technocracy

arXiv:2602.22381v1 Announce Type: new Abstract: Accurate prediction of malignancy in renal tumors is crucial for informing clinical decisions and optimizing treatment strategies. However, existing imaging modalities lack the necessary accuracy to reliably predict malignancy before surgical intervention. While deep learning has shown promise in malignancy prediction using 3D CT images, traditional approaches often rely on manual segmentation to isolate the tumor region and reduce noise, which enhances predictive performance. Manual segmentation, however, is labor-intensive, costly, and dependent on […]

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This paper surveys recent developments at the intersection of operator learning, statistical learning theory, and approximation theory. First, it reviews error bounds for empirical risk minimization with a focus on holomorphic operators and neural network approximations. Next, it illustrates fundamental performance limits in terms of sample size by adopting a minimax perspective and considering various notions of regularity beyond holomorphy. The paper ends with a discussion on the interplay between these two perspectives and related open questions.

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arXiv:2510.04441v2 Announce Type: replace-cross Abstract: Domain generalization (DG) is the problem of generalizing from several distributions (or domains), for which labeled training data are available, to a new test domain for which no labeled data is available. For the prevailing benchmark datasets in DG, there exists a single classifier that performs well across all domains. In this work, we study a fundamentally different regime where the domains satisfy a emph{posterior drift} assumption, in which the optimal classifier might […]

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I’ve been spending time with Apache ADBC (Arrow Database Connectivity) and DuckLake (lakehouse architecture using DuckDB) to read columnar data. I realized XGBoost took Arrow tables as a data input and I was able to pass arrow tables with little memory overhead to train. I also wanted to try to not use scikit-learn so I built a train and test split function with PyArrow instead. ADBC also allows you to stream larger than memory data and train a […]

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arXiv:2601.22259v1 Announce Type: new Abstract: While tabular foundation models have achieved remarkable success in classification and regression, adapting them to model time-to-event outcomes for survival analysis is non-trivial due to right-censoring, where data observations may end before the event occurs. We develop a classification-based framework that reformulates both static and dynamic survival analysis as a series of binary classification problems by discretizing event times. Censored observations are naturally handled as examples with missing labels at certain time points. […]

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arXiv:2601.17265v1 Announce Type: cross Abstract: The grade of membership model is a flexible latent variable model for analyzing multivariate categorical data through individual-level mixed membership scores. In many modern applications, auxiliary covariates are collected alongside responses and encode information about the same latent structure. Traditional approaches to incorporating such covariates typically rely on fully specified joint likelihoods, which are computationally intensive and sensitive to misspecification. We introduce a covariate-assisted grade of membership model that integrates response and covariate […]

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arXiv:2505.17004v3 Announce Type: replace-cross Abstract: We propose a general framework for conditional sampling in PDE-based inverse problems, targeting the recovery of whole solutions from extremely sparse or noisy measurements. This is accomplished by a function-space diffusion model and plug-and-play guidance for conditioning. Our method first trains an unconditional, discretization-agnostic denoising model using neural operator architectures. At inference, we refine the samples to satisfy sparse observation data via a gradient-based guidance mechanism. Through rigorous mathematical analysis, we extend Tweedie’s […]

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Hi all, We just released v1.1.2 of PerpetualBooster. For those who haven’t seen it, it’s a gradient boosting machine (GBM) written in Rust that eliminates the need for hyperparameter optimization by using a generalization algorithm controlled by a single “budget” parameter. This update focuses on performance, stability, and ecosystem integration. Key Technical Updates: – Performance: up to 2x faster training. – Ecosystem: Full R release, ONNX support, and native “Save as XGBoost” for interoperability. – Python Support: Added […]

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