Efficient Online Portfolio with Logarithmic Regret
Authors: Haipeng Luo, Chen-Yu Wei, Kai Zheng
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study the decades-old problem of online portfolio management and propose the first algorithm with logarithmic regret that is not based on Cover s Universal Portfolio algorithm and admits much faster implementation. Specifically Universal Portfolio enjoys optimal regret O(N ln T) for N financial instruments over T rounds, but requires log-concave sampling and has a large polynomial running time. Our algorithm, on the other hand, ensures a slightly larger but still logarithmic regret of O(N 2(ln T)4), and is based on the well-studied Online Mirror Descent framework with a novel regularizer that can be implemented via standard optimization methods in time O(TN 2.5) per round. The regret of all other existing works is either polynomial in T or has a potentially unbounded factor such as the inverse of the smallest price relative. |
| Researcher Affiliation | Academia | Haipeng Luo Department of Computer Science University of Southern California haipengl@usc.edu Chen-Yu Wei Department of Computer Science University of Southern California chenyu.wei@usc.edu Kai Zheng Key Laboratory of Machine Perception, MOE, School of EECS, Peking University Center for Data Science, Peking University, Beijing Institute of Big Data Research zhengk92@pku.edu.cn |
| Pseudocode | Yes | Algorithm 1: BARrier-Regularized Online Newton Step (BARRONS) and Algorithm 2: ADA-BARRONS |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-source code of their described methodology. |
| Open Datasets | No | The paper does not mention the use of any specific dataset that is publicly available or open. |
| Dataset Splits | No | The paper does not provide any information about training, validation, or test dataset splits. |
| Hardware Specification | No | The paper discusses theoretical computational complexity but does not specify any hardware (e.g., CPU/GPU models, memory, or cloud instances) used for running experiments. |
| Software Dependencies | No | The paper refers to the 'Interior Point Method' as a general optimization technique but does not list specific software dependencies with version numbers (e.g., libraries, frameworks, or solvers). |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup including concrete hyperparameter values or training configurations for empirical validation. |