Low-Rank Bandit Methods for High-Dimensional Dynamic Pricing
Authors: Jonas W. Mueller, Vasilis Syrgkanis, Matt Taddy
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Experiments We evaluate the performance of our methodology in settings where noisy demands are generated according to equation (2)... Our proposed algorithms are compared against the GDG online bandit algorithm of Flaxman et al. [2005]... Figures 1A and 1B show that our OPOK and OPOL algorithms are greatly superior to GDG... |
| Researcher Affiliation | Collaboration | Jonas Mueller MIT CSAIL jonasmueller@csail.mit.edu Vasilis Syrgkanis Microsoft Research vasy@microsoft.com Matt Taddy Chicago Booth taddy@chicagobooth.edu |
| Pseudocode | Yes | Algorithm 1 OPOK (Online Pricing Optimization with Known Features) ... Algorithm 2 FINDPRICE(x; U, S, pt 1) ... Algorithm 3 PROJECTION(x, , U, S) ... Algorithm 4 OPOL (Online Pricing Optimization with Latent Features) |
| Open Source Code | No | The paper does not contain any explicit statement about providing open-source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | Yes | Historical demand data obtained from: www.kaggle.com/c/grupo-bimbo-inventory-demand/ |
| Dataset Splits | No | The paper describes performance evaluation of online algorithms and analysis of historical data, but does not provide details on specific training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models, or cloud instance types used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific details on software dependencies with version numbers (e.g., programming languages, libraries, or solvers) used for implementing the described methods. |
| Experiment Setup | Yes | Throughout, pt and qt represent rescaled rather than absolute prices/demands, such that the feasible set S can be simply fixed as a centered sphere of radius r 20. Noise in the (rescaled) demands for each individual product is always sampled as: t Np0, 10q. Before each experiment, we sample the entries of z, V independently as zij Np100, 20q, Vij Np0, 2q, and U is fixed as a random sparse binary matrix... |