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 [1].
Learning Optimal Commitment to Overcome Insecurity
Authors: Avrim Blum, Nika Haghtalab, Ariel D Procaccia
NeurIPS 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We design an algorithm that learns an (additively) ϵ-optimal strategy for the defender with probability 1 δ, by asking a number of queries that is polynomial in the representation of the security game, and logarithmic in 1/ϵ and 1/δ. |
| Researcher Affiliation | Academia | Avrim Blum Carnegie Mellon University EMAIL Nika Haghtalab Carnegie Mellon University EMAIL Ariel D. Procaccia Carnegie Mellon University EMAIL |
| Pseudocode | Yes | Algorithm 1 LATTICE-ROUNDING (approximately optimal strategy p) Algorithm 2 OPTIMIZE (accuracy ϵ, confidence δ) |
| Open Source Code | No | The paper does not provide any links to open-source code or explicitly state that code for the described methodology is available. |
| Open Datasets | No | The paper is theoretical and focuses on algorithm design and complexity analysis in a game-theoretic context. It does not use or refer to any publicly available or open datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with dataset splits (e.g., training, validation, test splits). |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for computations or experiments. |
| Software Dependencies | No | The paper is theoretical and focuses on algorithms and proofs. It does not mention any specific software dependencies or their version numbers (e.g., programming languages, libraries, solvers) that would be needed for implementation. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details, hyperparameters, or system-level training settings. |