Method quantifies sentiment across languages without labeled data.
problem Performing sentiment analysis across multiple languages without labeled data.
method Combining quantification methods with cross-lingual vectorial representations.
result Cross-lingual sentiment quantification achieved surprising accuracy.
Quantification is a supervised learning task that consists in predicting, given a set of classes C and a set D of unlabelled items, the prevalence (or relative frequency) p(c|D) of each class c in C. Quantification can in principle be solved by classifying all the unlabelled items and counting how many of them have bee…
The paper addresses causal estimation for text data with apparent overlap violations.
problem Estimating causal effects from text data with unknown confounders and apparent overlap.
method Uses supervised representation learning to create a representation that preserves confounding information while eliminating predictive information, satisfying overlap assumptions.
result Shows how to obtain robust causal estimation in the presence of apparent overlap violations.
Geometry-aware KDE model improves multiclass quantification.
problem Accurately estimating class prevalence for label shift adaptation.
method Log-ratio representations and Aitchison geometry for compositional data, shrinkage regularization.
result Competitive with state-of-the-art quantifiers, often improving over standard KDE-based baselines.
Bayesian approach quantifies uncertainty in LLM evaluations.
problem Statistical uncertainty in evaluating LLM behavior.
method Bayesian evaluation of LLM behavior using probabilistic text generation strategies.
result Bayesian approach provides useful uncertainty quantification about LLM behavior.
Generative Score Inference improves uncertainty quantification for multimodal data.
problem Accurate uncertainty quantification in multimodal learning tasks.
method Generative Score Inference (GSI) uses synthetic samples to approximate conditional score distributions.
result GSI achieves state-of-the-art performance in hallucination detection and image captioning uncertainty estimation.
The paper introduces a method to quantify uncertainty in neural networks without parametric assumptions.
problem Uncertainty quantification for neural network predictions.
method Nonparametric estimation of conditional label distribution using Nadaraya-Watson kernel.
result The method effectively disentangles aleatoric and epistemic uncertainties.
Hybrid AI and rule-based framework de-identifies medical imaging data.
problem De-identifying medical imaging data to protect PHI and PII.
method Combines rule-based and AI techniques with uncertainty quantification.
result Robust performance across benchmark datasets and regulatory standards.
CAOS aggregates multiple one-shot predictors for efficient uncertainty quantification.
problem Lack of principled uncertainty quantification in one-shot prediction.
method CAOS, a conformal framework that aggregates multiple one-shot predictors and uses a leave-one-out calibration scheme.
result CAOS produces smaller prediction sets with reliable coverage compared to split conformal baselines.
Paper develops a method to compare generative models using KL divergence.
problem Lack of principled uncertainty quantification for generative models.
method Employ Kullback-Leibler divergence to measure generative model distance.
result Effective coverage rates and higher power compared to kernel-based methods.
The estimation of class prevalence, i.e., the fraction of a population that belongs to a certain class, is a very useful tool in data analytics and learning, and finds applications in many domains such as sentiment analysis, epidemiology, etc. For example, in sentiment analysis, the objective is often not to estimate w…
Statisticians contribute to LLMs for better trust and transparency.
problem Emerging statistical challenges in LLMs.
method Exploring statistical contributions to LLMs.
result Statisticians can enhance LLMs' trustworthiness and transparency.
Model clusters authors and topics in short texts like social media posts.
problem Analysis of short texts is difficult due to brevity and lack of context.
method Expands Latent Dirichlet Allocation to model word dependencies and cluster users.
result Improves topic and user clustering, outperforming traditional methods.
Paper detects adversarial speech inputs with high accuracy.
problem Adversarial attacks on ASR systems.
method Uncertainty quantification using neural networks.
result Detection accuracy of adversarial inputs over 0.99.
This work advances collaborative decision making by combining human and AI strengths in uncertainty quantification.
problem Current AI lacks robust decision-making capabilities under uncertainty, especially in high-stakes contexts.
method Introduces Human AI Collaborative Uncertainty Quantification (HACUQ) framework, formalizing AI-human collaboration and developing calibration algorithms.
result Optimal collaborative prediction sets follow a two-threshold structure, and online adaptation algorithms can adapt to evolving human behavior.
Study evaluates ensemble methods for zero-shot uncertainty quantification with diffusion models.
problem Quantifying uncertainty in zero-shot regression problems using diffusion models.
method Used diffusion probabilistic models for ensemble prediction and evaluated their effectiveness on various regression tasks.
result Ensemble methods consistently improve model prediction accuracy across different regression tasks.
New estimators improve Rasch model item parameter estimation for sparse data.
problem Estimating item parameters in sparse Rasch model data.
method Random pairing maximum likelihood estimator (RP-MLE) and its bootstrapped variant (MRP-MLE).
result RP-MLE and MRP-MLE are minimax optimal and provide precise item parameter estimates.
A new method ranks uncertainty vectors from multiple measures for robust prediction.
problem Single scalar measures of model reliability are insufficient for comprehensive uncertainty quantification.
method Optimal transport ranks vectors of uncertainty measures, supporting flexible fusion of aleatoric and epistemic uncertainties.
result The method provides a robust ranking of uncertainty that supports various downstream tasks.
New method predicts quasar continuum near Lyman-α with high precision and accuracy.
problem Precise measurement of quasar red damping wing for epoch of reionization.
method Fully probabilistic approach using conditional neural spline flows.
result Achieved state-of-the-art precision and accuracy in predicting quasar continua.
Algorithm constructs prediction sets with PAC guarantees in label shift settings.
problem Reliable uncertainty quantification in the face of distribution shift.
method Estimates predicted probabilities and confusion matrix, then propagates uncertainty through Gaussian elimination to compute confidence intervals and construct prediction sets.
result Satisfies PAC guarantees and produces smaller, more informative prediction sets.
Study examines biases in clinical word embeddings, revealing performance gaps across groups.
problem Biases in clinical word embeddings leading to performance differences across groups.
method Pretrained BERT models on MIMIC-III, fill-in-the-blank method, fairness evaluation on clinical tasks.
result Classifiers trained from BERT representations exhibit statistically significant differences in performance across groups.
New approach uses autoregressive models to explore and quantify uncertainty in decision-making.
problem Quantifying and exploring uncertainty in online decision-making.
method Reformulates uncertainty as missing future outcomes, training autoregressive models for next-outcome prediction.
result Establishes a reduction from online learning to offline next-outcome prediction, controlling Bayesian regret by sequence prediction loss.
New approach to topic modelling with covariates for large text corpora.
problem Complex topic modelling in large text corpora.
method Combining convex NMF with regression for tractable estimation.
result Faster, interpretable, and better inferential justification than generative models.
Integrates neural encoders into GLMMs for multimodal data analysis.
problem Scalable Bayesian inference for GLMMs assumes low-dimensional tabular predictors and does not handle high-dimensional modalities.
method Jointly learns modality-specific neural encoders with GLMM objective, performs variance-corrected stochastic-gradient MCMC.
result Preserves interpretable fixed and random effects while scaling to large longitudinal datasets.
Framework for ensuring fairness in machine learning models across multiple groups.
problem Ensuring fairness in machine learning models across multiple groups.
method Introduces (s,G,α)−GMC for multi-dimensional mappings and constraint sets, proposing algorithms to achieve multicalibration. result Demonstrates the effectiveness of the framework on various scenarios, including image segmentation, hierarchical classification, and text generation.
HistNetQ improves quantification tasks by optimizing loss functions and eliminating label requirements.
problem Quantification of class prevalence in bags of examples.
method Permutation-invariant Histograms and deep neural networks.
result HistNetQ outperforms other quantification methods and optimizes custom loss functions.
Fortuna simplifies uncertainty quantification in deep learning.
problem Improving uncertainty estimates in deep learning models.
method Supports various calibration techniques including conformal prediction and scalable Bayesian inference.
result Simplifies benchmarking and builds robust AI systems.
Bayesian meta learning improves uncertainty quantification in regression.
problem Trusting uncertainty quantification in Bayesian regression.
method Trust-Bayes framework for Bayesian meta learning, optimizing for trustworthy uncertainty quantification.
result Lower bounds and sample complexity for trustworthy uncertainty quantification are characterized.
Bayesian uncertainty quantification is flawed, according to new research.
problem Flawed interpretation of Bayesian uncertainty quantification.
method Discussion of Bayesian updating and optimization-based perspective, proposing measures of quality.
result Bayesian uncertainty quantification is not coherent with optimization-based perspective.
A new KDE-based method improves multiclass quantification.
problem Quantifying class prevalence in multiclass settings.
method Kernel Density Estimation (KDE) for multivariate densities.
result KDEy method yields superior quantification performance.
New GP-based method improves uncertainty quantification for causal functions.
problem Challenges in quantifying uncertainty for causal effects, especially for entire functions.
method GP-based approach using inner-product of observational functions in RKHS, with tractable posterior moments and calibration.
result Improves uncertainty quantification while maintaining causal effect estimation performance.
A new framework for scalable uncertainty quantification in statistical models.
problem Computational bottleneck in uncertainty quantification for statistical machine learning.
method Predictive-matching Generative Parameter Sampler (GPS) framework.
result The GPS framework provides successful uncertainty quantification and additional flexibility.
Unified taxonomy for ML uncertainty in physics, validated.
problem Uncertainty quantification in machine learning for physics.
method Unified taxonomy, principled validation tools.
result Illustrated validation tools with examples.
Proposes PQ, a more precise Bayesian quantifier for prevalence estimation.
problem Uncertainty quantification in prevalence estimation.
method Bayesian quantification methods, focusing on precision and coverage.
result PQ provides more precise and well-calibrated uncertainty quantification.
GPI uses GenAI models to infer causal and predictive effects from unstructured data.
problem Estimating causal and predictive effects from unstructured data like text and images.
method Leverages open-source GenAI models to generate and represent unstructured data, applying machine learning to these representations.
result GPI efficiently estimates causal and predictive effects with quantified uncertainty, without fine-tuning.
The paper discusses quantifying realism in generated images.
problem Designing functions to reliably distinguish realistic data from unrealistic data.
method Drawing on insights from algorithmic information theory, the paper introduces the notion of a universal critic.
result A good generative model alone is insufficient to solve the problem of realism quantification.
Bayesian neural network models improve uncertainty quantification in multivariate regression.
problem Uncertainty quantification in multivariate regression models with heteroscedastic noise.
method Proposes Bayesian Last Layer neural network models and EM algorithms for parameter learning.
result Capable of disentangling aleatoric and epistemic uncertainty.
Novel framework for uncertainty quantification in metric spaces.
problem Uncertainty quantification in regression models with metric responses.
method Developed algorithms for large datasets, agnostic to predictive models, with asymptotic and non-asymptotic guarantees.
result Asymptotic and non-asymptotic guarantees for special cases, demonstrated in clinical applications.
Quantification is the task of estimating, given a set σ of unlabelled items and a set of classes C={c1,…,c∣C∣}, the prevalence (or `relative frequency') in σ of each class ci∈C. While quantification may in principle be solved by classifying each item in σ and…
Bayesian neural networks outperform calibrated neural networks for tabular data.
problem Uncertainty in neural network predictions for tabular data.
method Bayesian neural networks vs. post-hoc calibration methods.
result Bayesian neural networks yield competitive performance compared to calibrated neural networks.
Proposes measures for uncertainty quantification using proper scoring rules.
problem Uncertainty quantification for prediction tasks.
method Decomposes proper scoring rules into divergence and entropy components, tailoring uncertainty quantification to specific tasks.
result Flexibility in uncertainty quantification improves performance in selective prediction and active learning.
Develops a framework for inferring causal relationships in networked data with uncertainty quantification.
problem Extracting reliable inference from complex Hawkes network data with uncertainty.
method Statistical inference framework based on maximum likelihood estimation and concentration inequalities of continuous-time martingales.
result Provides a non-asymptotic confidence set for uncertainty quantification.
Optimizes sampling for faster convergence in Bayesian experimental design and uncertainty quantification.
problem Efficiently selecting samples for faster convergence in Bayesian experimental design and uncertainty quantification.
method Output-weighted acquisition functions leveraging likelihood ratio to guide sampling towards relevant regions.
result Superiority of the proposed method in uncertainty quantification and rare event identification.
New BNN architectures reduce computational cost for uncertainty quantification.
problem High computational cost in Bayesian neural networks.
method Partial trace-class Bayesian neural networks (PaTraC BNNs).
result Comparable uncertainty quantification with fewer parameters.
SMURF-THP improves Transformer Hawkes process models by providing uncertainty quantification.
problem Uncertainty quantification for Transformer Hawkes process predictions.
method Score matching for learning the score function of event arrival times.
result SMURF-THP outperforms likelihood-based methods in confidence calibration.
Generative models improve image reconstruction and uncertainty quantification.
problem Bayesian inverse problems, especially image reconstruction from noisy and incomplete data.
method Data-driven priors and computationally tractable posterior analysis.
result Efficient uncertainty quantification without retraining for different corruption types.
Uncertainty Toolbox aids in assessing and improving uncertainty quantification in machine learning.
problem Disparate evaluation metrics and implementations hinder direct comparison of uncertainty quantification results.
method Provides an open-source Python library for assessing, visualizing, and improving uncertainty quantification.
result Facilitates more accurate and comparable uncertainty quantification across different works.
The paper analyzes uncertainty quantification in sparse Gaussian process regression with a Brownian motion prior.
problem Analyzing uncertainty in sparse Gaussian process regression with a Brownian motion prior.
method Theoretical guarantees and limitations for pointwise credible sets are derived for a rescaled Brownian motion prior with a sparse variational Gaussian process method.
result Theoretical characterization of asymptotic frequentist coverage for credible sets, distinguishing conservative and overconfident cases.