Train 'n Trade: Foundations of Parameter Markets
Authors: Tzu-Heng Huang, Harit Vishwakarma, Frederic Sala
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate it in both theoretical and empirical settings. Theoretically, in basic scenarios we show how agent training converges faster through purchasing parameters in the market. We offer bounds on the improvement gained via trading when training linear models. Empirically, we conduct experiments in a variety of practical scenarios to validate the framework s effectiveness. |
| Researcher Affiliation | Academia | Tzu-Heng Huang, Harit Vishwakarma, Frederic Sala Department of Computer Science University of Wisconsin-Madison {thuang273, hvishwakarma}@wisc.edu, fredsala@cs.wisc.edu |
| Pseudocode | Yes | Algorithm 1 Single Round of Parameter Trading |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository for the methodology described. |
| Open Datasets | Yes | We use MNIST [33], CIFAR10 [34], and Tiny Image Net [35] for training MLPs and Res Net20 [36]. |
| Dataset Splits | No | The paper mentions a validation dataset for the broker and describes data endowments for agents, but it does not specify explicit training/validation/test splits (e.g., percentages or sample counts) for the datasets used in the experiments (MNIST, CIFAR10, Tiny Image Net) that would be needed for reproducibility. |
| Hardware Specification | No | The paper does not provide specific details regarding the hardware (e.g., CPU, GPU models, or memory) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python, PyTorch, or library versions) required to replicate the experiments. |
| Experiment Setup | Yes | Models are trained from different random initializations and batch orders over 60 epochs. Agents trade entire parameter sets and join the market after five epochs. |