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].
Risk-Sensitive and Robust Decision-Making: a CVaR Optimization Approach
Authors: Yinlam Chow, Aviv Tamar, Shie Mannor, Marco Pavone
NeurIPS 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we present results from numerical experiments that corroborate our theoretical findings and show the practicality of our approach. |
| Researcher Affiliation | Academia | Yinlam Chow Stanford University EMAIL Aviv Tamar UC Berkeley EMAIL Shie Mannor Technion EMAIL Marco Pavone Stanford University EMAIL |
| Pseudocode | Yes | Algorithm 1 CVa R Value Iteration with Linear Interpolation |
| Open Source Code | Yes | The Matlab code used for the experiments is provided in the supplementary material. |
| Open Datasets | No | The paper describes a custom-generated 'grid-world simulation' for its experiments, rather than using a publicly available or open dataset with concrete access information. |
| Dataset Splits | No | The paper describes a simulation environment and evaluation procedure ('trained... on the nominal... evaluated them on 400 perturbed scenarios') but does not provide specific dataset split information (percentages, sample counts, or citations to predefined splits) in the context of training, validation, or test sets. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'Matlab code' and 'CPLEX linear programming solver' but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | For our experiments, we choose a 64 53 grid-world (see Figure 1), for a total of 3,312 states... We choose δ = 0.05, and a discount factor γ = 0.95... we set the penalty cost equal to M = 2/(1 γ)... we set ϵ = 0.1 and θ = 2.067 |