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].
Accelerating ERM for data-driven algorithm design using output-sensitive techniques
Authors: Maria-Florina F. Balcan, Christopher Seiler, Dravyansh Sharma
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We design novel approaches that use tools from computational geometry and lead to output-sensitive algorithms for learning good parameters by implementing the ERM (Empirical Risk Minimization) for several distinct data-driven design problems. The resulting learning algorithms scale polynomially with the number of sum dual class function pieces RĪ£ in the worst case (See Table 1) and are efficient for small constant d. |
| Researcher Affiliation | Academia | Carnegie Mellon University, EMAIL. Work done by Christopher Seiler while he was at CMU. Corresponding author: EMAIL. Work done by Dravyansh Sharma while he was at CMU. |
| Pseudocode | Yes | Algorithm 1: OUTPUTSENSITIVEPARTITIONSEARCH |
| Open Source Code | No | The paper does not include an unambiguous statement about releasing its source code or a direct link to a code repository. |
| Open Datasets | No | The paper discusses 'problem instances' and 'problem samples' but does not provide concrete access information (link, DOI, repository, or formal citation) for a specific publicly available or open dataset used in its analysis. |
| Dataset Splits | No | The paper does not specify exact train/validation/test split percentages, absolute sample counts, or reference predefined splits for reproducibility. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running experiments. |
| Software Dependencies | No | The paper does not list specific software components with version numbers (e.g., Python 3.8, PyTorch 1.9) or self-contained solvers (e.g., CPLEX 12.4) needed to replicate the work. |
| Experiment Setup | No | The paper does not provide specific experimental setup details such as concrete hyperparameter values, training configurations, or system-level settings. |