Papers quantile regression
“quantile regression” 태그가 달린 논문 420편 · 필터 해제
2048: Reinforcement Learning in a Delayed Reward Environment
Delayed and sparse rewards present a fundamental obstacle for reinforcement-learning (RL) agents, which struggle to assign credit for actions whose benefits emerge many steps later. The sliding-tile game 2048 epitomizes …
quantile regressionreinforcement-learningReinforcement LearningReinforcement Learning (RL)Enhancing One-run Privacy Auditing with Quantile Regression-Based Membership Inference
Differential privacy (DP) auditing aims to provide empirical lower bounds on the privacy guarantees of DP mechanisms like DP-SGD. While some existing techniques require many training runs that are prohibitively costly, r…
Computational Efficiencyimage-classificationImage Classificationquantile regressionConditional Generative Modeling for Enhanced Credit Risk Management in Supply Chain Finance
The rapid expansion of cross-border e-commerce (CBEC) has created significant opportunities for small and medium-sized enterprises (SMEs), yet financing remains a critical challenge due to SMEs' limited credit histories.…
Managementquantile regressionSafety-Aware Reinforcement Learning for Control via Risk-Sensitive Action-Value Iteration and Quantile Regression
Mainstream approximate action-value iteration reinforcement learning (RL) algorithms suffer from overestimation bias, leading to suboptimal policies in high-variance stochastic environments. Quantile-based action-value i…
quantile regressionReinforcement Learning (RL)Membership Inference Attacks for Unseen Classes
Shadow model attacks are the state-of-the-art approach for membership inference attacks on machine learning models. However, these attacks typically assume an adversary has access to a background (nonmember) data distrib…
quantile regressionregressionScalable quantile predictions of peak loads for non-residential customer segments
Electrical grid congestion has emerged as an immense challenge in Europe, making the forecasting of load and its associated metrics increasingly crucial. Among these metrics, peak load is fundamental. Non-time-resolved m…
quantile regressionUncertainty Quantification in SVM prediction
This paper explores Uncertainty Quantification (UQ) in SVM predictions, particularly for regression and forecasting tasks. Unlike the Neural Network, the SVM solutions are typically more stable, sparse, optimal and inter…
feature selectionPredictionquantile regressionregression+1Behind the Noise: Conformal Quantile Regression Reveals Emergent Representations
Scientific imaging often involves long acquisition times to obtain high-quality data, especially when probing complex, heterogeneous systems. However, reducing acquisition time to increase throughput inevitably introduce…
DenoisingExperimental DesignImage Restorationquantile regression+1Extreme Conformal Prediction: Reliable Intervals for High-Impact Events
Conformal prediction is a popular method to construct prediction intervals for black-box machine learning models with marginal coverage guarantees. In applications with potentially high-impact events, such as flooding or…
Conformal PredictionPredictionPrediction Intervalsquantile regressionWho's at Risk? Effects of Inflation on Unemployment Risk
We empirically investigate the distributional effects of inflation on workers' unemployment tail risks using instrumental variable quantile regression. We find that supply-driven inflation disproportionately raises unemp…
quantile regressionLikelihood-Free Adaptive Bayesian Inference via Nonparametric Distribution Matching
When the likelihood is analytically unavailable and computationally intractable, approximate Bayesian computation (ABC) has emerged as a widely used methodology for approximate posterior inference; however, it suffers fr…
Bayesian InferenceDensity Estimationquantile regressionLearning Survival Distributions with the Asymmetric Laplace Distribution
Probabilistic survival analysis models seek to estimate the distribution of the future occurrence (time) of an event given a set of covariates. In recent years, these models have preferred nonparametric specifications th…
quantile regressionSurvival AnalysisExtended Fiducial Inference for Individual Treatment Effects via Deep Neural Networks
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…
quantile regressionDynamic Discrete-Continuous Choice Models: Identification and Conditional Choice Probability Estimation
This paper develops a general framework for dynamic models in which individuals simultaneously make both discrete and continuous choices. The framework incorporates a wide range of unobserved heterogeneity. I show that s…
quantile regressionA Piecewise Lyapunov Analysis of Sub-quadratic SGD: Applications to Robust and Quantile Regression
Motivated by robust and quantile regression problems, we investigate the stochastic gradient descent (SGD) algorithm for minimizing an objective function $f$ that is locally strongly convex with a sub--quadratic tail. Th…
quantile regressionregressionDeep Distributional Learning with Non-crossing Quantile Network
In this paper, we introduce a non-crossing quantile (NQ) network for conditional distribution learning. By leveraging non-negative activation functions, the NQ network ensures that the learned distributions remain monoto…
Distributional Reinforcement Learningquantile regressionReinforcement Learning (RL)Offline and Distributional Reinforcement Learning for Wireless Communications
The rapid growth of heterogeneous and massive wireless connectivity in 6G networks demands intelligent solutions to ensure scalability, reliability, privacy, ultra-low latency, and effective control. Although artificial …
Distributional Reinforcement LearningManagementquantile regressionreinforcement-learning+2Multi-stage model predictive control for slug flow crystallizers using uncertainty-aware surrogate models
This paper presents a novel dynamic model for slug flow crystallizers that addresses the challenges of spatial distribution without backmixing or diffusion, potentially enabling advanced model-based control. The develope…
Model Predictive Controlquantile regressionUncertainty QuantificationEnhanced Route Planning with Calibrated Uncertainty Set
This paper investigates the application of probabilistic prediction methodologies in route planning within a road network context. Specifically, we introduce the Conformalized Quantile Regression for Graph Autoencoders (…
Conformal PredictionDecision MakingPredictionquantile regressionEfficient Membership Inference Attacks by Bayesian Neural Network
Membership Inference Attacks (MIAs) aim to estimate whether a specific data point was used in the training of a given model. Previous attacks often utilize multiple reference models to approximate the conditional score d…
Bayesian InferenceInference AttackMembership Inference Attackquantile regression