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Tag: inference

  • Scaling ML Inference on Databricks: Liquid or Partitioned? Salted or Not?

    Scaling ML Inference on Databricks: Liquid or Partitioned? Salted or Not? A case study on techniques to maximize your clusters The post Scaling ML Inference on Databricks: Liquid or Partitioned? Salted or Not? appeared first on Towards Data Science. Hector Mejia Go to original source

  • Amortised and provably-robust simulation-based inference

    Amortised and provably-robust simulation-based inference arXiv:2602.11325v1 Announce Type: new Abstract: Complex simulator-based models are now routinely used to perform inference across the sciences and engineering, but existing inference methods are often unable to account for outliers and other extreme values in data which occur due to faulty measurement instruments or human error. In this paper,…

  • Simulation-based Bayesian inference with ameliorative learned summary statistics — Part I

    Simulation-based Bayesian inference with ameliorative learned summary statistics — Part I arXiv:2601.22441v1 Announce Type: new Abstract: This paper, which is Part 1 of a two-part paper series, considers a simulation-based inference with learned summary statistics, in which such a learned summary statistic serves as an empirical-likelihood with ameliorative effects in the Bayesian setting, when the…

  • Efficient Evaluation of LLM Performance with Statistical Guarantees

    Efficient Evaluation of LLM Performance with Statistical Guarantees arXiv:2601.20251v1 Announce Type: new Abstract: Exhaustively evaluating many large language models (LLMs) on a large suite of benchmarks is expensive. We cast benchmarking as finite-population inference and, under a fixed query budget, seek tight confidence intervals (CIs) for model accuracy with valid frequentist coverage. We propose Factorized…

  • Statistical Inference for Explainable Boosting Machines

    Statistical Inference for Explainable Boosting Machines arXiv:2601.18857v1 Announce Type: new Abstract: Explainable boosting machines (EBMs) are popular “glass-box” models that learn a set of univariate functions using boosting trees. These achieve explainability through visualizations of each feature’s effect. However, unlike linear model coefficients, uncertainty quantification for the learned univariate functions requires computationally intensive bootstrapping, making…

  • A Statistical Assessment of Amortized Inference Under Signal-to-Noise Variation and Distribution Shift

    A Statistical Assessment of Amortized Inference Under Signal-to-Noise Variation and Distribution Shift arXiv:2601.07944v1 Announce Type: new Abstract: Since the turn of the century, approximate Bayesian inference has steadily evolved as new computational techniques have been incorporated to handle increasingly complex and large-scale predictive problems. The recent success of deep neural networks and foundation models has…

  • 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…

  • Likelihood-Preserving Embeddings for Statistical Inference

    Likelihood-Preserving Embeddings for Statistical Inference arXiv:2512.22638v1 Announce Type: new Abstract: Modern machine learning embeddings provide powerful compression of high-dimensional data, yet they typically destroy the geometric structure required for classical likelihood-based statistical inference. This paper develops a rigorous theory of likelihood-preserving embeddings: learned representations that can replace raw data in likelihood-based workflows — hypothesis testing,…

  • Robust Causal Directionality Inference in Quantum Inference under MNAR Observation and High-Dimensional Noise

    Robust Causal Directionality Inference in Quantum Inference under MNAR Observation and High-Dimensional Noise arXiv:2512.19746v1 Announce Type: new Abstract: In quantum mechanics, observation actively shapes the system, paralleling the statistical notion of Missing Not At Random (MNAR). This study introduces a unified framework for textbf{robust causal directionality inference} in quantum engineering, determining whether relations are system$to$observation,…

  • 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…

  • Maxitive Donsker-Varadhan Formulation for Possibilistic Variational Inference

    Maxitive Donsker-Varadhan Formulation for Possibilistic Variational Inference arXiv:2511.21223v1 Announce Type: new Abstract: Variational inference (VI) is a cornerstone of modern Bayesian learning, enabling approximate inference in complex models that would otherwise be intractable. However, its formulation depends on expectations and divergences defined through high-dimensional integrals, often rendering analytical treatment impossible and necessitating heavy reliance on…

  • Theory and computation for structured variational inference

    Theory and computation for structured variational inference arXiv:2511.09897v1 Announce Type: new Abstract: Structured variational inference constitutes a core methodology in modern statistical applications. Unlike mean-field variational inference, the approximate posterior is assumed to have interdependent structure. We consider the natural setting of star-structured variational inference, where a root variable impacts all the other ones. We…

  • Robust Sampling for Active Statistical Inference

    Robust Sampling for Active Statistical Inference arXiv:2511.08991v1 Announce Type: new Abstract: Active statistical inference is a new method for inference with AI-assisted data collection. Given a budget on the number of labeled data points that can be collected and assuming access to an AI predictive model, the basic idea is to improve estimation accuracy by…

  • Robust Experimental Design via Generalised Bayesian Inference

    Robust Experimental Design via Generalised Bayesian Inference arXiv:2511.07671v1 Announce Type: new Abstract: Bayesian optimal experimental design is a principled framework for conducting experiments that leverages Bayesian inference to quantify how much information one can expect to gain from selecting a certain design. However, accurate Bayesian inference relies on the assumption that one’s statistical model of…

  • VIKING: Deep variational inference with stochastic projections

    VIKING: Deep variational inference with stochastic projections arXiv:2510.23684v1 Announce Type: new Abstract: Variational mean field approximations tend to struggle with contemporary overparametrized deep neural networks. Where a Bayesian treatment is usually associated with high-quality predictions and uncertainties, the practical reality has been the opposite, with unstable training, poor predictive power, and subpar calibration. Building upon…

  • Efficient Inference for Coupled Hidden Markov Models in Continuous Time and Discrete Space

    Efficient Inference for Coupled Hidden Markov Models in Continuous Time and Discrete Space arXiv:2510.12916v1 Announce Type: new Abstract: Systems of interacting continuous-time Markov chains are a powerful model class, but inference is typically intractable in high dimensional settings. Auxiliary information, such as noisy observations, is typically only available at discrete times, and incorporating it via…

  • SADA: Safe and Adaptive Inference with Multiple Black-Box Predictions

    SADA: Safe and Adaptive Inference with Multiple Black-Box Predictions arXiv:2509.21707v1 Announce Type: new Abstract: Real-world applications often face scarce labeled data due to the high cost and time requirements of gold-standard experiments, whereas unlabeled data are typically abundant. With the growing adoption of machine learning techniques, it has become increasingly feasible to generate multiple predicted…

  • Imputation-Powered Inference

    Imputation-Powered Inference arXiv:2509.13778v1 Announce Type: cross Abstract: Modern multi-modal and multi-site data frequently suffer from blockwise missingness, where subsets of features are missing for groups of individuals, creating complex patterns that challenge standard inference methods. Existing approaches have critical limitations: complete-case analysis discards informative data and is potentially biased; doubly robust estimators for non-monotone missingness-where…

  • Robust variational neural posterior estimation for simulation-based inference

    Robust variational neural posterior estimation for simulation-based inference arXiv:2509.05724v1 Announce Type: new Abstract: Recent advances in neural density estimation have enabled powerful simulation-based inference (SBI) methods that can flexibly approximate Bayesian inference for intractable stochastic models. Although these methods have demonstrated reliable posterior estimation when the simulator accurately represents the underlying data generative process (GDP),…

  • Fisher Random Walk: Automatic Debiasing Contextual Preference Inference for Large Language Model Evaluation

    Fisher Random Walk: Automatic Debiasing Contextual Preference Inference for Large Language Model Evaluation arXiv:2509.05852v1 Announce Type: new Abstract: Motivated by the need for rigorous and scalable evaluation of large language models, we study contextual preference inference for pairwise comparison functionals of context-dependent preference score functions across domains. Focusing on the contextual Bradley-Terry-Luce model, we develop…

  • Prediction-Powered Inference with Inverse Probability Weighting

    Prediction-Powered Inference with Inverse Probability Weighting arXiv:2508.10149v1 Announce Type: new Abstract: Prediction-powered inference (PPI) is a recent framework for valid statistical inference with partially labeled data, combining model-based predictions on a large unlabeled set with bias correction from a smaller labeled subset. We show that PPI can be extended to handle informative labeling by replacing…

  • Debiasing Machine Learning Predictions for Causal Inference Without Additional Ground Truth Data: “One Map, Many Trials” in Satellite-Driven Poverty Analysis

    Debiasing Machine Learning Predictions for Causal Inference Without Additional Ground Truth Data: “One Map, Many Trials” in Satellite-Driven Poverty Analysis arXiv:2508.01341v1 Announce Type: new Abstract: Machine learning models trained on Earth observation data, such as satellite imagery, have demonstrated significant promise in predicting household-level wealth indices, enabling the creation of high-resolution wealth maps that can…

  • Accelerating Hamiltonian Monte Carlo for Bayesian Inference in Neural Networks and Neural Operators

    Accelerating Hamiltonian Monte Carlo for Bayesian Inference in Neural Networks and Neural Operators arXiv:2507.14652v1 Announce Type: new Abstract: Hamiltonian Monte Carlo (HMC) is a powerful and accurate method to sample from the posterior distribution in Bayesian inference. However, HMC techniques are computationally demanding for Bayesian neural networks due to the high dimensionality of the network’s…

  • 10,000x Faster Bayesian Inference: Multi-GPU SVI vs. Traditional MCMC

    10,000x Faster Bayesian Inference: Multi-GPU SVI vs. Traditional MCMC Using GPU acceleration to speed up Bayesian Inference from months to minutes… The post 10,000x Faster Bayesian Inference: Multi-GPU SVI vs. Traditional MCMC appeared first on Towards Data Science. Derek Tran Go to original source

  • Multilevel neural simulation-based inference

    Multilevel neural simulation-based inference arXiv:2506.06087v1 Announce Type: new Abstract: Neural simulation-based inference (SBI) is a popular set of methods for Bayesian inference when models are only available in the form of a simulator. These methods are widely used in the sciences and engineering, where writing down a likelihood can be significantly more challenging than constructing…

  • Evaluating LLMs for Inference, or Lessons from Teaching for Machine Learning

    Evaluating LLMs for Inference, or Lessons from Teaching for Machine Learning It’s like grading papers, but your student is an LLM The post Evaluating LLMs for Inference, or Lessons from Teaching for Machine Learning appeared first on Towards Data Science. Stephanie Kirmer Go to original source

  • Stable Thompson Sampling: Valid Inference via Variance Inflation

    Stable Thompson Sampling: Valid Inference via Variance Inflation arXiv:2505.23260v1 Announce Type: new Abstract: We consider the problem of statistical inference when the data is collected via a Thompson Sampling-type algorithm. While Thompson Sampling (TS) is known to be both asymptotically optimal and empirically effective, its adaptive sampling scheme poses challenges for constructing confidence intervals for…

  • Online Statistical Inference of Constrained Stochastic Optimization via Random Scaling

    Online Statistical Inference of Constrained Stochastic Optimization via Random Scaling arXiv:2505.18327v1 Announce Type: new Abstract: Constrained stochastic nonlinear optimization problems have attracted significant attention for their ability to model complex real-world scenarios in physics, economics, and biology. As datasets continue to grow, online inference methods have become crucial for enabling real-time decision-making without the need…

  • Regression Discontinuity Design: How It Works and When to Use It

    Regression Discontinuity Design: How It Works and When to Use It Regression Discontinuity Design: How It Works and When to Use It You’re an avid data scientist and experimenter. You know that randomisation is the summit of Mount Evidence Credibility, and you also know that when you can’t randomise, you resort to observational data and…

  • Extended Fiducial Inference for Individual Treatment Effects via Deep Neural Networks

    Extended Fiducial Inference for Individual Treatment Effects via Deep Neural Networks arXiv:2505.01995v1 Announce Type: new Abstract: Individual treatment effect estimation has gained significant attention in recent data science literature. This work introduces the Double Neural Network (Double-NN) method to address this problem within the framework of extended fiducial inference (EFI). In the proposed method, deep…

  • Inference for max-linear Bayesian networks with noise

    Inference for max-linear Bayesian networks with noise arXiv:2505.00229v1 Announce Type: new Abstract: Max-Linear Bayesian Networks (MLBNs) provide a powerful framework for causal inference in extreme-value settings; we consider MLBNs with noise parameters with a given topology in terms of the max-plus algebra by taking its logarithm. Then, we show that an estimator of a parameter…

  • Physics-Informed Inference Time Scaling via Simulation-Calibrated Scientific Machine Learning

    Physics-Informed Inference Time Scaling via Simulation-Calibrated Scientific Machine Learning arXiv:2504.16172v1 Announce Type: cross Abstract: High-dimensional partial differential equations (PDEs) pose significant computational challenges across fields ranging from quantum chemistry to economics and finance. Although scientific machine learning (SciML) techniques offer approximate solutions, they often suffer from bias and neglect crucial physical insights. Inspired by inference-time…

  • Normalizing Flow Regression for Bayesian Inference with Offline Likelihood Evaluations

    Normalizing Flow Regression for Bayesian Inference with Offline Likelihood Evaluations arXiv:2504.11554v1 Announce Type: new Abstract: Bayesian inference with computationally expensive likelihood evaluations remains a significant challenge in many scientific domains. We propose normalizing flow regression (NFR), a novel offline inference method for approximating posterior distributions. Unlike traditional surrogate approaches that require additional sampling or inference…

  • Throughput-Optimal Scheduling Algorithms for LLM Inference and AI Agents

    Throughput-Optimal Scheduling Algorithms for LLM Inference and AI Agents arXiv:2504.07347v1 Announce Type: new Abstract: As demand for Large Language Models (LLMs) and AI agents rapidly grows, optimizing systems for efficient LLM inference becomes critical. While significant efforts have targeted system-level engineering, little is explored through a mathematical modeling and queuing perspective. In this paper, we…

  • The Case for Centralized AI Model Inference Serving

    The Case for Centralized AI Model Inference Serving As AI models continue to increase in scope and accuracy, even tasks once dominated by traditional algorithms are gradually being replaced by Deep Learning models. Algorithmic pipelines — workflows that take an input, process it through a series of algorithms, and produce an output — increasingly rely…

  • Is Best-of-N the Best of Them? Coverage, Scaling, and Optimality in Inference-Time Alignment

    Is Best-of-N the Best of Them? Coverage, Scaling, and Optimality in Inference-Time Alignment arXiv:2503.21878v1 Announce Type: cross Abstract: Inference-time computation provides an important axis for scaling language model performance, but naively scaling compute through techniques like Best-of-$N$ sampling can cause performance to degrade due to reward hacking. Toward a theoretical understanding of how to best…

  • DeepRV: pre-trained spatial priors for accelerated disease mapping

    DeepRV: pre-trained spatial priors for accelerated disease mapping arXiv:2503.21473v1 Announce Type: new Abstract: Recently introduced prior-encoding deep generative models (e.g., PriorVAE, $pi$VAE, and PriorCVAE) have emerged as powerful tools for scalable Bayesian inference by emulating complex stochastic processes like Gaussian processes (GPs). However, these methods remain largely a proof-of-concept and inaccessible to practitioners. We propose…

  • Near-Optimal Approximations for Bayesian Inference in Function Space

    Near-Optimal Approximations for Bayesian Inference in Function Space arXiv:2502.18279v1 Announce Type: new Abstract: We propose a scalable inference algorithm for Bayes posteriors defined on a reproducing kernel Hilbert space (RKHS). Given a likelihood function and a Gaussian random element representing the prior, the corresponding Bayes posterior measure $Pi_{text{B}}$ can be obtained as the stationary distribution…

  • Federated Variational Inference for Bayesian Mixture Models

    Federated Variational Inference for Bayesian Mixture Models arXiv:2502.12684v1 Announce Type: new Abstract: We present a federated learning approach for Bayesian model-based clustering of large-scale binary and categorical datasets. We introduce a principled ‘divide and conquer’ inference procedure using variational inference with local merge and delete moves within batches of the data in parallel, followed by…

  • Optimizing Likelihoods via Mutual Information: Bridging Simulation-Based Inference and Bayesian Optimal Experimental Design

    Optimizing Likelihoods via Mutual Information: Bridging Simulation-Based Inference and Bayesian Optimal Experimental Design arXiv:2502.08004v1 Announce Type: new Abstract: Simulation-based inference (SBI) is a method to perform inference on a variety of complex scientific models with challenging inference (inverse) problems. Bayesian Optimal Experimental Design (BOED) aims to efficiently use experimental resources to make better inferences. Various…

  • Optimistic Algorithms for Adaptive Estimation of the Average Treatment Effect

    Optimistic Algorithms for Adaptive Estimation of the Average Treatment Effect arXiv:2502.04673v1 Announce Type: new Abstract: Estimation and inference for the Average Treatment Effect (ATE) is a cornerstone of causal inference and often serves as the foundation for developing procedures for more complicated settings. Although traditionally analyzed in a batch setting, recent advances in martingale theory…

  • Robust Amortized Bayesian Inference with Self-Consistency Losses on Unlabeled Data

    Robust Amortized Bayesian Inference with Self-Consistency Losses on Unlabeled Data arXiv:2501.13483v1 Announce Type: new Abstract: Neural amortized Bayesian inference (ABI) can solve probabilistic inverse problems orders of magnitude faster than classical methods. However, neural ABI is not yet sufficiently robust for widespread and safe applicability. In particular, when performing inference on observations outside of the…

  • 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…

  • Adaptive Conformal Inference by Betting

    Adaptive Conformal Inference by Betting arXiv:2412.19318v1 Announce Type: new Abstract: Conformal prediction is a valuable tool for quantifying predictive uncertainty of machine learning models. However, its applicability relies on the assumption of data exchangeability, a condition which is often not met in real-world scenarios. In this paper, we consider the problem of adaptive conformal inference…

  • A Flexible Defense Against the Winner’s Curse

    A Flexible Defense Against the Winner’s Curse arXiv:2411.18569v1 Announce Type: new Abstract: Across science and policy, decision-makers often need to draw conclusions about the best candidate among competing alternatives. For instance, researchers may seek to infer the effectiveness of the most successful treatment or determine which demographic group benefits most from a specific treatment. Similarly,…