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..
Parsimonious Predictions for Strategyproof Scheduling
Authors: Richard Cole, Anupam Gupta, Pranav Jangir
NeurIPS 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We show how we can predict O(m + n) values to obtain a deterministic strategyproof algorithm whose makespan is within a constant factor of the optimal makespan when the predictions are correct, and O(n) times the optimum no matter how poor the predictions are. ... The paper ensures that each theoretical result has clearly stated assumptions and provides a formal proof. The proofs are well organized and broken up into supporting lemmas wherever required to make them more readable. Due to space limitations, we provide an intuitive proof sketch in the main body and detail the complete proof in the supplemental material. |
| Researcher Affiliation | Academia | Richard Cole Anupam Gupta Pranav Jangir Department of Computer Science New York University New York, NY 10012. |
| Pseudocode | Yes | Mechanism 1: DUAL-PREDICTOR Input: Reported values pij for all (i, j) M J, predictions bβ Rn +, map bφ : J M, and b T R+. Output: Assignment x {0, 1}n m. for each job j do Let small(j) := {i | pij b T}. if small(j) = then φ(j) arg mini pij. else φ(j) arg mini{bβi pij | i small(j)}. // breaking ties in favor of bφ(j). return the vector x corresponding to this allocation φ. |
| Open Source Code | No | The paper does not include experiments requiring code. |
| Open Datasets | No | The paper does not include experiments. |
| Dataset Splits | No | The paper does not include experiments. |
| Hardware Specification | No | The paper does not include experiments. |
| Software Dependencies | No | The paper does not include experiments. |
| Experiment Setup | No | The paper does not include experiments. |