Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Adaptive Passive-Aggressive Framework for Online Regression with Side Information
Authors: Runhao Shi, Jiaxi Ying, Daniel Palomar
NeurIPS 2024 | Venue PDF | 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 EMAIL, EMAIL |
| 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. |