Dynamic Rebalancing

Dynamic rebalancing addresses the challenge of efficiently adjusting the composition of a system over time to optimize performance, encompassing diverse applications from financial portfolio management to bike-sharing and electric vehicle fleet operations. Current research heavily utilizes reinforcement learning, often coupled with other techniques like graph neural networks or linear programming, to create adaptive strategies that account for dynamic conditions and constraints, such as transaction costs or limited vehicle range. These advancements offer significant potential for improving resource allocation, reducing operational costs, and enhancing the efficiency and robustness of various real-world systems.

Papers

November 14, 2024

Long-Tailed Object Detection Pre-training: Dynamic Rebalancing Contrastive Learning with Dual Reconstruction
Chen-Long Duan, Yong Li, Xiu-Shen Wei, Lin Zhao
Contrastive Learning Pre Training Long Tailed Object Detection Structured Reconstruction Dynamic Rebalancing

October 8, 2024

Generalizing to any diverse distribution: uniformity, gentle finetuning and rebalancing
Andreas Loukas, Karolis Martinkus, Ed Wagstaff, Kyunghyun Cho
Domain Adaptation Strong Generalization Uniformity Metric Dynamic Rebalancing Distribution Classifier Diverse Test

October 2, 2024

Dynamic Portfolio Rebalancing: A Hybrid new Model Using GNNs and Pathfinding for Cost Efficiency
Diego Vallarino
Graph Neural Network High Frequency Hybrid Model Cost Effective Portfolio Optimization Dynamic Rebalancing

June 2, 2024

Dual Policy Reinforcement Learning for Real-time Rebalancing in Bike-sharing Systems
Jiaqi Liang, Defeng Liu, Sanjay Dominik Jena, Andrea Lodi, Thibaut Vidal
Bike Sharing System Dynamic Rebalancing Vehicle Rebalancing Dual Reinforcement Learning

April 27, 2024

Application of Deep Learning for Factor Timing in Asset Management
Prabhu Prasad Panda, Maysam Khodayari Gharanchaei, Xilin Chen, Haoshu Lyu
Deep Learning Application Proficiency Regression Model Ridge Regression Portfolio Management Dynamic Rebalancing Factor Timing

February 6, 2024

Do we need rebalancing strategies? A theoretical and empirical study around SMOTE and its variants
Abdoulaye Sakho, Emmanuel Malherbe, Erwan Scornet
Empirical Study Natural Language Oriented Variant Synthetic Minority Oversampling Technique Dynamic Rebalancing

February 5, 2024

A Reinforcement Learning Approach for Dynamic Rebalancing in Bike-Sharing System
Jiaqi Liang, Sanjay Dominik Jena, Defeng Liu, Andrea Lodi
Reinforcement Learning Bike Sharing System Dynamic Rebalancing Vehicle Rebalancing

November 27, 2023

Planning for the Efficient Updating of Mutual Fund Portfolios
Tomás de la Rosa
Task Planning Linear Programming Heuristic Search Parameter Update Portfolio Management Dynamic Rebalancing Mutual Fund

September 18, 2023

Adaptive Liquidity Provision in Uniswap V3 with Deep Reinforcement Learning
Haochen Zhang, Xi Chen, Lin F. Yang
Deep Reinforcement Learning Decentralized Finance Dynamic Rebalancing Liquidity Pool

August 5, 2023

Reinforcement Learning for Financial Index Tracking
Xianhua Peng, Chenyin Gong, Xue Dong He
Reinforcement Learning Relevant Covariates Finite Horizon Dynamic Rebalancing Index Tracking

May 2, 2023

DRPT: Disentangled and Recurrent Prompt Tuning for Compositional Zero-Shot Learning
Xiaocheng Lu, Ziming Liu, Song Guo, Jingcai Guo, Fushuo Huo, Sikai Bai, Tao Han
Vision Language Model Prompt Tuning Compositional Zero Shot Learning Dynamic Rebalancing

February 10, 2023

Long-Tailed Partial Label Learning via Dynamic Rebalancing
Feng Hong, Jiangchao Yao, Zhihan Zhou, Ya Zhang, Yanfeng Wang
Long Tailed Partial Label Learning Dynamic Rebalancing Label Ambiguity Label Disambiguation Long Tailed Semi Supervised Learning

November 11, 2022

Fleet Rebalancing for Expanding Shared e-Mobility Systems: A Multi-agent Deep Reinforcement Learning Approach
Man Luo, Bowen Du, Wenzhe Zhang, Tianyou Song, Kun Li, Hongming Zhu, Mark Birkin, Hongkai Wen
Dynamic Rebalancing E Mobility Shared E Mobility

October 13, 2022

Deep Reinforcement Learning-based Rebalancing Policies for Profit Maximization of Relay Nodes in Payment Channel Networks
Nikolaos Papadis, Leandros Tassiulas
Deep Reinforcement Revenue Maximization Dynamic Rebalancing Wireless Node Payment Channel

October 10, 2022

A Hybrid Active-Passive Approach to Imbalanced Nonstationary Data Stream Classification
Kleanthis Malialis, Manuel Roveri, Cesare Alippi, Christos G. Panayiotou, Marios M. Polycarpou
Incremental Learning Concept Drift Drift Detection Imbalanced Datasets Learning Model Dynamic Rebalancing

September 17, 2022

A Robust and Constrained Multi-Agent Reinforcement Learning Electric Vehicle Rebalancing Method in AMoD Systems
Sihong He, Yue Wang, Shuo Han, Shaofeng Zou, Fei Miao
Transition Kernel Dynamic Rebalancing Autonomous Mobility on Demand Vehicle Rebalancing

Dynamic Rebalancing

Papers

Long-Tailed Object Detection Pre-training: Dynamic Rebalancing Contrastive Learning with Dual Reconstruction

Generalizing to any diverse distribution: uniformity, gentle finetuning and rebalancing

Dynamic Portfolio Rebalancing: A Hybrid new Model Using GNNs and Pathfinding for Cost Efficiency

Dual Policy Reinforcement Learning for Real-time Rebalancing in Bike-sharing Systems

Application of Deep Learning for Factor Timing in Asset Management

Do we need rebalancing strategies? A theoretical and empirical study around SMOTE and its variants

A Reinforcement Learning Approach for Dynamic Rebalancing in Bike-Sharing System

Planning for the Efficient Updating of Mutual Fund Portfolios

Adaptive Liquidity Provision in Uniswap V3 with Deep Reinforcement Learning

Reinforcement Learning for Financial Index Tracking

DRPT: Disentangled and Recurrent Prompt Tuning for Compositional Zero-Shot Learning

Long-Tailed Partial Label Learning via Dynamic Rebalancing

Fleet Rebalancing for Expanding Shared e-Mobility Systems: A Multi-agent Deep Reinforcement Learning Approach

Deep Reinforcement Learning-based Rebalancing Policies for Profit Maximization of Relay Nodes in Payment Channel Networks

A Hybrid Active-Passive Approach to Imbalanced Nonstationary Data Stream Classification

A Robust and Constrained Multi-Agent Reinforcement Learning Electric Vehicle Rebalancing Method in AMoD Systems