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.
Data Market Design through Deep Learning
Authors: Sai Srivatsa Ravindranath, Yanchen Jiang, David C. Parkes
NeurIPS 2023 | Venue PDF | LLM Run Details | Input Tokens: 30,846 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,816 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 | Our experiments demonstrate that this new deep learning framework can almost precisely replicate all known solutions from theory, expand to more complex settings, and be used to establish the optimality of new designs for data markets and make conjectures in regard to the structure of optimal designs. [...] 5 Experimental Results for the Single Buyer Setting [...] 6 Experimental Results for the Multi-Buyer Setting |
| Researcher Affiliation | Academia | Sai Srivatsa Ravindranath Yanchen Jiang David C. Parkes Harvard John A. Paulson School of Engineering and Applied Sciences {saisr, yanchen_jiang, parkes} @g.harvard.edu |
| Pseudocode | No | The paper includes figures illustrating neural network architectures (e.g., Figure 4, Figure 5) but does not provide any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | The source code for all experiments is available from Github at https://github.com/saisrivatsan/ deep-data-markets/ |
| Open Datasets | Yes | We specifically consider the following settings with binary states and binary actions with payoffs v = 1 and interim beliefs drawn from: A. an unit interval, i.e, θ U[0, 1]. B. an equal weight mixture of Beta(8, 30) and Beta(60, 30). [...] The optimal menus for each of these settings are given by [7]. |
| Dataset Splits | No | The paper mentions training data ("minibatch", "ℓi.i.d samples S") and test data ("separate test set") but does not specify a separate validation dataset split with explicit percentages, counts, or a detailed methodology for splitting beyond "separate test set". |
| Hardware Specification | Yes | All our experiments were run on a single NVIDIA Tesla V100 GPU. |
| Software Dependencies | No | The paper mentions software components like "Adam Optimizer" and "Leaky ReLU activation functions" but does not provide specific version numbers for any libraries or dependencies. |
| Experiment Setup | Yes | We set the softmax temperature τ to 1/200. We train Rochet Net for 20, 000 iterations with a minibatch of size 2^15 sampled online for every update. [...] For the multi-buyer setting, all our neural networks consist of 3 hidden layers with 200 hidden units each. [...] We train the neural networks for 20000 iterations and make parameter updates using the Adam Optimizer with a learning rate of 0.001. |