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].
Time-uniform confidence bands for the CDF under nonstationarity
Authors: Paul Mineiro, Steven Howard
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We exhibit our techniques in various simulations in Section 4. These simulations explore the empirical behaviour of Algorithm 1 and Algorithm 2 when instantiated with ϵpdq 2d and curved boundary oracles Λ and Ξ. |
| Researcher Affiliation | Industry | Paul Mineiro Microsoft Research EMAIL Steve Howard The Voleon Group EMAIL |
| Pseudocode | Yes | Algorithm 1 Unit Interval Upper Bound. and Algorithm 2 Unit Interval Lower Bound. and Algorithm 3 Entire Real Line Upper Bound. |
| Open Source Code | Yes | Reference implementations which reproduce the figures are available at https://github.com/microsoft/csrobust. |
| Open Datasets | No | The paper uses synthetic data generated from distributions such as 'i.i.d. Beta(6, 3)', 'i.i.d. Log Normal(0, 1)', and 'i.i.d. Gaussian(0, 1)', but does not provide access information or citations for any publicly available or open dataset. |
| Dataset Splits | No | The paper describes using simulations and realizations from distributions, but it does not specify explicit training, validation, or test dataset splits. The work focuses on sequential methods where data accumulates over time. |
| Hardware Specification | No | The paper does not provide specific details regarding the hardware (e.g., GPU/CPU models, memory specifications) used to run its simulations or experiments. |
| Software Dependencies | No | The paper refers to Python implementations in its appendix (e.g., '.ipynb' files), but it does not list specific software dependencies or libraries with their version numbers required for replication. |
| Experiment Setup | Yes | For the proof we introduce an integer parameter η ě 2 which controls both the grid spacing (ϵpdq ηd) and the allocation of error probabilities to levels (δd α{pηdϵpdqq). In the main paper we set η 2. Our (synthetic) data set consists of five values... We use resolution ϵpdq 2d and coverage error α 1{20. |