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Category: aimldsaimlds

  • How to Use Pre-Trained Language Models for Regression

    How to Use Pre-Trained Language Models for Regression Why and how to convert mT5 into a regression metric for numerical prediction Continue reading on Towards Data Science » Aden Haussmann Go to original source

  • Satellite Image Classification with Deep Learning — Complete Project

    Satellite Image Classification with Deep Learning — Complete Project A Comprehensive Guide Using PyTorch and CNNs Continue reading on Towards Data Science » Leo Anello Go to original source

  • My Experience Switching From Power BI to Looker (as a Senior Data Analyst)

    My Experience Switching From Power BI to Looker (as a Senior Data Analyst) What you need to know before you switch from Power BI to Looker. Continue reading on Towards Data Science » Tomas Jancovic (It’s AI Thomas) Go to original source

  • Where to Start When Data is Limited

    Where to Start When Data is Limited A launch pad for projects with small datasets Photo by Google DeepMind: https://www.pexels.com/photo/an-artist-s-illustration-of-artificial-intelligence-ai-this-image-depicts-how-ai-can-help-humans-to-understand-the-complexity-of-biology-it-was-created-by-artist-khyati-trehan-as-part-17484975/ Machine Learning (ML) has driven remarkable breakthroughs in computer vision, natural language processing, and speech recognition, largely due to the abundance of data in these fields. However, many challenges — especially those tied to specific product features or…

  • 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,…

  • A Practical Exploration of Sora — Intuitively and Exhaustively Explained

    A Practical Exploration of Sora — Intuitively and Exhaustively Explained A new cutting edge video generation tool, and the theory behind it Continue reading on Towards Data Science » Daniel Warfield Go to original source

  • Generative Models with ELBOs Converging to Entropy Sums

    Generative Models with ELBOs Converging to Entropy Sums arXiv:2501.09022v1 Announce Type: new Abstract: The evidence lower bound (ELBO) is one of the most central objectives for probabilistic unsupervised learning. For the ELBOs of several generative models and model classes, we here prove convergence to entropy sums. As one result, we provide a list of generative…

  • Estimating shared subspace with AJIVE: the power and limitation of multiple data matrices

    Estimating shared subspace with AJIVE: the power and limitation of multiple data matrices arXiv:2501.09336v1 Announce Type: new Abstract: Integrative data analysis often requires disentangling joint and individual variations across multiple datasets, a challenge commonly addressed by the Joint and Individual Variation Explained (JIVE) model. While numerous methods have been developed to estimate the shared subspace…

  • On the convergence of noisy Bayesian Optimization with Expected Improvement

    On the convergence of noisy Bayesian Optimization with Expected Improvement arXiv:2501.09262v1 Announce Type: new Abstract: Expected improvement (EI) is one of the most widely-used acquisition functions in Bayesian optimization (BO). Despite its proven success in applications for decades, important open questions remain on the theoretical convergence behaviors and rates for EI. In this paper, we…

  • Predictions as Surrogates: Revisiting Surrogate Outcomes in the Age of AI

    Predictions as Surrogates: Revisiting Surrogate Outcomes in the Age of AI arXiv:2501.09731v1 Announce Type: new Abstract: We establish a formal connection between the decades-old surrogate outcome model in biostatistics and economics and the emerging field of prediction-powered inference (PPI). The connection treats predictions from pre-trained models, prevalent in the age of AI, as cost-effective surrogates…

  • Gradient Descent Converges Linearly to Flatter Minima than Gradient Flow in Shallow Linear Networks

    Gradient Descent Converges Linearly to Flatter Minima than Gradient Flow in Shallow Linear Networks arXiv:2501.09137v1 Announce Type: cross Abstract: We study the gradient descent (GD) dynamics of a depth-2 linear neural network with a single input and output. We show that GD converges at an explicit linear rate to a global minimum of the training…

  • Learnings from a Machine Learning Engineer — Part 4: The Model

    Learnings from a Machine Learning Engineer — Part 4: The Model Practical insights for a data-driven approach to model optimization Continue reading on Towards Data Science » David Martin Go to original source

  • 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

  • Learnings from a Machine Learning Engineer — Part 2: The Data Sets

    Learnings from a Machine Learning Engineer — Part 2: The Data Sets Practical insights for a data-driven approach to model optimization Continue reading on Towards Data Science » David Martin Go to original source

  • Top 3 Questions to Ask in Near Real-Time Data Solutions

    Top 3 Questions to Ask in Near Real-Time Data Solutions Questions that guide architectural decisions to balance functional requirements with non-functional ones, like latency and scalability Continue reading on Towards Data Science » Shawn Shi Go to original source

  • The Data Analyst Every CEO Wants

    The Data Analyst Every CEO Wants Data Analyst is probably the most underrated job in the data industry Continue reading on Towards Data Science » Benoit Pimpaud Go to original source

  • A Constant Velocity Latent Dynamics Approach for Accelerating Simulation of Stiff Nonlinear Systems

    A Constant Velocity Latent Dynamics Approach for Accelerating Simulation of Stiff Nonlinear Systems arXiv:2501.08423v1 Announce Type: new Abstract: Solving stiff ordinary differential equations (StODEs) requires sophisticated numerical solvers, which are often computationally expensive. In particular, StODE’s often cannot be solved with traditional explicit time integration schemes and one must resort to costly implicit methods to…

  • Causal vs. Anticausal merging of predictors

    Causal vs. Anticausal merging of predictors arXiv:2501.08426v1 Announce Type: cross Abstract: We study the differences arising from merging predictors in the causal and anticausal directions using the same data. In particular we study the asymmetries that arise in a simple model where we merge the predictors using one binary variable as target and two continuous…

  • A Theory of Optimistically Universal Online Learnability for General Concept Classes

    A Theory of Optimistically Universal Online Learnability for General Concept Classes arXiv:2501.08551v1 Announce Type: new Abstract: We provide a full characterization of the concept classes that are optimistically universally online learnable with ${0, 1}$ labels. The notion of optimistically universal online learning was defined in [Hanneke, 2021] in order to understand learnability under minimal assumptions.…

  • Quantum Reservoir Computing and Risk Bounds

    Quantum Reservoir Computing and Risk Bounds arXiv:2501.08640v1 Announce Type: cross Abstract: We propose a way to bound the generalisation errors of several classes of quantum reservoirs using the Rademacher complexity. We give specific, parameter-dependent bounds for two particular quantum reservoir classes. We analyse how the generalisation bounds scale with growing numbers of qubits. Applying our…

  • Diagonal Over-parameterization in Reproducing Kernel Hilbert Spaces as an Adaptive Feature Model: Generalization and Adaptivity

    Diagonal Over-parameterization in Reproducing Kernel Hilbert Spaces as an Adaptive Feature Model: Generalization and Adaptivity arXiv:2501.08679v1 Announce Type: cross Abstract: This paper introduces a diagonal adaptive kernel model that dynamically learns kernel eigenvalues and output coefficients simultaneously during training. Unlike fixed-kernel methods tied to the neural tangent kernel theory, the diagonal adaptive kernel model adapts…

  • A 12-step visual guide to understanding NeRF (Representing Scenes as Neural Radiance Fields)

    A 12-step visual guide to understanding NeRF (Representing Scenes as Neural Radiance Fields) NeRF overview — Image by Author A Beginner’s 12-Step Visual Guide to Understanding NeRF: Neural Radiance Fields for Scene Representation and View Synthesis A basic understanding of NeRF’s workings through visual representations Who should read this article? This article aims to provide a basic beginner level…

  • Basics of GANs & SMOTE for Data Augmentation

    Basics of GANs & SMOTE for Data Augmentation GANs and SMOTE Explained with Bartending: Data Science for Machine Learning Series (1) Continue reading on Towards Data Science » Sunghyun Ahn Go to original source

  • Learnings from a Machine Learning Engineer — Part 1: The Data

    Learnings from a Machine Learning Engineer — Part 1: The Data Practical insights for a data-driven approach to model optimization Continue reading on Towards Data Science » David Martin Go to original source

  • Water Cooler Small Talk: Benford’s Law

    Water Cooler Small Talk: Benford’s Law A look into the strange first digit distribution of naturally occurring datasets Continue reading on Towards Data Science » Maria Mouschoutzi, PhD Go to original source

  • Qubits Explained: Everything You Need to Know

    Qubits Explained: Everything You Need to Know A deep dive into the building block of quantum computers. Continue reading on Towards Data Science » Sara A. Metwalli Go to original source

  • Concentration of Measure for Distributions Generated via Diffusion Models

    Concentration of Measure for Distributions Generated via Diffusion Models arXiv:2501.07741v1 Announce Type: new Abstract: We show via a combination of mathematical arguments and empirical evidence that data distributions sampled from diffusion models satisfy a Concentration of Measure Property saying that any Lipschitz $1$-dimensional projection of a random vector is not too far from its mean…

  • On the use of Statistical Learning Theory for model selection in Structural Health Monitoring

    On the use of Statistical Learning Theory for model selection in Structural Health Monitoring arXiv:2501.08050v1 Announce Type: new Abstract: Whenever data-based systems are employed in engineering applications, defining an optimal statistical representation is subject to the problem of model selection. This paper focusses on how well models can generalise in Structural Health Monitoring (SHM). Although…

  • On the Statistical Capacity of Deep Generative Models

    On the Statistical Capacity of Deep Generative Models arXiv:2501.07763v1 Announce Type: new Abstract: Deep generative models are routinely used in generating samples from complex, high-dimensional distributions. Despite their apparent successes, their statistical properties are not well understood. A common assumption is that with enough training data and sufficiently large neural networks, deep generative model samples…

  • Globally Convergent Variational Inference

    Globally Convergent Variational Inference arXiv:2501.08201v1 Announce Type: new Abstract: In variational inference (VI), an approximation of the posterior distribution is selected from a family of distributions through numerical optimization. With the most common variational objective function, known as the evidence lower bound (ELBO), only convergence to a local optimum can be guaranteed. In this work,…

  • Avoiding subtraction and division of stochastic signals using normalizing flows: NFdeconvolve

    Avoiding subtraction and division of stochastic signals using normalizing flows: NFdeconvolve arXiv:2501.08288v1 Announce Type: new Abstract: Across the scientific realm, we find ourselves subtracting or dividing stochastic signals. For instance, consider a stochastic realization, $x$, generated from the addition or multiplication of two stochastic signals $a$ and $b$, namely $x=a+b$ or $x = ab$. For…

  • Hands-On Delivery Routes Optimization (TSP) with AI, Using LKH and Python

    Hands-On Delivery Routes Optimization (TSP) with AI, Using LKH and Python Here’s how to optimize the delivery routes, from theory to code. Continue reading on Towards Data Science » Piero Paialunga Go to original source

  • How To: Forecast Time Series Using Lags

    How To: Forecast Time Series Using Lags Lag columns can significantly boost your model’s performance Continue reading on Towards Data Science » Haden Pelletier Go to original source

  • Static and Dynamic Attention: Implications for Graph Neural Networks

    Static and Dynamic Attention: Implications for Graph Neural Networks Examining the expressive capacity of Graph Attention Networks Image by the author In graph representation learning, neighborhood aggregation is one of the most well-studied and investigated areas, among which attention-based methods largely remain state-of-the-art. Leveraging learnable attention scores for weighted aggregations, graph attention networks exhibit higher expressivity…

  • Deep Dive into KV-Caching In Mistral

    Deep Dive into KV-Caching In Mistral Ever wondered why the time to first token in LLMs is high but subsequent tokens are super fast? In this post, I dive into the details of KV-Caching used in Mistral, a topic I initially found quite daunting. However, as I delved deeper, it became a fascinating subject, especially when…

  • Scale Experiment Decision-Making with Programmatic Decision Rules

    Scale Experiment Decision-Making with Programmatic Decision Rules Decide what to do with experiment results in code Photo by Cytonn Photography on Unsplash The experiment lifecycle is like the human lifecycle. First, a person or idea is born, then it develops, then it is tested, then its test ends, and then the Gods (or Product Managers) decide its worth.…

  • Counterfactually Fair Reinforcement Learning via Sequential Data Preprocessing

    Counterfactually Fair Reinforcement Learning via Sequential Data Preprocessing arXiv:2501.06366v1 Announce Type: new Abstract: When applied in healthcare, reinforcement learning (RL) seeks to dynamically match the right interventions to subjects to maximize population benefit. However, the learned policy may disproportionately allocate efficacious actions to one subpopulation, creating or exacerbating disparities in other socioeconomically-disadvantaged subgroups. These biases…

  • Computational and Statistical Asymptotic Analysis of the JKO Scheme for Iterative Algorithms to update distributions

    Computational and Statistical Asymptotic Analysis of the JKO Scheme for Iterative Algorithms to update distributions arXiv:2501.06408v1 Announce Type: new Abstract: The seminal paper of Jordan, Kinderlehrer, and Otto introduced what is now widely known as the JKO scheme, an iterative algorithmic framework for computing distributions. This scheme can be interpreted as a Wasserstein gradient flow…

  • Variable Selection Methods for Multivariate, Functional, and Complex Biomedical Data in the AI Age

    Variable Selection Methods for Multivariate, Functional, and Complex Biomedical Data in the AI Age arXiv:2501.06868v1 Announce Type: new Abstract: Many problems within personalized medicine and digital health rely on the analysis of continuous-time functional biomarkers and other complex data structures emerging from high-resolution patient monitoring. In this context, this work proposes new optimization-based variable selection…

  • Dynamic Causal Structure Discovery and Causal Effect Estimation

    Dynamic Causal Structure Discovery and Causal Effect Estimation arXiv:2501.06534v1 Announce Type: new Abstract: To represent the causal relationships between variables, a directed acyclic graph (DAG) is widely utilized in many areas, such as social sciences, epidemics, and genetics. Many causal structure learning approaches are developed to learn the hidden causal structure utilizing deep-learning approaches. However,…

  • Automatic Double Reinforcement Learning in Semiparametric Markov Decision Processes with Applications to Long-Term Causal Inference

    Automatic Double Reinforcement Learning in Semiparametric Markov Decision Processes with Applications to Long-Term Causal Inference arXiv:2501.06926v1 Announce Type: new Abstract: Double reinforcement learning (DRL) enables statistically efficient inference on the value of a policy in a nonparametric Markov Decision Process (MDP) given trajectories generated by another policy. However, this approach necessarily requires stringent overlap between…

  • Machine Learning: From 0 to Something

    Machine Learning: From 0 to Something How I learned ML foundations to tackle a complex problem Continue reading on Towards Data Science » Ricardo Ribas Go to original source

  • Four Ways to Improve Statistical Power in A/B Testing (Without Increasing Test Duration, Duh)

    Four Ways to Improve Statistical Power in A/B Testing (Without Increasing Test Duration, Duh) In A/B testing, you often have to balance statistical power and how long the test takes. Learn how Allocation, Effect Size, CUPED & Binarization can help you. Image by author In A/B testing, you often have to balance statistical power and how long…

  • The AI (R)Evolution, Looking From 2024 Into the Immediate Future

    The AI (R)Evolution, Looking From 2024 Into the Immediate Future Witnessing rapid innovation, fierce competition, and transformative tools for life, work, and human development Continue reading on Towards Data Science » LucianoSphere (Luciano Abriata, PhD) Go to original source

  • Contextual Topic Modelling in Chinese Corpora with KeyNMF

    Contextual Topic Modelling in Chinese Corpora with KeyNMF A comprehensive guide on getting the most out of your Chinese topic models, from preprocessing to interpretation. With our recent paper on discourse dynamics in European Chinese diaspora media, our team has tapped into an almost unanimous frustration with the quality of topic modelling approaches when applied…

  • llama.cpp: Writing A Simple C++ Inference Program for GGUF LLM Models

    llama.cpp: Writing A Simple C++ Inference Program for GGUF LLM Models Exploring llama.cpp internals and a basic chat program flow Photo by Mathew Schwartz on Unsplash llama.cpp has revolutionized the space of LLM inference by the means of wide adoption and simplicity. It has enabled enterprises and individual developers to deploy LLMs on devices ranging from SBCs…

  • Covariate Dependent Mixture of Bayesian Networks

    Covariate Dependent Mixture of Bayesian Networks arXiv:2501.05745v1 Announce Type: new Abstract: Learning the structure of Bayesian networks from data provides insights into underlying processes and the causal relationships that generate the data, but its usefulness depends on the homogeneity of the data population, a condition often violated in real-world applications. In such cases, using a…

  • Outlyingness Scores with Cluster Catch Digraphs

    Outlyingness Scores with Cluster Catch Digraphs arXiv:2501.05530v1 Announce Type: new Abstract: This paper introduces two novel, outlyingness scores (OSs) based on Cluster Catch Digraphs (CCDs): Outbound Outlyingness Score (OOS) and Inbound Outlyingness Score (IOS). These scores enhance the interpretability of outlier detection results. Both OSs employ graph-, density-, and distribution-based techniques, tailored to high-dimensional data…

  • Analog Bayesian neural networks are insensitive to the shape of the weight distribution

    Analog Bayesian neural networks are insensitive to the shape of the weight distribution arXiv:2501.05564v1 Announce Type: cross Abstract: Recent work has demonstrated that Bayesian neural networks (BNN’s) trained with mean field variational inference (MFVI) can be implemented in analog hardware, promising orders of magnitude energy savings compared to the standard digital implementations. However, while Gaussians…

  • rmlnomogram: An R package to construct an explainable nomogram for any machine learning algorithms

    rmlnomogram: An R package to construct an explainable nomogram for any machine learning algorithms arXiv:2501.05772v1 Announce Type: cross Abstract: Background: Current nomogram can only be created for regression algorithm. Providing nomogram for any machine learning (ML) algorithms may accelerate model deployment in clinical settings or improve model availability. We developed an R package and web…

  • Random Sparse Lifts: Construction, Analysis and Convergence of finite sparse networks

    Random Sparse Lifts: Construction, Analysis and Convergence of finite sparse networks arXiv:2501.05930v1 Announce Type: cross Abstract: We present a framework to define a large class of neural networks for which, by construction, training by gradient flow provably reaches arbitrarily low loss when the number of parameters grows. Distinct from the fixed-space global optimality of non-convex…

  • Weekly Entering & Transitioning – Thread 13 Jan, 2025 – 20 Jan, 2025

    Weekly Entering & Transitioning – Thread 13 Jan, 2025 – 20 Jan, 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.…

  • Where do you go to stay up to date on data analytics/science?

    Where do you go to stay up to date on data analytics/science? Are there any people or organizations you follow on Youtube, Twitter, Medium, LinkedIn, or some other website/blog/podcast that you always tend to keep going back to? My previous career absolutely lacked all the professional “content creators” that data analytics have, so I was…

  • Is data science at meta just a/b testing?

    Is data science at meta just a/b testing? I’ve been at Meta a year and all I do is run a/b tests. In my old jobs I used to build models and products using data science. Does this happen under a different job title here or am I just in wrong department? submitted by /u/Longjumping-Will-127…

  • How we matured Fisher, our A/B testing library

    How we matured Fisher, our A/B testing library submitted by /u/chomoloc0 [link] [comments] /u/chomoloc0 Go to original source

  • 200 applications – no response, please help. I have applied for data science (associate or mid-level) positions. Thank you

    200 applications – no response, please help. I have applied for data science (associate or mid-level) positions. Thank you submitted by /u/Sad_Campaign713 [link] [comments] /u/Sad_Campaign713 Go to original source

  • Using Constraint Programming to Solve Math Theorems

    Using Constraint Programming to Solve Math Theorems Case study: the quasigroups existence problem TLDR Some mathematical theorems can be solved by combinatorial exploration. In this article, we focus on the problem of the existence of some quasigroups. We will demonstrate the existence or non existence of some quasigroups using NuCS. NuCs is a fast constraint…

  • What is MicroPython? Do I Need to Know it as a Data Scientist?

    What is MicroPython? Do I Need to Know it as a Data Scientist? In this year’s edition of the Stack Overflow survey, MicroPython is with 1.6% in the Most Popular Technologies — but why? Continue reading on Towards Data Science » Sarah Lea Go to original source

  • Your Classifier Is Broken, But It Is Still Useful

    Your Classifier Is Broken, But It Is Still Useful When you run a binary classifier over a population you get an estimate of the proportion of true positives in that population. This is known as the prevalence. Photo by Rod Long on Unsplash But that estimate is biased, because no classifier is perfect. For example, if…

  • What Would a Stoic Do? — An AI-Based Decision-Making Model

    What Would a Stoic Do? — An AI-Based Decision-Making Model Using AI to build Marcus Aurelius’ reincarnation Continue reading on Towards Data Science » Pol Marin Go to original source

  • LightGBM: The Fastest Option of Gradient Boosting

    LightGBM: The Fastest Option of Gradient Boosting Learn how to implement a fast and effective Gradient Boosting model using Python Continue reading on Towards Data Science » Gustavo R Santos Go to original source

  • Machine Learning + openAI: solving a text classification problem

    Machine Learning + openAI: solving a text classification problem How I migrated an old solution to a more elegant, robust and scalable solution using text classification from openAI Continue reading on Towards Data Science » Ricardo Ribas Go to original source

  • Exploring New Hyperparameter Dimensions with Laplace Approximated Bayesian Optimization

    Exploring New Hyperparameter Dimensions with Laplace Approximated Bayesian Optimization Is it better than grid search? Image by author from canva When I notice my model is overfitting, I often think, “It is time to regularize”. But how do I decide which regularization method to use (L1, L2) and what parameters to choose? Typically, I perform hyperparameter optimization…

  • Building Visual Agents that can Navigate the Web Autonomously

    Building Visual Agents that can Navigate the Web Autonomously A step-by-step guide to creating visual agents that can navigate the web autonomously Continue reading on Towards Data Science » Luís Roque Go to original source

  • A Visual Understanding of Neural Networks

    A Visual Understanding of Neural Networks The math behind neural networks visually explained Continue reading on Towards Data Science » Reza Bagheri Go to original source

  • 3 Powerful Examples of the Python Re Library

    3 Powerful Examples of the Python Re Library Explore the power of regex and save time in data analysis Continue reading on Towards Data Science » Suraj Gurav Go to original source

  • Solving A Rubik’s Cube with Supervised Learning — Intuitively and Exhaustively Explained

    Solving A Rubik’s Cube with Supervised Learning — Intuitively and Exhaustively Explained A Popular Toy in a Brave New World Continue reading on Towards Data Science » Daniel Warfield Go to original source

  • Model Calibration, Explained: A Visual Guide with Code Examples for Beginners

    Model Calibration, Explained: A Visual Guide with Code Examples for Beginners MODEL EVALUATION & OPTIMIZATION When all models have similar accuracy, now what? You’ve trained several classification models, and they all seem to be performing well with high accuracy scores. Congratulations! But hold on — is one model truly better than the others? Accuracy alone doesn’t tell the…

  • Sustainable Business Strategy with Data Analytics

    Sustainable Business Strategy with Data Analytics Use data analytics to help companies design and implement strategic sustainability roadmaps to reduce their environmental footprint. Sustainable Business Strategy with Analytics — (Image by Samir Saci) Consensus means that everyone agrees to say collectively what no one believes individually. This quote captures a critical issue many companies face during their strategic…

  • Linearizing Llama

    Linearizing Llama Speeding up Llama: A hybrid approach to attention mechanisms Source: Image by Author (Generated using Gemini 1.5 Flash) In this article, we will see how to replace softmax self-attention in Llama-3.2-1B with hybrid attention combining softmax sliding window and linear attention. This implementation will help us better understand the growing interest in linear attention…

  • Deep Transfer $Q$-Learning for Offline Non-Stationary Reinforcement Learning

    Deep Transfer $Q$-Learning for Offline Non-Stationary Reinforcement Learning arXiv:2501.04870v1 Announce Type: new Abstract: In dynamic decision-making scenarios across business and healthcare, leveraging sample trajectories from diverse populations can significantly enhance reinforcement learning (RL) performance for specific target populations, especially when sample sizes are limited. While existing transfer learning methods primarily focus on linear regression settings,…

  • RieszBoost: Gradient Boosting for Riesz Regression

    RieszBoost: Gradient Boosting for Riesz Regression arXiv:2501.04871v1 Announce Type: new Abstract: Answering causal questions often involves estimating linear functionals of conditional expectations, such as the average treatment effect or the effect of a longitudinal modified treatment policy. By the Riesz representation theorem, these functionals can be expressed as the expected product of the conditional expectation…

  • Towards understanding the bias in decision trees

    Towards understanding the bias in decision trees arXiv:2501.04903v1 Announce Type: new Abstract: There is a widespread and longstanding belief that machine learning models are biased towards the majority (or negative) class when learning from imbalanced data, leading them to neglect or ignore the minority (or positive) class. In this study, we show that this belief…

  • Optimality and Adaptivity of Deep Neural Features for Instrumental Variable Regression

    Optimality and Adaptivity of Deep Neural Features for Instrumental Variable Regression arXiv:2501.04898v1 Announce Type: new Abstract: We provide a convergence analysis of deep feature instrumental variable (DFIV) regression (Xu et al., 2021), a nonparametric approach to IV regression using data-adaptive features learned by deep neural networks in two stages. We prove that the DFIV algorithm…

  • Non-asymptotic analysis of the performance of the penalized least trimmed squares in sparse models

    Non-asymptotic analysis of the performance of the penalized least trimmed squares in sparse models arXiv:2501.04946v1 Announce Type: new Abstract: The least trimmed squares (LTS) estimator is a renowned robust alternative to the classic least squares estimator and is popular in location, regression, machine learning, and AI literature. Many studies exist on LTS, including its robustness,…

  • The Best Way to Prepare for Data Science and Machine Learning Interviews

    The Best Way to Prepare for Data Science and Machine Learning Interviews Never get stumped again Continue reading on Towards Data Science » Marina Wyss – Gratitude Driven Go to original source

  • Sentiment Analysis with Transformers: A Complete Deep Learning Project — PT. I

    Sentiment Analysis with Transformers: A Complete Deep Learning Project — PT. I Master Fine-Tuning Transformers, Comparing Deep Learning Architectures, and Deploying Sentiment Analysis Models Continue reading on Towards Data Science » Leo Anello Go to original source

  • What to Do If the Logit Decision Boundary Fails?

    What to Do If the Logit Decision Boundary Fails? Feature engineering for classification models using Bayesian Machine Learning Continue reading on Towards Data Science » Lukasz Gatarek Go to original source

  • How to Run Jupyter Notebooks and Generate HTML Reports with Python Scripts

    How to Run Jupyter Notebooks and Generate HTML Reports with Python Scripts A step-by-step guide to automating Jupyter Notebook execution and report generation using Python Continue reading on Towards Data Science » Amanda Iglesias Moreno Go to original source

  • Building Autonomous Multi-Tool Agents with Gemini 2.0 and LangGraph

    Building Autonomous Multi-Tool Agents with Gemini 2.0 and LangGraph A practical tutorial with full code examples for building and running multi-tool agents Continue reading on Towards Data Science » Youness Mansar Go to original source

  • Mixing Times and Privacy Analysis for the Projected Langevin Algorithm under a Modulus of Continuity

    Mixing Times and Privacy Analysis for the Projected Langevin Algorithm under a Modulus of Continuity arXiv:2501.04134v1 Announce Type: new Abstract: We study the mixing time of the projected Langevin algorithm (LA) and the privacy curve of noisy Stochastic Gradient Descent (SGD), beyond nonexpansive iterations. Specifically, we derive new mixing time bounds for the projected LA…

  • Generation from Noisy Examples

    Generation from Noisy Examples arXiv:2501.04179v1 Announce Type: new Abstract: We continue to study the learning-theoretic foundations of generation by extending the results from Kleinberg and Mullainathan [2024] and Li et al. [2024] to account for noisy example streams. In the noiseless setting of Kleinberg and Mullainathan [2024] and Li et al. [2024], an adversary picks…

  • Statistical Uncertainty Quantification for Aggregate Performance Metrics in Machine Learning Benchmarks

    Statistical Uncertainty Quantification for Aggregate Performance Metrics in Machine Learning Benchmarks arXiv:2501.04234v1 Announce Type: new Abstract: Modern artificial intelligence is supported by machine learning models (e.g., foundation models) that are pretrained on a massive data corpus and then adapted to solve a variety of downstream tasks. To summarize performance across multiple tasks, evaluation metrics are…

  • Circuit Complexity Bounds for Visual Autoregressive Model

    Circuit Complexity Bounds for Visual Autoregressive Model arXiv:2501.04299v1 Announce Type: new Abstract: Understanding the expressive ability of a specific model is essential for grasping its capacity limitations. Recently, several studies have established circuit complexity bounds for Transformer architecture. Besides, the Visual AutoRegressive (VAR) model has risen to be a prominent method in the field of…

  • On weight and variance uncertainty in neural networks for regression tasks

    On weight and variance uncertainty in neural networks for regression tasks arXiv:2501.04272v1 Announce Type: new Abstract: We consider the problem of weight uncertainty proposed by [Blundell et al. (2015). Weight uncertainty in neural network. In International conference on machine learning, 1613-1622, PMLR.] in neural networks {(NNs)} specialized for regression tasks. {We further} investigate the effect…

  • Missing Data in Time-Series? Machine Learning Techniques (Part 2)

    Missing Data in Time-Series? Machine Learning Techniques (Part 2) Using Clustering Algorithms to Handle Missing Time-Series Data Continue reading on Towards Data Science » Sara Nóbrega Go to original source

  • Advanced SQL Techniques for Unstructured Data Handling

    Advanced SQL Techniques for Unstructured Data Handling Everything you need to know to get started with text mining Continue reading on Towards Data Science » Jiayan Yin Go to original source

  • Bayesian A/B Testing Falls Short

    Bayesian A/B Testing Falls Short Why Bayesian A/B testing can lead to misunderstandings, inflated false positive rates, introduce bias and complicate results (Image generated by the author using Midjourney) Over the past decade, I’ve engaged in countless discussions about Bayesian A/B testing versus Frequentist A/B testing. In nearly every conversation, I’ve maintained the same viewpoint:…

  • Method of Moments Estimation with Python Code

    Method of Moments Estimation with Python Code How to understand and implement the estimator from scratch Photo by Petr Macháček on Unsplash Let’s say you are in a customer care center, and you would like to know the probability distribution of the number of calls per minute, or in other words, you want to answer the question:…

  • Statistical Learnability of Strategic Linear Classifiers: A Proof Walkthrough

    Statistical Learnability of Strategic Linear Classifiers: A Proof Walkthrough With the help of an intricate geometric construction, we can prove that instance-wise cost functions quickly drive SVC to infinity. In the previous article in this series, we examined the concept of strategic VC dimension (SVC) and its connection to the Fundamental Theorem of Strategic Learning.…

  • Class-Balance Bias in Regularized Regression

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