Adaptive Conformal Inference Under Distribution Shift
Authors: Isaac Gibbs, Emmanuel Candes
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | 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 igibbs@stanford.edu Emmanuel J. Candès Department of Statistics Department of Mathematics Stanford University candes@stanford.edu |
| 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. |