regression

This paper consolidates the state of the art in supervised political scaling, investigating whether joint prediction of ideological scales and a middle ground between classification and regression can improve performance.

Google Research introduces TabFM, a zero-shot foundation model for tabular data that uses in-context learning to perform classification and regression without requiring manual model training or hyperparameter tuning.

Google Research introduces TabFM, a foundation model for zero-shot tabular data classification and regression, integrated into BigQuery ML to simplify workflows by eliminating manual training and feature engineering.

This paper proposes active shot allocation strategies for quantum kernel estimation in Gaussian process regression, deriving pair-level sensitivities to guide non-uniform shot budgets and demonstrating significant improvements in test RMSE over uniform allocation on benchmarks.

Proposes Counterfactual Residual Data Augmentation (CRDA) for tabular regression, leveraging residual invariance under feature perturbations to generate realistic training samples, achieving significant MSE reduction on benchmarks.

Google Research releases TabFM, a zero-shot tabular foundation model for classification and regression using PyTorch, requiring no fine-tuning.

This article introduces a new natural-language testing system for AI agents that uses simulated isolates to automatically generate multi-turn simulations and evaluate agent behavior, helping developers catch regressions from prompt changes.

This paper proposes Continuous Power Forecasting, treating power forecasting as a continual learning problem to handle nonstationary conditions. It evaluates six CL approaches on real-world datasets, showing benefits in adaptation and mitigating catastrophic forgetting.

A user reports that gpt-5.5 via the codex agentRuntime silently drops tool calls on short conversational turns, resulting in no response despite the model generating text. The issue is specific to gpt-5.5 and is fixed by switching to deepseek/deepseek-v4-pro, suggesting a regression.

A structured survey of AI-based models for soil moisture estimation and classification, covering statistical time-series, geostatistical, classical ML, deep learning, and probabilistic/Bayesian methods.

Tokyo Institute of Technology has released free machine learning course materials covering topics like regression, neural networks, SVM, clustering, and PCA, with hands-on code using NumPy, scikit-learn, and PyTorch.

The author reports that the Gemma 4 12b QAT model suffers from a regression in tool calling and coding tasks compared to the standard Q5_K_L version, due to a bug involving control token misconfiguration. Despite high token speed, the model's inconsistent outputs make it unsuitable for agent workflows.

A method using Bayesian Probabilistic Context-Free Grammar to generate synthetic regression datasets that structurally resemble physics equations, validated against the Feynman corpus and shown to be effective for hyperparameter tuning.

Proposes Gaussian process latent factor regression (GPLFR) for low-data, high-dimensional output problems, demonstrating it with a spatially resolved emulator of global climate models for rocky exoplanets.

This paper evaluates the use of tabular foundation models, particularly TabPFN, for calibrating near-infrared spectroscopy data. The model shows strong performance on regression and classification tasks compared to traditional chemometric methods.

This paper introduces a distribution-aware reinforcement learning framework that enhances MLLM performance in long-tailed numerical regression tasks using batch-level comparison-based supervision.

User reports that Gemini previously provided useful audio feedback on music tracks but has stopped recognizing or analyzing uploaded files in the same chat.