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].
Contextual Pricing for Lipschitz Buyers
Authors: Jieming Mao, Renato Leme, Jon Schneider
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We investigate the problem of learning a Lipschitz function from binary feedback. ... For the symmetric loss ... we provide an algorithm for this problem achieving total loss O(log T) when d = 1 and O(T (d 1)/d) when d > 1, and show that both bounds are tight (up to a factor of log T). To prove these results, we investigate a more general problem, which we term learning a Lipschitz function with binary feedback, and which may be of independent interest. |
| Researcher Affiliation | Collaboration | Jieming Mao University of Pennsylvania EMAIL Renato Paes Leme Google Research EMAIL Jon Schneider Google Research EMAIL |
| Pseudocode | Yes | Algorithm 1 Algorithm for learning a L-Lipschitz function from R to R under symmetric loss with regret O(L log T). ... Algorithm 2 Midpoint algorithm for learning a L-Lipschitz function from R to R under symmetric loss with regret O(L log T). |
| Open Source Code | No | The paper does not provide any statement or link indicating the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper focuses on theoretical algorithms and their bounds, and does not describe the use of any specific dataset for training, nor does it provide access information for a publicly available dataset. |
| Dataset Splits | No | The paper is theoretical and does not describe experimental validation on data, thus no information on training, validation, or test dataset splits is provided. |
| Hardware Specification | No | The paper is theoretical and focuses on algorithms and proofs; therefore, it does not provide any specific details regarding hardware used for experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for software components, libraries, or solvers, as it focuses on theoretical contributions rather than practical implementation details or experimental setups. |
| Experiment Setup | No | The paper is theoretical and describes algorithms and proofs rather than empirical experiments, so it does not provide specific experimental setup details such as hyperparameters or system-level training settings. |