regression

This paper identifies blind spots in evaluating deep imbalanced regression, proposing balanced metrics and showing high tail-region instability across random seeds.

The article discusses a paper titled 'Reasoning-Intensive Regression' that proposes MENTAT, a lightweight method combining batch-reflective prompt optimization with neural ensemble learning to improve numerical score prediction from text in AI tasks, showing up to 65% improvement over baselines.

The author discusses the use of classical ML versus LLMs for business classification and regression problems, evaluates the Jev model as a potential tool, and compares it with existing techniques like Structured Outputs and DSPy, highlighting accessibility and effectiveness concerns.

The paper introduces PersonaLink, a training-free method that distills user history into a bounded persona, matching retrieval on classification tasks but not on regression, highlighting a task-type asymmetry.

This paper introduces a graph neural regression framework for non-invasive estimation of body composition metrics such as body fat percentage and bone mineral density, demonstrating improved accuracy over previous methods using clinical data.

Peter Szilagyi reports that Fable introduced a regression into a cryptographic library, which his fuzzer detected, and Fable refused to patch the security issue.

This paper proposes LitEm, a neural regression model that enables transductive knowledge graph embedding models to predict numerical attributes, achieving strong benchmark results and introducing a co-training framework for improved performance.

The paper introduces D^CF5, a diagnostic to predict regionwise gains in dynamic ensembling for regression tasks under distribution shift, validated with high correlation across datasets.

The article reports on performance regression in Grok 4.6 compared to 4.5, with lower pass rate and higher latency, affecting practical business tasks.

This paper tests whether perceived attributes of consumer technologies, rated by humans and frontier language models, predict ownership prevalence better than years-since-launch, finding modest improvements but limitations for short-term forecasts.

This paper proposes a new asymmetric robust bounded sparse smooth (aR) loss function for l1-norm penalized geometric twin support vector machine (aRSGTSVM) to handle classification and regression tasks with label and feature noise, achieving feature selection and robustness. Experiments on synthetic and UCI datasets plus China stock market index tracking demonstrate superiority.

This paper introduces TS2TabPFN, a framework that combines explicit feature extraction with the TabPFN 2.5 tabular foundation model for time series classification and extrinsic regression. Experiments show it outperforms state-of-the-art models in TSER and achieves competitive results in TSC.

This paper systematically compares tabular foundation models (TabPFN) with classical regression approaches across 85 soil spectroscopy tasks, finding that TabPFN combined with PLS-derived features achieves the best predictive performance from field-scale to global spectral libraries.

This paper studies the efficacy of various Graph Neural Network message-passing layers in regression contexts, finding that deep convolutional GNNs, particularly GEN, outperform attention-based GNNs.

The article evaluates the upgrade from Gemini 3.5 Flash to 3.6 Flash, noting aggregate benchmark gains but potential regressions in certain tasks, and recommends rigorous evaluation with predeclared failure gates before upgrading.

This paper proposes a two-stage online learning framework for detecting service-affecting failures in mobile core networks by modeling normal traffic dynamics and analyzing residuals, achieving improved precision-recall trade-off over static thresholds.

This paper presents a systematic comparison of two geospatial foundation models, TerraMind and THOR, developed under ESA's φ-lab, analyzing how architectural choices like patch size and decoder type affect performance across ten use cases in Earth observation tasks.

The paper proposes a lightweight method that reformulates regression-based INR training as a classification task by discretizing continuous targets into bins, enabling flexible distribution modeling for error-aware uncertainty estimation in scientific data compression.

Zer0Fit provides an MCP server that wraps Google's TabFM and TimesFM foundation models for zero-shot forecasting, classification, and regression tasks, running entirely locally.

OpenAI's model experiences a significant regression on the SimpleBench benchmark, indicating a drop in performance.