Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Diameter-Based Active Learning
Authors: Christopher Tosh, Sanjoy Dasgupta
ICML 2017 | Venue PDF | LLM Run Details | Input Tokens: 15,455 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,061 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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. |