Cover Image

Calibrated Multi-Level Quantile Forecasting

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Ding, Tiffany, Gibbs, Isaac, Tibshirani, Ryan J.
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Optimization and Control Methodology
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Locally Adaptive Multi-Objective Learning
by: Kaur, Jivat Neet, et al.
Published: (2026)

Sample-Efficient Omniprediction for Proper Losses
by: Gibbs, Isaac, et al.
Published: (2025)

Optimal Cross-Validation for Sparse Linear Regression
by: Cory-Wright, Ryan, et al.
Published: (2023)

Application-Driven Learning: A Closed-Loop Prediction and Optimization Approach Applied to Dynamic Reserves and Demand Forecasting
by: Garcia, Joaquim Dias, et al.
Published: (2021)

StablePCA: Distributionally Robust Learning of Shared Representations from Multi-Source Data
by: Wang, Zhenyu, et al.
Published: (2025)

AltGDmin: Alternating GD and Minimization for Partly-Decoupled (Federated) Optimization
by: Vaswani, Namrata
Published: (2025)

Global Group Fairness in Federated Learning via Function Tracking
by: Rychener, Yves, et al.
Published: (2025)

Optimizing Input Data Collection for Ranking and Selection
by: Song, Eunhye, et al.
Published: (2025)

Data-Driven Sequential Sampling for Tail Risk Mitigation
by: Ahn, Dohyun, et al.
Published: (2025)

Bridging Constraints and Stochasticity: A Fully First-Order Method for Stochastic Bilevel Optimization with Linear Constraints
by: Phan, Cac, et al.
Published: (2025)

Bayesian optimization for mixed variables using an adaptive dimension reduction process: applications to aircraft design
by: Saves, Paul, et al.
Published: (2025)

"Over-optimizing" for Normality: Budget-constrained Uncertainty Quantification for Contextual Decision-making
by: Wang, Yanyuan, et al.
Published: (2025)

Automatic Outlier Rectification via Optimal Transport
by: Blanchet, Jose, et al.
Published: (2024)

Learning Large Causal Structures from Inverse Covariance Matrix via Sparse Matrix Decomposition
by: Dong, Shuyu, et al.
Published: (2022)

Robust Data-driven Prescriptiveness Optimization
by: Poursoltani, Mehran, et al.
Published: (2023)

Fast Robust Kernel Regression through Sign Gradient Descent with Early Stopping
by: Allerbo, Oskar
Published: (2023)

SplitVAEs: Decentralized scenario generation from siloed data for stochastic optimization problems
by: Islam, H M Mohaimanul, et al.
Published: (2024)

A distribution-free mixed-integer optimization approach to hierarchical modelling of clustered and longitudinal data
by: Sankaranarayanan, Madhav, et al.
Published: (2023)

Random Pareto front surfaces
by: Tu, Ben, et al.
Published: (2024)

Changing the Kernel During Training Leads to Double Descent in Kernel Regression
by: Allerbo, Oskar
Published: (2023)

DCILP: A Distributed Approach for Large-Scale Causal Structure Learning
by: Dong, Shuyu, et al.
Published: (2024)

Learning Sequential Decisions from Multiple Sources via Group-Robust Markov Decision Processes
by: Xu, Mingyuan, et al.
Published: (2026)

From Contextual Data to Newsvendor Decisions: On the Actual Performance of Data-Driven Algorithms
by: Besbes, Omar, et al.
Published: (2023)

Arc travel time and path choice model estimation subsumed
by: Mohammadpour, Sobhan, et al.
Published: (2022)

Data-Driven Influence Functions for Optimization-Based Causal Inference
by: Jordan, Michael I., et al.
Published: (2022)

Structured Difference-of-Q via Orthogonal Learning
by: Cao, Defu, et al.
Published: (2024)

The Stochastic Proximal Distance Algorithm
by: Jiang, Haoyu, et al.
Published: (2022)

Convergence of projected stochastic natural gradient variational inference for various step size and sample or batch size schedules
by: Guilmeau, Thomas, et al.
Published: (2026)

Robust Learning Rate Selection for Stochastic Optimization via Splitting Diagnostic
by: Sordello, Matteo, et al.
Published: (2019)

Simultaneous estimation of multiple discrete unimodal distributions under stochastic order constraints
by: Yoshida, Yasuhiro, et al.
Published: (2026)

Proximal optimal transport divergences
by: Baptista, Ricardo, et al.
Published: (2025)

BoTier: Multi-Objective Bayesian Optimization with Tiered Composite Objectives
by: Haddadnia, Mohammad, et al.
Published: (2025)

Decision-Focused Bias Correction for Fluid Approximation
by: Er, Can, et al.
Published: (2025)

Online Resource Allocation with Average Budget Constraints
by: Ao, Ruicheng, et al.
Published: (2024)

Statistical Performance Guarantee for Subgroup Identification with Generic Machine Learning
by: Li, Michael Lingzhi, et al.
Published: (2023)

Gradient Equilibrium in Online Learning: Theory and Applications
by: Angelopoulos, Anastasios N., et al.
Published: (2025)

Set Invariance with Probability One for Controlled Diffusion: Score-based Approach
by: Wang, Wenqing, et al.
Published: (2025)

Accelerating Look-ahead in Bayesian Optimization: Multilevel Monte Carlo is All you Need
by: Yang, Shangda, et al.
Published: (2024)

Beyond Maximum Likelihood: Variational Inequality Estimation for Generalized Linear Models
by: Zhu, Linglingzhi, et al.
Published: (2025)

Statistical Analysis of Conditional Group Distributionally Robust Optimization with Cross-Entropy Loss
by: Guo, Zijian, et al.
Published: (2025)