AI/ML on CPU — Part 2 The post Optimizing PyTorch Model Inference on AWS Graviton appeared first on Towards Data Science. Chaim Rand Go to original source

  • Functional Random Forest with Adaptive Cost-Sensitive Splitting for Imbalanced Functional Data Classification

    Functional Random Forest with Adaptive Cost-Sensitive Splitting for Imbalanced Functional Data Classification arXiv:2512.07888v1 Announce Type: new Abstract: Classification of functional data where observations are curves or trajectories poses unique challenges, particularly under severe class imbalance. Traditional Random Forest algorithms, while robust for tabular data, often fail to capture the intrinsic structure of functional observations and…

  • Provable Diffusion Posterior Sampling for Bayesian Inversion

    Provable Diffusion Posterior Sampling for Bayesian Inversion arXiv:2512.08022v1 Announce Type: new Abstract: This paper proposes a novel diffusion-based posterior sampling method within a plug-and-play (PnP) framework. Our approach constructs a probability transport from an easy-to-sample terminal distribution to the target posterior, using a warm-start strategy to initialize the particles. To approximate the posterior score, we…

  • Worst-case generation via minimax optimization in Wasserstein space

    Worst-case generation via minimax optimization in Wasserstein space arXiv:2512.08176v1 Announce Type: new Abstract: Worst-case generation plays a critical role in evaluating robustness and stress-testing systems under distribution shifts, in applications ranging from machine learning models to power grids and medical prediction systems. We develop a generative modeling framework for worst-case generation for a pre-specified risk,…

  • Heuristics for Combinatorial Optimization via Value-based Reinforcement Learning: A Unified Framework and Analysis

    Heuristics for Combinatorial Optimization via Value-based Reinforcement Learning: A Unified Framework and Analysis arXiv:2512.08601v1 Announce Type: new Abstract: Since the 1990s, considerable empirical work has been carried out to train statistical models, such as neural networks (NNs), as learned heuristics for combinatorial optimization (CO) problems. When successful, such an approach eliminates the need for experts…

  • Bayesian Optimization for Function-Valued Responses under Min-Max Criteria

    Bayesian Optimization for Function-Valued Responses under Min-Max Criteria arXiv:2512.07868v1 Announce Type: cross Abstract: Bayesian optimization is widely used for optimizing expensive black box functions, but most existing approaches focus on scalar responses. In many scientific and engineering settings the response is functional, varying smoothly over an index such as time or wavelength, which makes classical…

  • The Machine Learning “Advent Calendar” Day 9: LOF in Excel

    The Machine Learning “Advent Calendar” Day 9: LOF in Excel In this article, we explore LOF through three simple steps: distances and neighbors, reachability distances, and the final LOF score. Using tiny datasets, we see how two anomalies can look obvious to us but completely different to different algorithms. This reveals the key idea of…

  • Personal, Agentic Assistants: A Practical Blueprint for a Secure, Multi-User, Self-Hosted Chatbot

    Personal, Agentic Assistants: A Practical Blueprint for a Secure, Multi-User, Self-Hosted Chatbot Build a self-hosted, end-to-end platform that gives each user a personal, agentic chatbot that can autonomously vector-search through files that the user explicitly allows it to access. The post Personal, Agentic Assistants: A Practical Blueprint for a Secure, Multi-User, Self-Hosted Chatbot appeared first…

  • How to Develop AI-Powered Solutions, Accelerated by AI

    How to Develop AI-Powered Solutions, Accelerated by AI From idea to impact :  using AI as your accelerating copilot The post How to Develop AI-Powered Solutions, Accelerated by AI appeared first on Towards Data Science. Anna Via Go to original source

  • GraphRAG in Practice: How to Build Cost-Efficient, High-Recall Retrieval Systems

    GraphRAG in Practice: How to Build Cost-Efficient, High-Recall Retrieval Systems Smarter retrieval strategies that outperform dense graphs — with hybrid pipelines and lower cost The post GraphRAG in Practice: How to Build Cost-Efficient, High-Recall Retrieval Systems appeared first on Towards Data Science. Partha Sarkar Go to original source

  • A Realistic Roadmap to Start an AI Career in 2026

    A Realistic Roadmap to Start an AI Career in 2026 How to learn AI in 2026 through real, usable projects The post A Realistic Roadmap to Start an AI Career in 2026 appeared first on Towards Data Science. Sabrine Bendimerad Go to original source

  • Contextual Strongly Convex Simulation Optimization: Optimize then Predict with Inexact Solutions

    Contextual Strongly Convex Simulation Optimization: Optimize then Predict with Inexact Solutions arXiv:2512.06270v1 Announce Type: new Abstract: In this work, we study contextual strongly convex simulation optimization and adopt an “optimize then predict” (OTP) approach for real-time decision making. In the offline stage, simulation optimization is conducted across a set of covariates to approximate the optimal-solution…

  • Modeling Spatio-temporal Extremes via Conditional Variational Autoencoders

    Modeling Spatio-temporal Extremes via Conditional Variational Autoencoders arXiv:2512.06348v1 Announce Type: new Abstract: Extreme weather events are widely studied in fields such as agriculture, ecology, and meteorology. The spatio-temporal co-occurrence of extreme events can strengthen or weaken under changing climate conditions. In this paper, we propose a novel approach to model spatio-temporal extremes by integrating climate…

  • Canonical Tail Dependence for Soft Extremal Clustering of Multichannel Brain Signals

    Canonical Tail Dependence for Soft Extremal Clustering of Multichannel Brain Signals arXiv:2512.06435v1 Announce Type: new Abstract: We develop a novel characterization of extremal dependence between two cortical regions of the brain when its signals display extremely large amplitudes. We show that connectivity in the tails of the distribution reveals unique features of extreme events (e.g.,…

  • ADAM Optimization with Adaptive Batch Selection

    ADAM Optimization with Adaptive Batch Selection arXiv:2512.06795v1 Announce Type: new Abstract: Adam is a widely used optimizer in neural network training due to its adaptive learning rate. However, because different data samples influence model updates to varying degrees, treating them equally can lead to inefficient convergence. To address this, a prior work proposed adapting the…

  • Latent Nonlinear Denoising Score Matching for Enhanced Learning of Structured Distributions

    Latent Nonlinear Denoising Score Matching for Enhanced Learning of Structured Distributions arXiv:2512.06615v1 Announce Type: new Abstract: We present latent nonlinear denoising score matching (LNDSM), a novel training objective for score-based generative models that integrates nonlinear forward dynamics with the VAE-based latent SGM framework. This combination is achieved by reformulating the cross-entropy term using the approximate…

  • Bridging the Silence: How LEO Satellites and Edge AI Will Democratize Connectivity

    Bridging the Silence: How LEO Satellites and Edge AI Will Democratize Connectivity Why on-device intelligence and low-orbit constellations are the only viable path to universal accessibility The post Bridging the Silence: How LEO Satellites and Edge AI Will Democratize Connectivity appeared first on Towards Data Science. Aakash Goswami Go to original source

  • The Machine Learning “Advent Calendar” Day 8: Isolation Forest in Excel

    The Machine Learning “Advent Calendar” Day 8: Isolation Forest in Excel Isolation Forest may look technical, but its idea is simple: isolate points using random splits. If a point is isolated quickly, it is an anomaly; if it takes many splits, it is normal. Using the tiny dataset 1, 2, 3, 9, we can see…

  • The AI Bubble Will Pop — And Why That Doesn’t Matter

    The AI Bubble Will Pop — And Why That Doesn’t Matter How history’s biggest tech bubble explains where AI is headed next The post The AI Bubble Will Pop — And Why That Doesn’t Matter appeared first on Towards Data Science. Michael Malin Go to original source

  • How to Create an ML-Focused Newsletter

    How to Create an ML-Focused Newsletter Learn how to make a newsletter with AI tools The post How to Create an ML-Focused Newsletter appeared first on Towards Data Science. Eivind Kjosbakken Go to original source

  • Optimizing PyTorch Model Inference on CPU

    Optimizing PyTorch Model Inference on CPU Flyin’ Like a Lion on Intel Xeon The post Optimizing PyTorch Model Inference on CPU appeared first on Towards Data Science. Chaim Rand Go to original source

  • How to Tame Your LLM: Semantic Collapse in Continuous Systems

    How to Tame Your LLM: Semantic Collapse in Continuous Systems arXiv:2512.05162v1 Announce Type: new Abstract: We develop a general theory of semantic dynamics for large language models by formalizing them as Continuous State Machines (CSMs): smooth dynamical systems whose latent manifolds evolve under probabilistic transition operators. The associated transfer operator $P: L^2(M,mu) to L^2(M,mu)$ encodes…

  • One-Step Diffusion Samplers via Self-Distillation and Deterministic Flow

    One-Step Diffusion Samplers via Self-Distillation and Deterministic Flow arXiv:2512.05251v1 Announce Type: new Abstract: Sampling from unnormalized target distributions is a fundamental yet challenging task in machine learning and statistics. Existing sampling algorithms typically require many iterative steps to produce high-quality samples, leading to high computational costs. We introduce one-step diffusion samplers which learn a step-conditioned…

  • Symmetric Linear Dynamical Systems are Learnable from Few Observations

    Symmetric Linear Dynamical Systems are Learnable from Few Observations arXiv:2512.05337v1 Announce Type: new Abstract: We consider the problem of learning the parameters of a $N$-dimensional stochastic linear dynamics under both full and partial observations from a single trajectory of time $T$. We introduce and analyze a new estimator that achieves a small maximum element-wise error…

  • Design-marginal calibration of Gaussian process predictive distributions: Bayesian and conformal approaches

    Design-marginal calibration of Gaussian process predictive distributions: Bayesian and conformal approaches arXiv:2512.05611v1 Announce Type: new Abstract: We study the calibration of Gaussian process (GP) predictive distributions in the interpolation setting from a design-marginal perspective. Conditioning on the data and averaging over a design measure mu, we formalize mu-coverage for central intervals and mu-probabilistic calibration through…

  • Do We Really Even Need Data? A Modern Look at Drawing Inference with Predicted Data

    Do We Really Even Need Data? A Modern Look at Drawing Inference with Predicted Data arXiv:2512.05456v1 Announce Type: new Abstract: As artificial intelligence and machine learning tools become more accessible, and scientists face new obstacles to data collection (e.g., rising costs, declining survey response rates), researchers increasingly use predictions from pre-trained algorithms as substitutes for…

  • Weekly Entering & Transitioning – Thread 08 Dec, 2025 – 15 Dec, 2025

    Weekly Entering & Transitioning – Thread 08 Dec, 2025 – 15 Dec, 2025 Welcome to this week’s entering & transitioning thread! This thread is for any questions about getting started, studying, or transitioning into the data science field. Topics include: Learning resources (e.g. books, tutorials, videos) Traditional education (e.g. schools, degrees, electives) Alternative education (e.g.…

  • Inferential Statistics on long-form census data from stats can

    Inferential Statistics on long-form census data from stats can I am using the following tool https://www150.statcan.gc.ca/t1/tbl1/en/tv.action?pid=9810065601 to query Statistics Canada and get data from the long-form census. However, since it’s a census of 25% of the population, there is a need for inferential statistics. That being said in order to do inferential statistics on the…

  • Lost and Feel Like a Fraud

    Lost and Feel Like a Fraud This might not be the appropriate place to say this, but I honestly feel like the biggest fraud ever. If I could go back, I don’t think I would have went into data science. I did my undergraduate in biology, and then did a masters in data science. I’ve…

  • MLE coding rounds (UK)

    MLE coding rounds (UK) I’m a data scientist transitioning to ML-AI Engineer roles. What kind of coding questions-rounds should I expect? I’ve heard that it’s a mixed bag, can be leetcode, can be Pytorch,tf for all ML related I’ve also heard about building ML concepts-algos from scratch using numpy etc. Or even an ML pipeline…

  • DS audiobook recommendations?

    DS audiobook recommendations? I have a very, very long road trip ahead of me. I would like recommendations for a DS audiobook that can help make the ride easier. submitted by /u/Cross_examination [link] [comments] /u/Cross_examination Go to original source

  • How to Climb the Hidden Career Ladder of Data Science

    How to Climb the Hidden Career Ladder of Data Science The behaviors that get you promoted The post How to Climb the Hidden Career Ladder of Data Science appeared first on Towards Data Science. Greg Rafferty Go to original source

  • The Machine Learning “Advent Calendar” Day 7: Decision Tree Classifier

    The Machine Learning “Advent Calendar” Day 7: Decision Tree Classifier In Day 6, we saw how a Decision Tree Regressor finds its optimal split by minimizing the Mean Squared Error. Today, for Day 7 of the Machine Learning “Advent Calendar”, we switch to classification. With just one numerical feature and two classes, we explore how…

  • Artificial Intelligence, Machine Learning, Deep Learning, and Generative AI — Clearly Explained

    Artificial Intelligence, Machine Learning, Deep Learning, and Generative AI — Clearly Explained Understanding AI in 2026 — from machine learning to generative models The post Artificial Intelligence, Machine Learning, Deep Learning, and Generative AI — Clearly Explained appeared first on Towards Data Science. Sabrine Bendimerad Go to original source

  • Reading Research Papers in the Age of LLMs

    Reading Research Papers in the Age of LLMs How I keep up with papers with a mix of manual and AI-assisted reading The post Reading Research Papers in the Age of LLMs appeared first on Towards Data Science. Parul Pandey Go to original source

  • The Machine Learning “Advent Calendar” Day 6: Decision Tree Regressor

    The Machine Learning “Advent Calendar” Day 6: Decision Tree Regressor During the first days of this Machine Learning Advent Calendar, we explored models based on distances. Today, we switch to a completely different way of learning: Decision Trees. With a simple one-feature dataset, we can see how a tree chooses its first split. The idea…

  • How We Are Testing Our Agents in Dev

    How We Are Testing Our Agents in Dev Testing that your AI agent is performing as expected is not easy. Here are a few strategies we learned the hard way. The post How We Are Testing Our Agents in Dev appeared first on Towards Data Science. Michael Segner Go to original source

  • The Step-by-Step Process of Adding a New Feature to My IOS App with Cursor

    The Step-by-Step Process of Adding a New Feature to My IOS App with Cursor Cursor is great at writing code but not as good when it comes to design The post The Step-by-Step Process of Adding a New Feature to My IOS App with Cursor appeared first on Towards Data Science. Soner Yıldırım Go to…

  • The Machine Learning “Advent Calendar” Day 5: GMM in Excel

    The Machine Learning “Advent Calendar” Day 5: GMM in Excel This article introduces the Gaussian Mixture Model as a natural extension of k-Means, by improving how distance is measured through variances and the Mahalanobis distance. Instead of assigning points to clusters with hard boundaries, GMM uses probabilities learned through the Expectation–Maximization algorithm – the general…

  • A Product Data Scientist’s Take on LinkedIn Games After 500 Days of Play

    A Product Data Scientist’s Take on LinkedIn Games After 500 Days of Play What a simple puzzle game reveals about experimentation, product thinking, and data science The post A Product Data Scientist’s Take on LinkedIn Games After 500 Days of Play appeared first on Towards Data Science. Yu Dong Go to original source

  • YOLOv1 Paper Walkthrough: The Day YOLO First Saw the World

    YOLOv1 Paper Walkthrough: The Day YOLO First Saw the World A detailed walkthrough of the YOLOv1 architecture and its PyTorch implementation from scratch The post YOLOv1 Paper Walkthrough: The Day YOLO First Saw the World appeared first on Towards Data Science. Muhammad Ardi Go to original source

  • On the Challenge of Converting TensorFlow Models to PyTorch

    On the Challenge of Converting TensorFlow Models to PyTorch How to upgrade and optimize legacy AI/ML models The post On the Challenge of Converting TensorFlow Models to PyTorch appeared first on Towards Data Science. Chaim Rand Go to original source

  • Informative missingness and its implications in semi-supervised learning

    Informative missingness and its implications in semi-supervised learning arXiv:2512.04392v1 Announce Type: new Abstract: Semi-supervised learning (SSL) constructs classifiers using both labelled and unlabelled data. It leverages information from labelled samples, whose acquisition is often costly or labour-intensive, together with unlabelled data to enhance prediction performance. This defines an incomplete-data problem, which statistically can be formulated…

  • Recurrent Neural Networks with Linear Structures for Electricity Price Forecasting

    Recurrent Neural Networks with Linear Structures for Electricity Price Forecasting arXiv:2512.04690v1 Announce Type: new Abstract: We present a novel recurrent neural network architecture designed explicitly for day-ahead electricity price forecasting, aimed at improving short-term decision-making and operational management in energy systems. Our combined forecasting model embeds linear structures, such as expert models and Kalman filters,…

  • Provable FDR Control for Deep Feature Selection: Deep MLPs and Beyond

    Provable FDR Control for Deep Feature Selection: Deep MLPs and Beyond arXiv:2512.04696v1 Announce Type: new Abstract: We develop a flexible feature selection framework based on deep neural networks that approximately controls the false discovery rate (FDR), a measure of Type-I error. The method applies to architectures whose first layer is fully connected. From the second…

  • Towards a unified framework for guided diffusion models

    Towards a unified framework for guided diffusion models arXiv:2512.04985v1 Announce Type: new Abstract: Guided or controlled data generation with diffusion modelsblfootnote{Partial preliminary results of this work appeared in International Conference on Machine Learning 2025 citep{li2025provable}.} has become a cornerstone of modern generative modeling. Despite substantial advances in diffusion model theory, the theoretical understanding of guided…

  • Learning Causality for Longitudinal Data

    Learning Causality for Longitudinal Data arXiv:2512.04980v1 Announce Type: new Abstract: This thesis develops methods for causal inference and causal representation learning (CRL) in high-dimensional, time-varying data. The first contribution introduces the Causal Dynamic Variational Autoencoder (CDVAE), a model for estimating Individual Treatment Effects (ITEs) by capturing unobserved heterogeneity in treatment response driven by latent risk…

  • Do Labels Make AI Blind? Self-Supervision Solves the Age-Old Binding Problem

    Do Labels Make AI Blind? Self-Supervision Solves the Age-Old Binding Problem A new NeurIPS 2025 paper shows how self-supervised learning imbues ViT with better image understanding than supervised learning The post Do Labels Make AI Blind? Self-Supervision Solves the Age-Old Binding Problem appeared first on Towards Data Science. Jonathan Williford Go to original source

  • The Machine Learning “Advent Calendar” Day 4: k-Means in Excel

    The Machine Learning “Advent Calendar” Day 4: k-Means in Excel How to implement a training algorithm that finally looks like “real” machine learning The post The Machine Learning “Advent Calendar” Day 4: k-Means in Excel appeared first on Towards Data Science. angela shi Go to original source

  • Build and Deploy Your First Supply Chain App in 20 Minutes

    Build and Deploy Your First Supply Chain App in 20 Minutes A factory operator that discovered happiness by switching from notebook to streamlit – (Image Generated with GPT-5.1 by Samir Saci) The post Build and Deploy Your First Supply Chain App in 20 Minutes appeared first on Towards Data Science. Samir Saci Go to original…

  • Bootstrap a Data Lakehouse in an Afternoon

    Bootstrap a Data Lakehouse in an Afternoon Using Apache Iceberg on AWS with Athena, Glue/Spark and DuckDB The post Bootstrap a Data Lakehouse in an Afternoon appeared first on Towards Data Science. Thomas Reid Go to original source

  • The Best Data Scientists are Always Learning

    The Best Data Scientists are Always Learning Why continuous learning matters & how to come up with topics to study The post The Best Data Scientists are Always Learning appeared first on Towards Data Science. Jarom Hulet Go to original source

  • A note on the impossibility of conditional PAC-efficient reasoning in large language models

    A note on the impossibility of conditional PAC-efficient reasoning in large language models arXiv:2512.03057v1 Announce Type: new Abstract: We prove an impossibility result for conditional Probably Approximately Correct (PAC)-efficient reasoning in large language models. While recent work has established marginal PAC efficiency guarantees for composite models that switch between expensive expert models and cheaper fast…

  • Uncertainty Quantification for Large Language Model Reward Learning under Heterogeneous Human Feedback

    Uncertainty Quantification for Large Language Model Reward Learning under Heterogeneous Human Feedback arXiv:2512.03208v1 Announce Type: new Abstract: We study estimation and statistical inference for reward models used in aligning large language models (LLMs). A key component of LLM alignment is reinforcement learning from human feedback (RLHF), where humans compare pairs of model-generated answers and their…

  • Novelty detection on path space

    Novelty detection on path space arXiv:2512.03243v1 Announce Type: new Abstract: We frame novelty detection on path space as a hypothesis testing problem with signature-based test statistics. Using transportation-cost inequalities of Gasteratos and Jacquier (2023), we obtain tail bounds for false positive rates that extend beyond Gaussian measures to laws of RDE solutions with smooth bounded…

  • Iterative Tilting for Diffusion Fine-Tuning

    Iterative Tilting for Diffusion Fine-Tuning arXiv:2512.03234v1 Announce Type: new Abstract: We introduce iterative tilting, a gradient-free method for fine-tuning diffusion models toward reward-tilted distributions. The method decomposes a large reward tilt $exp(lambda r)$ into $N$ sequential smaller tilts, each admitting a tractable score update via first-order Taylor expansion. This requires only forward evaluations of the…

  • Colored Markov Random Fields for Probabilistic Topological Modeling

    Colored Markov Random Fields for Probabilistic Topological Modeling arXiv:2512.03727v1 Announce Type: new Abstract: Probabilistic Graphical Models (PGMs) encode conditional dependencies among random variables using a graph -nodes for variables, links for dependencies- and factorize the joint distribution into lower-dimensional components. This makes PGMs well-suited for analyzing complex systems and supporting decision-making. Recent advances in topological…

  • The Architecture Behind Web Search in AI Chatbots

    The Architecture Behind Web Search in AI Chatbots And what this means for generative engine optimization (GEO) The post The Architecture Behind Web Search in AI Chatbots appeared first on Towards Data Science. Ida Silfverskiöld Go to original source

  • Overcoming the Hidden Performance Traps of Variable-Shaped Tensors: Efficient Data Sampling in PyTorch

    Overcoming the Hidden Performance Traps of Variable-Shaped Tensors: Efficient Data Sampling in PyTorch PyTorch Model Performance Analysis and Optimization — Part 11 The post Overcoming the Hidden Performance Traps of Variable-Shaped Tensors: Efficient Data Sampling in PyTorch appeared first on Towards Data Science. Chaim Rand Go to original source

  • The Machine Learning “Advent Calendar” Day 3: GNB, LDA and QDA in Excel

    The Machine Learning “Advent Calendar” Day 3: GNB, LDA and QDA in Excel From local distance to global probability The post The Machine Learning “Advent Calendar” Day 3: GNB, LDA and QDA in Excel appeared first on Towards Data Science. angela shi Go to original source

  • How to Turn Your LLM Prototype into a Production-Ready System

    How to Turn Your LLM Prototype into a Production-Ready System The most famous applications of LLMs are the ones that I like to call the “wow effect LLMs.” There are plenty of viral LinkedIn posts about them, and they all sound like this: “I built [x] that does [y] in [z] minutes using AI.” Where:…

  • Multi-Agent Arena: Insights from London Great Agent Hack 2025

    Multi-Agent Arena: Insights from London Great Agent Hack 2025 What mattered: robust agents, glass-box reasoning, and red-team resilience The post Multi-Agent Arena: Insights from London Great Agent Hack 2025 appeared first on Towards Data Science. Erika G. Gonçalves Go to original source

  • Bayesian Physics-Informed Neural Networks for Inverse Problems (BPINN-IP): Application in Infrared Image Processing

    Bayesian Physics-Informed Neural Networks for Inverse Problems (BPINN-IP): Application in Infrared Image Processing arXiv:2512.02495v1 Announce Type: new Abstract: Inverse problems arise across scientific and engineering domains, where the goal is to infer hidden parameters or physical fields from indirect and noisy observations. Classical approaches, such as variational regularization and Bayesian inference, provide well established theoretical…

  • Laplace Approximation For Tensor Train Kernel Machines In System Identification

    Laplace Approximation For Tensor Train Kernel Machines In System Identification arXiv:2512.02532v1 Announce Type: new Abstract: To address the scalability limitations of Gaussian process (GP) regression, several approximation techniques have been proposed. One such method is based on tensor networks, which utilizes an exponential number of basis functions without incurring exponential computational cost. However, extending this…

  • Revisiting Theory of Contrastive Learning for Domain Generalization

    Revisiting Theory of Contrastive Learning for Domain Generalization arXiv:2512.02831v1 Announce Type: new Abstract: Contrastive learning is among the most popular and powerful approaches for self-supervised representation learning, where the goal is to map semantically similar samples close together while separating dissimilar ones in the latent space. Existing theoretical methods assume that downstream task classes are…

  • EcoCast: A Spatio-Temporal Model for Continual Biodiversity and Climate Risk Forecasting

    EcoCast: A Spatio-Temporal Model for Continual Biodiversity and Climate Risk Forecasting arXiv:2512.02260v1 Announce Type: cross Abstract: Increasing climate change and habitat loss are driving unprecedented shifts in species distributions. Conservation professionals urgently need timely, high-resolution predictions of biodiversity risks, especially in ecologically diverse regions like Africa. We propose EcoCast, a spatio-temporal model designed for continual…

  • Spatiotemporal Pyramid Flow Matching for Climate Emulation

    Spatiotemporal Pyramid Flow Matching for Climate Emulation arXiv:2512.02268v1 Announce Type: cross Abstract: Generative models have the potential to transform the way we emulate Earth’s changing climate. Previous generative approaches rely on weather-scale autoregression for climate emulation, but this is inherently slow for long climate horizons and has yet to demonstrate stable rollouts under nonstationary forcings.…

  • The Machine Learning “Advent Calendar” Day 2: k-NN Classifier in Excel

    The Machine Learning “Advent Calendar” Day 2: k-NN Classifier in Excel Exploring the k-NN classifier with its variants and improvements The post The Machine Learning “Advent Calendar” Day 2: k-NN Classifier in Excel appeared first on Towards Data Science. angela shi Go to original source

  • JSON Parsing for Large Payloads: Balancing Speed, Memory, and Scalability

    JSON Parsing for Large Payloads: Balancing Speed, Memory, and Scalability Benchmarking JSON libraries for large payloads The post JSON Parsing for Large Payloads: Balancing Speed, Memory, and Scalability appeared first on Towards Data Science. Subha Ganapathi Go to original source

  • How to Use Simple Data Contracts in Python for Data Scientists

    How to Use Simple Data Contracts in Python for Data Scientists Stop your pipelines from breaking on Friday afternoons using simple, open-source validation with Pandera. The post How to Use Simple Data Contracts in Python for Data Scientists appeared first on Towards Data Science. Eirik Berge Go to original source

  • How to Generate QR Codes in Python

    How to Generate QR Codes in Python A beginner-friendly tutorial exploring the Python “qrcode” Package The post How to Generate QR Codes in Python appeared first on Towards Data Science. Mahnoor Javed Go to original source

  • DAISI: Data Assimilation with Inverse Sampling using Stochastic Interpolants

    DAISI: Data Assimilation with Inverse Sampling using Stochastic Interpolants arXiv:2512.00252v1 Announce Type: new Abstract: Data assimilation (DA) is a cornerstone of scientific and engineering applications, combining model forecasts with sparse and noisy observations to estimate latent system states. Classical DA methods, such as the ensemble Kalman filter, rely on Gaussian approximations and heuristic tuning (e.g.,…

  • An RKHS Perspective on Tree Ensembles

    An RKHS Perspective on Tree Ensembles arXiv:2512.00397v1 Announce Type: new Abstract: Random Forests and Gradient Boosting are among the most effective algorithms for supervised learning on tabular data. Both belong to the class of tree-based ensemble methods, where predictions are obtained by aggregating many randomized regression trees. In this paper, we develop a theoretical framework…

  • No-Regret Gaussian Process Optimization of Time-Varying Functions

    No-Regret Gaussian Process Optimization of Time-Varying Functions arXiv:2512.00517v1 Announce Type: new Abstract: Sequential optimization of black-box functions from noisy evaluations has been widely studied, with Gaussian Process bandit algorithms such as GP-UCB guaranteeing no-regret in stationary settings. However, for time-varying objectives, it is known that no-regret is unattainable under pure bandit feedback unless strong and…

  • Statistical-computational gap in multiple Gaussian graph alignment

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  • The Machine Learning “Advent Calendar” Day 1: k-NN Regressor in Excel

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