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..
Conformal Inference for Online Prediction with Arbitrary Distribution Shifts
Authors: Isaac Gibbs, Emmanuel J. Candès
JMLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test our techniques on two real-world datasets aimed at predicting stock market volatility and COVID-19 case counts and find that they are robust and adaptive to real-world distribution shifts. Keywords: Conformal inference, online prediction, distribution shift, prediction set, online convex optimization |
| Researcher Affiliation | Academia | Isaac Gibbs EMAIL Department of Statistics Stanford University Stanford, CA 94305, USA Emmanuel Cand es EMAIL Departments of Statistics and Mathematics Stanford University Stanford, CA 94305, USA |
| Pseudocode | Yes | Algorithm 1: Dt ACI, modified version of Algorithm 1 in Gradu et al. (2023). ... Algorithm 2: |
| Open Source Code | Yes | Code for reproducing these results can be found at https://github.com/isgibbs/Dt ACI. |
| Open Datasets | Yes | All data is obtained from a public repository made available by the DELPHI group at Carnegie Mellon (Reinhart et al. (2021)). |
| Dataset Splits | Yes | For our first real-world data example, we return to a stock market prediction task... At each time step, t, we use the most recent 1250 days of returns {Rs}t 1250 s<t to produce estimates... To compute prediction sets for county i, we define the conformity scores St,i := |d COt,i COt,i|/|COt 7,i COt,i| and counts nt := |{i : County i has available data at time t 1}|, and set ˆCt,i(αt,i) := c : |d COt,i c| |COt 7,i c| Quantile. |
| Hardware Specification | No | No specific hardware details (like GPU/CPU models, memory, or cloud instances) are mentioned in the paper. |
| Software Dependencies | No | The paper does not provide specific version numbers for software libraries, frameworks, or programming languages used in the experiments. |
| Experiment Setup | Yes | In all experiments the set of candidate γ values is taken to be {0.001, 0.002, 0.004, 0.008, 0.0160, 0.032, 0.064, 0.128}. ... In our experiments, we will set η and σ using the choice |I| = 500. ... The hyperparameters to be m = 40, η = q 4.2m , and r = 800000. ... We model the stock returns Rt := Pt Pt 1 Pt 1 as coming from a GARCH(1,1) design. ... using least-squares regression to fit the model COs,i βt 0 + j=1 λt j COs 7j,i + j=1 κt j Fs 7j,i, s = t 14 . . . , t, i = 1, . . . , 3243. |