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..
Adaptive Conformal Inference Under Distribution Shift
Authors: Isaac Gibbs, Emmanuel Candes
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test our method, adaptive conformal inference, on two real world datasets and find that its predictions are robust to visible and significant distribution shifts. |
| Researcher Affiliation | Academia | Isaac Gibbs Department of Statistics Stanford University EMAIL Emmanuel J. Candès Department of Statistics Department of Mathematics Stanford University EMAIL |
| Pseudocode | No | The paper describes algorithmic steps (e.g., 't+1 := t + γ( errt)') but does not present them within a clearly labeled 'Algorithm' or 'Pseudocode' block. |
| Open Source Code | No | The paper does not include any statement about releasing source code or provide links to a code repository for the described methodology. |
| Open Datasets | Yes | Daily open prices were obtained from publicly available datasets published by The Wall Street Journal. |
| Dataset Splits | No | The paper mentions a 'calibration set Dcal' and dynamic data usage (e.g., 'fit the model using only the last 1250 trading days') but does not specify fixed train/validation/test splits with percentages or sample counts. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions statistical models and methods (e.g., 'GARCH(1,1) model', 'conformalized quantile regression (CQR)') but does not list specific software packages or libraries with version numbers used for implementation. |
| Experiment Setup | Yes | More precisely, for all times t > 1250 we fit the coefficients ˆ!t, ˆ t, ˆβt as well as the sequence of variances {ˆσts}1 s t 1 using only the data {Rr}t 1250 r<t. |