Introspective Forecasting
Authors: Loizos Michael
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We show how learning can naturally resolve this conundrum. The problem is studied within a causal or temporal version of the Probably Approximately Correct semantics, extended so that a learner s predictions are first recorded in the states upon which the learned hypothesis is later applied. On the negative side, we make concrete the intuitive impossibility of predicting reliably, even under very weak assumptions. On the positive side, we identify conditions under which a generic learning schema, akin to randomized trials, supports agnostic learnability. |
| Researcher Affiliation | Academia | Loizos Michael Open University of Cyprus loizos@ouc.ac.cy |
| Pseudocode | No | The paper describes algorithmic steps within the proofs of theorems (e.g., in Theorem 1 and Theorem 3), but it does not include formally structured pseudocode blocks or algorithms labeled as such. |
| Open Source Code | No | The paper does not provide any statement or link indicating the availability of open-source code for the methodology described. |
| Open Datasets | No | The paper describes a theoretical learning model, including a 'training phase' where a learner observes abstract states and functions. It does not refer to any specific, publicly available dataset used for training, nor does it provide concrete access information for such data. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments with real-world datasets, therefore it does not specify validation dataset splits. |
| Hardware Specification | No | The paper focuses on theoretical concepts and does not describe any empirical experiments, therefore no specific hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not detail any specific software dependencies with version numbers that would be required to reproduce experimental results. |
| Experiment Setup | No | The paper focuses on theoretical aspects of introspective forecasting and does not describe any empirical experiments, thus it does not provide details on experimental setup, hyperparameters, or training configurations. |