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..
Minimizing Adaptive Regret with One Gradient per Iteration
Authors: Guanghui Wang, Dakuan Zhao, Lijun Zhang
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we present empirical results on different data sets to evaluate the proposed algorithms. ... Empirical results demonstrate the efficiency and effectiveness of our methods. |
| Researcher Affiliation | Academia | Guanghui Wang, Dakuan Zhao, Lijun Zhang National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 The meta algorithm for exp-concave and strongly convex functions... Algorithm 2 The algorithm for expert Ei (exp-concave version)... Algorithm 3 The algorithm for expert Ei (strongly convex version)... Algorithm 4 The meta algorithm for general convex functions... Algorithm 5 The algorithm for expert Ei (general convex version) |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | Following the same spirit as in Section 4.1., We build a dynamic scenario based on real-world binary classification data set IJCNN01 [Prokhorov, 2001; Chang and Lin, 2011]. |
| Dataset Splits | No | The paper describes how data is sampled over iterations for dynamic scenarios, but it does not specify traditional train/validation/test splits (e.g., percentages or counts) for a fixed dataset. For IJCNN01, it describes a dynamic data usage pattern over 9000 iterations, where training data batches arrive and labels are flipped in certain intervals, rather than defining static data splits for training, validation, and testing. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments (e.g., CPU, GPU models, memory). |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers used in the experiments. |
| Experiment Setup | Yes | Following Jun et al. [2017], we scale both loss and surrogate loss by assigning a = 500 and capping them above at 1. The value of b is empirically set as 10 4. ... We set n = 256 for both algorithms. ... To make Assumption 2 is satisfied, we also add a domain constraint such that the optimal parameters are inside a d-dimensional ball with radius 10. |