Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Learning Valuation Distributions from Partial Observation
Authors: Avrim Blum, Yishay Mansour, Jame Morgenstern
AAAI 2015 | Venue PDF | 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 EMAIL Yishay Mansour Tel Aviv University Microsoft Research, Hertzelia EMAIL Jamie Morgenstern Computer Science Department Carnegie Mellon University EMAIL |
| 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. |