Imagen de Portada

Locally Adaptive Multi-Objective Learning

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Kaur, Jivat Neet, Gibbs, Isaac, Jordan, Michael I.
Formato:	Preprint
Publicado:	2026
Materias:	Machine Learning Optimization and Control Methodology
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Conformal Prediction Sets with Improved Conditional Coverage using Trust Scores
por: Kaur, Jivat Neet, et al.
Publicado: (2025)

Calibrated Multi-Level Quantile Forecasting
por: Ding, Tiffany, et al.
Publicado: (2025)

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

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

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

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

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

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

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

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

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

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

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

Provable Offline Reinforcement Learning for Structured Cyclic MDPs
por: Lee, Kyungbok, et al.
Publicado: (2026)

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

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

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

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

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

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

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

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

Random Pareto front surfaces
por: Tu, Ben, et al.
Publicado: (2024)

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

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

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

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

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

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

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

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

The Stochastic Proximal Distance Algorithm
por: Jiang, Haoyu, et al.
Publicado: (2022)

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

The Sample Complexity of Online Reinforcement Learning: A Multi-model Perspective
por: Muehlebach, Michael, et al.
Publicado: (2025)

Causal Invariance Learning via Efficient Nonconvex Optimization
por: Wang, Zhenyu, et al.
Publicado: (2024)

$K$-Nearest-Neighbor Resampling for Off-Policy Evaluation in Stochastic Control
por: Giegrich, Michael, et al.
Publicado: (2023)

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

Proximal optimal transport divergences
por: Baptista, Ricardo, et al.
Publicado: (2025)

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

Low-Rank Thinning
por: Carrell, Annabelle Michael, et al.
Publicado: (2025)