Learning Valuation Distributions from Partial Observation
Authors: Avrim Blum, Yishay Mansour, Jame Morgenstern
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our main result in this work is to solve this problem efficiently. Namely, we derive a polynomial time algorithm (with polynomial sample complexity) that recovers an approximation b Di of each of the distributions Di, down to some price pγ, below which there is at most γ probability of any bidder winning. |
| Researcher Affiliation | Collaboration | Computer Science Department, Carnegie Mellon University avrim@cs.cmu.edu Yishay Mansour Tel Aviv University Microsoft Research, Hertzelia mansour@tau.ac.il Jamie Morgenstern Computer Science Department Carnegie Mellon University jamiemmt@cs.cmu.edu |
| Pseudocode | Yes | Algorithm Kaplan: Estimates the CDF of i from samples with reserves; Algorithm IWin: Est. P[i wins in [ℓτ, ℓτ+1]| maxj bj < ℓτ+1]; Algorithm Intervals: Partitions bid space to est. fi |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of open-source code. |
| Open Datasets | No | The paper is theoretical and does not refer to specific public datasets used for empirical training. It discusses a theoretical distribution 'P over items' and drawing 'x at random from P'. |
| Dataset Splits | No | The paper is theoretical and does not provide specific training/validation/test dataset splits. It discusses 'm samples' in a theoretical context. |
| Hardware Specification | No | The paper is theoretical and does not mention any hardware specifications used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not list any software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not provide details about an experimental setup, hyperparameters, or training settings. |