Adaptive Passive-Aggressive Framework for Online Regression with Side Information
Authors: Runhao Shi, Jiaxi Ying, Daniel Palomar
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments show that our model achieves outstanding performance associated with the side information while maintaining low tracking error, demonstrating marked improvements over traditional PA methods across various scenarios. |
| Researcher Affiliation | Academia | Runhao Shi, Jiaxi Ying, Daniel P. Palomar The Hong Kong University of Science and Technology {rshiaf, jx.ying}@connect.ust.hk, palomar@ust.hk |
| Pseudocode | Yes | Algorithm 1 Adaptive Passive-Aggressive Framework with Side Information (APAS) |
| Open Source Code | Yes | Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material? Answer: [Yes] , Justification: The code, data, and instructions to reproduce the experiments are available in the supplemental material. |
| Open Datasets | No | We conduct simulations on two well-known indices using real market data from Yahoo! Finance TM: the S&P 500 Index and the NASDAQ 100 Index. For the S&P 500 Index, we collect data from 2021-01-01 to 2023-01-01... For the NASDAQ 100 Index, we collect data from 2019-01-01 to 2021-01-01... The paper mentions using data from 'Yahoo! Finance TM' but does not provide a specific link, DOI, or formal citation for the collected dataset. |
| Dataset Splits | No | The training set consists of 50% of the data, while the test set contains the remaining 50%. The paper specifies training and test splits but does not explicitly mention a validation set split or methodology for it. |
| Hardware Specification | Yes | The experiments are conducted on a PC equipped with a 13th Gen Intel(R) Core(TM) i7-13700 CPU and 16GB of memory. |
| Software Dependencies | No | The paper mentions software like CVXR, PGD, and ADMM but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | The training set consists of 50% of the data, while the test set contains the remaining 50%. Both SLAIT-ETE and SLAIT-DR use a rolling training window of 100-day observations, rebalanced every 3 days. |