Joint-Perturbation Simultaneous Pseudo-Gradient
Authors: Carlos Martin, Tuomas Sandholm
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our approach on various games, including auctions, which have important real-world applications. Our approach yields a dramatic improvement in performance in terms of the wall time required to reach an approximate Nash equilibrium. We test our approach against the classical approach on several continuous-action games, in particular on many kinds of auction and on continuous Goofspiel (which can be thought of as a kind of sequential auction with budget constraints). |
| Researcher Affiliation | Collaboration | 1Carnegie Mellon University 2Strategy Robot, Inc. 3Optimized Markets, Inc. 4Strategic Machine, Inc. |
| Pseudocode | No | The paper describes the proposed method mathematically and in prose (Section 4 'Proposed Method'), including identities and estimators, but it does not present a structured pseudocode or algorithm block. |
| Open Source Code | No | The paper lists software used for the implementation ("For our code, we used Python 3.12.3, JAX 0.4.30 [Bradbury and others, 2018], Flax 0.8.5 [Heek and others, 2023], Optax 0.2.3 [Deep Mind and others, 2020], Matplotlib 3.9.1 [Hunter, 2007], and Sci Py 1.14.0 [Virtanen and others, 2020]") but does not explicitly state that the authors' own code for the methodology is open-source or provide a link to a code repository. |
| Open Datasets | No | For our experiment, we use a prior where bidder-item valuations vij are independently sampled uniformly at random from the unit interval. In our experiment, we sample the vis and cis from the standard uniform distribution, and sample C from the standard uniform distribution on [0, n]. The paper describes generating synthetic data for various auction scenarios by sampling from uniform distributions, rather than using a pre-existing public dataset with specific access information. |
| Dataset Splits | No | The paper describes generating data from specified distributions for its experiments, rather than using a fixed dataset that would then be split into training, validation, and test sets. Therefore, there is no explicit mention of dataset splits like percentages or sample counts for reproduction. |
| Hardware Specification | Yes | Each experiment was run individually on one NVIDIA A100 SXM4 40GB GPU. |
| Software Dependencies | Yes | For our code, we used Python 3.12.3, JAX 0.4.30 [Bradbury and others, 2018], Flax 0.8.5 [Heek and others, 2023], Optax 0.2.3 [Deep Mind and others, 2020], Matplotlib 3.9.1 [Hunter, 2007], and Sci Py 1.14.0 [Virtanen and others, 2020]. |
| Experiment Setup | Yes | Our experimental hyperparameters are as follows. For each experiment, we run 8 trials. We use a stepsize of 10 4. For the Gaussian smoothing, we use a perturbation scale σ of 10 1. We use a batch size per iteration of 256. To update parameters, we use the Ada Belief optimizer [Zhuang and others, 2020]. For each player s strategy network, we use a single hidden layer of size 64, the Re LU activation function, and He initialization [He and others, 2015] for initializing the network s weights. |