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].
When Is Inductive Inference Possible?
Authors: Zhou Lu
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we provide a tight characterization of inductive inference by establishing a novel link to online learning theory. As our main result, we prove that inductive inference is possible if and only if the hypothesis class is a countable union of online learnable classes, potentially with an uncountable size, no matter the observations are adaptively chosen or iid sampled. |
| Researcher Affiliation | Academia | Zhou Lu Princeton University EMAIL |
| Pseudocode | Yes | Algorithm 1 Non-uniform Online Learner; Algorithm 2 Agnostic Non-uniform Online Learner; Algorithm 3 Standard Optimal Algorithm (SOA); Algorithm 4 Expert(i1, , i L); Algorithm 5 Follow the Perturber Leader (FPL) |
| Open Source Code | No | The paper does not include experiments requiring code. |
| Open Datasets | No | The paper does not include experiments. |
| Dataset Splits | No | The paper does not include experiments. |
| Hardware Specification | No | The paper does not include experiments. |
| Software Dependencies | No | The paper does not include experiments. |
| Experiment Setup | No | The paper does not include experiments. |