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.
Weitzman's Rule for Pandora's Box with Correlations
Authors: Evangelia Gergatsouli, Christos Tzamos
NeurIPS 2023 | Venue PDF | LLM Run Details | Input Tokens: 24,855 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,528 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work we revisit PANDORA S BOX when the value distributions are correlated, first studied in Chawla et al. [2020]. We show that the optimal algorithm for the independent case, given by Weitzman s rule, directly works for the correlated case. In fact, our algorithm results in significantly improved approximation guarantees compared to the previous work, while also being substantially simpler. We also show how to implement the rule given only sample access to the correlated distribution of values. |
| Researcher Affiliation | Academia | Evangelia Gergatsouli University of Wisconsin-Madison EMAIL Christos Tzamos University of Wisconsin-Madison & University of Athens EMAIL |
| Pseudocode | Yes | Algorithm 1: Weitzman s algorithm, for correlated D. Algorithm 2: Weitzman s rule for Partial Updates Algorithm 3: Weitzman s rule for Full Updates Algorithm 4: General format of PANDORA S BOX algorithm. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | No | The paper is theoretical and discusses properties of distributions and sampling for analytical purposes (e.g., learnability), but does not use or provide access information for a publicly available dataset for empirical training. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments with dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup or specific hardware used. |
| Software Dependencies | No | The paper is theoretical and does not describe any software dependencies with specific version numbers. |
| Experiment Setup | No | The paper is theoretical and does not provide details on experimental setup, hyperparameters, or training configurations. |