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.
Doubly Robust Covariate Shift Correction
Authors: Sashank Reddi, Barnabas Poczos, Alex Smola
AAAI 2015 | Venue PDF | LLM Run Details | Input Tokens: 15,538 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,582 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 prove that this yields an efficient estimator and demonstrate good experimental performance. [...] Finally, we show good experimental performance on several UCI datasets. |
| Researcher Affiliation | Academia | Sashank J. Reddi Machine Learning Department Carnegie Mellon University EMAIL Barnab as P oczos Machine Learning Department Carnegie Mellon University EMAIL Alex Smola Machine Learning Department Carnegie Mellon University EMAIL |
| Pseudocode | No | The paper describes the algorithm steps in textual form but does not provide a formal pseudocode block or algorithm listing. |
| Open Source Code | No | The paper does not provide a link to open-source code for the described methodology or state that code will be released. |
| Open Datasets | Yes | Real Data: For a more realistic comparison we apply our method to several UCI3 and benchmark4 datasets. To control the amount of bias we use PCA to obtain the leading principal component. The projections onto the ο¬rst principal component are then used to construct a subsampling distribution q. [...] 3http://archive.ics.uci.edu/ml/datasets.html 4http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/ |
| Dataset Splits | Yes | The regularization parameters are chosen separately for each empirical estimator by cross validation. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models or memory used for experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | We use linear regression with standard β2 penalization. [...] The bandwidth of the kernel is chosen by cross-validation. [...] The regularization parameters are chosen separately for each empirical estimator by cross validation. [...] As explained earlier, we first train a regression tree on the unweighted dataset and then build a differential regression tree on the residual with restricted tree depth in order to train the doubly robust regression tree. |