Imagen de Portada

Unsupervised Machine Learning Hybrid Approach Integrating Linear Programming in Loss Function: A Robust Optimization Technique

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Kiruluta, Andrew, Lemos, Andreas
Formato:	Preprint
Publicado:	2024
Materias:	Machine Learning Artificial Intelligence Optimization and Control
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Learning Backdoors for Mixed Integer Linear Programs with Contrastive Learning
por: Cai, Junyang, et al.
Publicado: (2024)

Multi-task Representation Learning for Mixed Integer Linear Programming
por: Cai, Junyang, et al.
Publicado: (2024)

Contextual Distributionally Robust Optimization with Causal and Continuous Structure: An Interpretable and Tractable Approach
por: Zhang, Fenglin, et al.
Publicado: (2026)

Deep Learning for Two-Stage Robust Integer Optimization
por: Dumouchelle, Justin, et al.
Publicado: (2023)

How Memory in Optimization Algorithms Implicitly Modifies the Loss
por: Cattaneo, Matias D., et al.
Publicado: (2025)

Unsupervised Training of Diffusion Models for Feasible Solution Generation in Neural Combinatorial Optimization
por: Hong, Seong-Hyun, et al.
Publicado: (2024)

Hyperparameter Optimization for Driving Strategies Based on Reinforcement Learning
por: Adde, Nihal Acharya, et al.
Publicado: (2024)

Logistics Hub Location Optimization: A K-Means and P-Median Model Hybrid Approach Using Road Network Distances
por: Rahman, Muhammad Abdul, et al.
Publicado: (2023)

On a Gradient Approach to Chebyshev Center Problems with Applications to Function Learning
por: Raghuvanshi, Abhinav, et al.
Publicado: (2026)

Hindsight-Guided Momentum (HGM) Optimizer: An Approach to Adaptive Learning Rate
por: Sarkar, Krisanu
Publicado: (2025)

Robust Evolutionary Multi-Objective Network Architecture Search for Reinforcement Learning (EMNAS-RL)
por: Adde, Nihal Acharya, et al.
Publicado: (2025)

Optimizing Inventory Routing: A Decision-Focused Learning Approach using Neural Networks
por: Islam, MD Shafikul, et al.
Publicado: (2023)

Differentiable Distributionally Robust Optimization Layers
por: Ma, Xutao, et al.
Publicado: (2024)

Riemann Sum Optimization for Accurate Integrated Gradients Computation
por: Swain, Swadesh, et al.
Publicado: (2024)

Differentiable Optimization for Deep Learning-Enhanced DC Approximation of AC Optimal Power Flow
por: Rosemberg, Andrew, et al.
Publicado: (2025)

A Safe Screening Rule with Bi-level Optimization of $ν$ Support Vector Machine
por: Yang, Zhiji, et al.
Publicado: (2024)

Adaptively Robust LLM Inference Optimization under Prediction Uncertainty
por: Chen, Zixi, et al.
Publicado: (2025)

Solving Functional Optimization with Deep Networks and Variational Principles
por: Kamtue, Kawisorn, et al.
Publicado: (2024)

Policy Optimization in Hybrid Discrete-Continuous Action Spaces via Mixed Gradients
por: Alvo, Matias, et al.
Publicado: (2026)

Optimal and Diffusion Transports in Machine Learning
por: Peyré, Gabriel
Publicado: (2025)

Federated Distributionally Robust Optimization with Non-Convex Objectives: Algorithm and Analysis
por: Jiao, Yang, et al.
Publicado: (2023)

Nash Equilibria, Regularization and Computation in Optimal Transport-Based Distributionally Robust Optimization
por: Shafiee, Soroosh, et al.
Publicado: (2023)

ECPv2: Fast, Efficient, and Scalable Global Optimization of Lipschitz Functions
por: Fourati, Fares, et al.
Publicado: (2025)

DiscoverDCP: A Data-Driven Approach for Construction of Disciplined Convex Programs via Symbolic Regression
por: Myhre, Sveinung
Publicado: (2025)

Inverse Mixed-Integer Programming: Learning Constraints then Objective Functions
por: Kitaoka, Akira
Publicado: (2025)

Beyond Minimax Rates in Group Distributionally Robust Optimization via a Novel Notion of Sparsity
por: Nguyen, Quan, et al.
Publicado: (2024)

Understanding Optimization in Deep Learning with Central Flows
por: Cohen, Jeremy M., et al.
Publicado: (2024)

$γ$-weakly $θ$-up-concavity: A Unified Framework for Non-Convex Optimization Beyond DR-Submodular and OSS Functions
por: Pedramfar, Mohammad, et al.
Publicado: (2026)

Convex and Bilevel Optimization for Neuro-Symbolic Inference and Learning
por: Dickens, Charles, et al.
Publicado: (2024)

Towards Stable Machine Learning Model Retraining via Slowly Varying Sequences
por: Bertsimas, Dimitris, et al.
Publicado: (2024)

Learning Branching Policies for MILPs with Proximal Policy Optimization
por: Mhamed, Abdelouahed Ben, et al.
Publicado: (2025)

Robust Reinforcement Learning in Finance: Modeling Market Impact with Elliptic Uncertainty Sets
por: Ma, Shaocong, et al.
Publicado: (2025)

A Benchmark for Maximum Cut: Towards Standardization of the Evaluation of Learned Heuristics for Combinatorial Optimization
por: Nath, Ankur, et al.
Publicado: (2024)

Integrated Offline and Online Learning to Solve a Large Class of Scheduling Problems
por: Liu, Anbang, et al.
Publicado: (2025)

Sublinear Regret for a Class of Continuous-Time Linear-Quadratic Reinforcement Learning Problems
por: Huang, Yilie, et al.
Publicado: (2024)

Federated Dynamical Low-Rank Training with Global Loss Convergence Guarantees
por: Schotthöfer, Steffen, et al.
Publicado: (2024)

Every Call is Precious: Global Optimization of Black-Box Functions with Unknown Lipschitz Constants
por: Fourati, Fares, et al.
Publicado: (2025)

The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric
por: Harvey, Thomas R.
Publicado: (2025)

ART for Diffusion Sampling: A Reinforcement Learning Approach to Timestep Schedule
por: Huang, Yilie, et al.
Publicado: (2026)

Multi-Objective Optimization for Sparse Deep Multi-Task Learning
por: Hotegni, S. S., et al.
Publicado: (2023)