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Tag: conditional

  • A Bayesian Generative Modeling Approach for Arbitrary Conditional Inference

    A Bayesian Generative Modeling Approach for Arbitrary Conditional Inference arXiv:2601.05355v1 Announce Type: new Abstract: Modern data analysis increasingly requires flexible conditional inference P(X_B | X_A) where (X_A, X_B) is an arbitrary partition of observed variable X. Existing conditional inference methods lack this flexibility as they are tied to a fixed conditioning structure and cannot perform…

  • On the Hardness of Conditional Independence Testing In Practice

    On the Hardness of Conditional Independence Testing In Practice arXiv:2512.14000v1 Announce Type: new Abstract: Tests of conditional independence (CI) underpin a number of important problems in machine learning and statistics, from causal discovery to evaluation of predictor fairness and out-of-distribution robustness. Shah and Peters (2020) showed that, contrary to the unconditional case, no universally finite-sample…

  • 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…

  • 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…

  • Beyond Linear Diffusions: Improved Representations for Rare Conditional Generative Modeling

    Beyond Linear Diffusions: Improved Representations for Rare Conditional Generative Modeling arXiv:2510.02499v1 Announce Type: new Abstract: Diffusion models have emerged as powerful generative frameworks with widespread applications across machine learning and artificial intelligence systems. While current research has predominantly focused on linear diffusions, these approaches can face significant challenges when modeling a conditional distribution, $P(Y|X=x)$, when…

  • One-shot Conditional Sampling: MMD meets Nearest Neighbors

    One-shot Conditional Sampling: MMD meets Nearest Neighbors arXiv:2509.25507v1 Announce Type: new Abstract: How can we generate samples from a conditional distribution that we never fully observe? This question arises across a broad range of applications in both modern machine learning and classical statistics, including image post-processing in computer vision, approximate posterior sampling in simulation-based inference,…

  • Conditional Multidimensional Scaling with Incomplete Conditioning Data

    Conditional Multidimensional Scaling with Incomplete Conditioning Data arXiv:2509.16627v1 Announce Type: new Abstract: Conditional multidimensional scaling seeks for a low-dimensional configuration from pairwise dissimilarities, in the presence of other known features. By taking advantage of available data of the known features, conditional multidimensional scaling improves the estimation quality of the low-dimensional configuration and simplifies knowledge discovery…

  • Conditional Normalizing Flow Surrogate for Monte Carlo Prediction of Radiative Properties in Nanoparticle-Embedded Layers

    Conditional Normalizing Flow Surrogate for Monte Carlo Prediction of Radiative Properties in Nanoparticle-Embedded Layers arXiv:2508.19841v1 Announce Type: new Abstract: We present a probabilistic, data-driven surrogate model for predicting the radiative properties of nanoparticle embedded scattering media. The model uses conditional normalizing flows, which learn the conditional distribution of optical outputs, including reflectance, absorbance, and transmittance,…

  • Non-asymptotic convergence bound of conditional diffusion models

    Non-asymptotic convergence bound of conditional diffusion models arXiv:2508.10944v1 Announce Type: new Abstract: Learning and generating various types of data based on conditional diffusion models has been a research hotspot in recent years. Although conditional diffusion models have made considerable progress in improving acceleration algorithms and enhancing generation quality, the lack of non-asymptotic properties has hindered…

  • In-Context Learning as Nonparametric Conditional Probability Estimation: Risk Bounds and Optimality

    In-Context Learning as Nonparametric Conditional Probability Estimation: Risk Bounds and Optimality arXiv:2508.08673v1 Announce Type: new Abstract: This paper investigates the expected excess risk of In-Context Learning (ICL) for multiclass classification. We model each task as a sequence of labeled prompt samples and a query input, where a pre-trained model estimates the conditional class probabilities of…

  • Self-Consistent Equation-guided Neural Networks for Censored Time-to-Event Data

    Self-Consistent Equation-guided Neural Networks for Censored Time-to-Event Data arXiv:2503.09097v1 Announce Type: new Abstract: In survival analysis, estimating the conditional survival function given predictors is often of interest. There is a growing trend in the development of deep learning methods for analyzing censored time-to-event data, especially when dealing with high-dimensional predictors that are complexly interrelated. Many…

  • Amortized Conditional Independence Testing

    Amortized Conditional Independence Testing arXiv:2502.20925v1 Announce Type: new Abstract: Testing for the conditional independence structure in data is a fundamental and critical task in statistics and machine learning, which finds natural applications in causal discovery – a highly relevant problem to many scientific disciplines. Existing methods seek to design explicit test statistics that quantify the…

  • Rectifying Conformity Scores for Better Conditional Coverage

    Rectifying Conformity Scores for Better Conditional Coverage arXiv:2502.16336v1 Announce Type: new Abstract: We present a new method for generating confidence sets within the split conformal prediction framework. Our method performs a trainable transformation of any given conformity score to improve conditional coverage while ensuring exact marginal coverage. The transformation is based on an estimate of…

  • Testing Conditional Mean Independence Using Generative Neural Networks

    Testing Conditional Mean Independence Using Generative Neural Networks arXiv:2501.17345v1 Announce Type: new Abstract: Conditional mean independence (CMI) testing is crucial for statistical tasks including model determination and variable importance evaluation. In this work, we introduce a novel population CMI measure and a bootstrap-based testing procedure that utilizes deep generative neural networks to estimate the conditional…

  • Marginal and Conditional Importance Measures from Machine Learning Models and Their Relationship with Conditional Average Treatment Effect

    Marginal and Conditional Importance Measures from Machine Learning Models and Their Relationship with Conditional Average Treatment Effect arXiv:2501.16988v1 Announce Type: new Abstract: Interpreting black-box machine learning models is challenging due to their strong dependence on data and inherently non-parametric nature. This paper reintroduces the concept of importance through “Marginal Variable Importance Metric” (MVIM), a model-agnostic…

  • Conformal Inference of Individual Treatment Effects Using Conditional Density Estimates

    Conformal Inference of Individual Treatment Effects Using Conditional Density Estimates arXiv:2501.14933v1 Announce Type: new Abstract: In an era where diverse and complex data are increasingly accessible, the precise prediction of individual treatment effects (ITE) becomes crucial across fields such as healthcare, economics, and public policy. Current state-of-the-art approaches, while providing valid prediction intervals through Conformal…

  • Conditional Variational Autoencoders for Text to Image Generation

    Conditional Variational Autoencoders for Text to Image Generation Investigating an early generative architecture and applying it to image generation from text input Recently I was tasked with text-to-image synthesis using a conditional variational autoencoder (CVAE). Being one of the earlier generative structures, it has its limitations but is easily implementable. This article will cover CVAEs at…