Learning the Valuations of a $k$-demand Agent
Authors: Hanrui Zhang, Vincent Conitzer
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Section 4, we experimentally evaluate the performance of ERM algorithms. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Duke University, Durham, USA. Correspondence to: Hanrui Zhang <hrzhang@cs.duke.edu>, Vincent Conitzer <conitzer@cs.duke.edu>. |
| Pseudocode | Yes | Algorithm 1 Biased Binary Search |
| Open Source Code | No | The paper does not contain any statement about making its source code publicly available, nor does it provide a link to a code repository. |
| Open Datasets | No | We draw the ground truth value vector uniformly at random from the unit hypercube [0, 1]n, and for each sample, we draw the price vector uniformly at random from [−1, 0]n. The paper generates its own data for experiments and does not use a pre-existing public dataset with concrete access information. |
| Dataset Splits | No | The paper mentions training and testing but does not explicitly describe a validation dataset split. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using an 'LP solver' but does not provide specific version numbers for it or any other software dependencies. |
| Experiment Setup | Yes | We implement the ERM learner by solving the system in Proposition 2 using an LP solver, where the objective is to maximize the minimum margin. We draw the ground truth value vector uniformly at random from the unit hypercube [0, 1]n, and for each sample, we draw the price vector uniformly at random from [−1, 0]n. To study the performance of ERM for different values of k, we fix the number of items to be n = 50, and examine the accuracy of the ERM learner for k ∈ {5, 10, 15, 20, 25} respectively. To study the performance of ERM for different values of n, we fix the agent to be unit-demand (i.e., k = 1), and calculate the accuracy of the ERM learner for n ∈ {20, 40, 60, 80, 100} respectively. In both experiments, we let the size of the training set grow, and plot the empirical error rate for each size of the training set ℓ ∈ {50, 100, 150, 200, 250, 300, 350, 400, 450, 500}. |