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 [1].
Heteroscedastic Sequences: Beyond Gaussianity
Authors: Oren Anava, Shie Mannor
ICML 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The theoretical results are corroborated by an empirical study. |
| Researcher Affiliation | Academia | Oren Anava EMAIL Technion, Haifa, Israel Shie Mannor EMAIL Technion, Haifa, Israel |
| Pseudocode | Yes | Algorithm 1 LAZY OGD (on the โ2 unit ball) |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository for the described methodology. |
| Open Datasets | No | The paper describes generating synthetic data using the ARCH model (Equations (6) and (7)) with specified parameters and error distributions, but it does not provide access information (link, DOI, citation) to a pre-existing publicly available dataset. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, or citations to predefined splits) for training, validation, or testing. It describes an online, sequential evaluation up to 1000 rounds. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | To test the robustness of our approach to different error distributions, we generate three time series using the ARCH model (Equations (6) and (7)) with u0 = (0, 0.55, 0.11) and v0 = (0.1, 0.25, 0.25), each differs only in its error distribution." and "if we choose ฮทSig = ฮทVar = 1 / sqrt(T) |