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].
Diameter-Based Active Learning
Authors: Christopher Tosh, Sanjoy Dasgupta
ICML 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide, for the first time, an efficient algorithm that is able to realize this upper bound, and we empirically demonstrate its good performance. |
| Researcher Affiliation | Academia | 1Department of Computer Science and Engineering, UC San Diego, La Jolla, CA, USA. |
| Pseudocode | Yes | Algorithm 1 DBAL; Algorithm 2 SELECT |
| Open Source Code | No | The paper does not include any statement or link providing access to open-source code for the methodology described. |
| Open Datasets | No | The paper describes generating data for simulations (e.g., 'both the prior distribution and the data distribution are uniform over the unit sphere'), rather than using pre-existing publicly available datasets with access information. |
| Dataset Splits | No | The paper describes simulation setups and data generation but does not specify training, validation, or test dataset splits in percentages or sample counts, nor does it reference predefined splits. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models, processor types, or memory used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python 3.8, PyTorch 1.9). |
| Experiment Setup | Yes | We tested on two hypothesis classes: homogeneous, or through-the-origin, linear separators and k-sparse monotone disjunctions. In our simulations, both the prior distribution and the data distribution are uniform over the unit sphere. In our simulations, each data point is a vector whose coordinates are i.i.d. Bernoulli random variables with parameter p. Figure 1 shows the results of our simulations on homogeneous linear separators. Left: d = 10. Middle: d = 25. Right: d = 50. The results of our simulations on k-sparse monotone disjunctions are in Figure 2. In all cases k = 4. Top left: d = 75, p = 0.25. Top right: d = 75, p = 0.5. Bottom left: d = 100, p = 0.25. Bottom right: d = 100, p = 0.5. |