The Label Complexity of Active Learning from Observational Data
Authors: Songbai Yan, Kamalika Chaudhuri, Tara Javidi
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present some empirical results in Section F in Appendix. |
| Researcher Affiliation | Academia | Songbai Yan University of California San Diego yansongbai@eng.ucsd.edu Kamalika Chaudhuri University of California San Diego kamalika@cs.ucsd.edu Tara Javidi University of California San Diego tjavidi@eng.ucsd.edu |
| Pseudocode | Yes | Algorithm 1 Disagreement-Based Active Learning with Logged Observational Data |
| Open Source Code | No | The paper does not provide an explicit statement about releasing its source code nor does it include a link to a repository for the code described in the paper. |
| Open Datasets | No | The paper defines abstract datasets like 'T0 = {(Xt, Yt, Zt)}m t=1' and 'additional n examples {(Xt, Yt)}m+n t=m+1 drawn i.i.d. from distribution D,' but it does not specify a concrete, publicly available dataset with access information (link, citation, or repository). |
| Dataset Splits | No | The paper discusses data collection ('logged observational data' and 'online stream') but does not provide specific details on training, validation, or test set splits, percentages, or sample counts within the main text. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU models, CPU types, or cloud computing instance specifications used for running experiments. |
| Software Dependencies | No | The paper references 'Vowpal Wabbit' but does not specify version numbers for any software components or libraries used in its methodology or experiments. |
| Experiment Setup | Yes | Input: confidence δ, logged data T0, epoch schedule τ1, . . . , τK, n = PK i=1 τi. |