Papers regression
“regression” 태그가 달린 논문 9,424편 · 필터 해제
Language Integration in Fine-Tuning Multimodal Large Language Models for Image-Based Regression
Multimodal Large Language Models (MLLMs) show promise for image-based regression tasks, but current approaches face key limitations. Recent methods fine-tune MLLMs using preset output vocabularies and generic task-level …
Aesthetics Quality AssessmentNo-Reference Image Quality AssessmentregressionNeural Network-Guided Symbolic Regression for Interpretable Descriptor Discovery in Perovskite Catalysts
Understanding and predicting the activity of oxide perovskite catalysts for the oxygen evolution reaction (OER) requires descriptors that are both accurate and physically interpretable. While symbolic regression (SR) off…
Feature ImportanceregressionSymbolic RegressionImbalanced Regression Pipeline Recommendation
Imbalanced problems are prevalent in various real-world scenarios and are extensively explored in classification tasks. However, they also present challenges for regression tasks due to the rarity of certain target value…
AutoMLMeta-LearningregressionSecond-Order Bounds for [0,1]-Valued Regression via Betting Loss
We consider the $[0,1]$-valued regression problem in the i.i.d. setting. In a related problem called cost-sensitive classification, \citet{foster21efficient} have shown that the log loss minimizer achieves an improved ge…
Distributional Reinforcement LearningregressionSparse Regression Codes exploit Multi-User Diversity without CSI
We study sparse regression codes (SPARC) for multiple access channels with multiple receive antennas, in non-coherent flat fading channels. We propose a novel practical decoder, referred to as maximum likelihood matching…
DecoderDiversityregressionBradley-Terry and Multi-Objective Reward Modeling Are Complementary
Reward models trained on human preference data have demonstrated strong effectiveness in aligning Large Language Models (LLMs) with human intent under the framework of Reinforcement Learning from Human Feedback (RLHF). H…
AttributeregressionActive Learning for Manifold Gaussian Process Regression
This paper introduces an active learning framework for manifold Gaussian Process (GP) regression, combining manifold learning with strategic data selection to improve accuracy in high-dimensional spaces. Our method joint…
Active LearningDimensionality ReductionregressionA Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression
Predictive maintenance (PdM) has become a crucial element of modern industrial practice. PdM plays a significant role in operational dependability and cost management by decreasing unforeseen downtime and optimizing asse…
BenchmarkingManagementregressionIntegrating Pharmacokinetics and Pharmacodynamics Modeling with Quantum Regression for Predicting Herbal Compound Toxicity
Herbal compounds present complex toxicity profiles that are often influenced by both intrinsic chemical properties and pharmacokinetics (PK) governing absorption and clearance. In this study, we develop a quantum regress…
regressionVector Contrastive Learning For Pixel-Wise Pretraining In Medical Vision
Contrastive learning (CL) has become a cornerstone of self-supervised pretraining (SSP) in foundation models, however, extending CL to pixel-wise representation, crucial for medical vision, remains an open problem. Stand…
Contrastive LearningFeature CorrelationregressionSelf-LearningThe Persistent Effects of Peru's Mining MITA: Double Machine Learning Approach
This study examines the long-term economic impact of the colonial Mita system in Peru, building on Melissa Dell's foundational work on the enduring effects of forced labor institutions. The Mita, imposed by the Spanish c…
regressionNewtonian and Lagrangian Neural Networks: A Comparison Towards Efficient Inverse Dynamics Identification
Accurate inverse dynamics models are essential tools for controlling industrial robots. Recent research combines neural network regression with inverse dynamics formulations of the Newton-Euler and the Euler-Lagrange equ…
Industrial RobotsregressionIdentifiable Convex-Concave Regression via Sub-gradient Regularised Least Squares
We propose a novel nonparametric regression method that models complex input-output relationships as the sum of convex and concave components. The method-Identifiable Convex-Concave Nonparametric Least Squares (ICCNLS)-d…
BenchmarkingregressionGRASP: Grouped Regression with Adaptive Shrinkage Priors
We introduce GRASP, a simple Bayesian framework for regression with grouped predictors, built on the normal beta prime (NBP) prior. The NBP prior is an adaptive generalization of the horseshoe prior with tunable hyperpar…
regressionFaster Low-Rank Approximation and Kernel Ridge Regression via the Block-Nyström Method
The Nystr\"om method is a popular low-rank approximation technique for large matrices that arise in kernel methods and convex optimization. Yet, when the data exhibits heavy-tailed spectral decay, the effective dimension…
regressionPredicting Stock Market Crash with Bayesian Generalised Pareto Regression
This paper develops a Bayesian Generalised Pareto Regression (GPR) model to forecast extreme losses in Indian equity markets, with a focus on the Nifty 50 index. Extreme negative returns, though rare, can cause significa…
GPRregressionEmpirical Models of the Time Evolution of SPX Option Prices
The key objective of this paper is to develop an empirical model for pricing SPX options that can be simulated over future paths of the SPX. To accomplish this, we formulate and rigorously evaluate several statistical mo…
regressionA Simplified Analysis of SGD for Linear Regression with Weight Averaging
Theoretically understanding stochastic gradient descent (SGD) in overparameterized models has led to the development of several optimization algorithms that are widely used in practice today. Recent work by~\citet{zou202…
regressionSchedulingTRUST: Transparent, Robust and Ultra-Sparse Trees
Piecewise-constant regression trees remain popular for their interpretability, yet often lag behind black-box models like Random Forest in predictive accuracy. In this work, we introduce TRUST (Transparent, Robust, and U…
regressionCAWR: Corruption-Averse Advantage-Weighted Regression for Robust Policy Optimization
Offline reinforcement learning (offline RL) algorithms often require additional constraints or penalty terms to address distribution shift issues, such as adding implicit or explicit policy constraints during policy opti…
D4RLOffline RLregression