Data-Derived Weak Universal Consistency
Authors: Narayana Santhanam, Venkatachalam Anantharam, Wojciech Szpankowski
JMLR 2022 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper we obtain a novel characterization of lossless compression problems over a countable alphabet in the data-derived framework in terms of what we term deceptive distributions. We also show that the ability to estimate the redundancy of compressing memoryless sources is equivalent to learning the underlying single-letter marginal in a data-derived fashion. We expect that the methodology underlying such characterizations in a dataderived estimation framework will be broadly applicable to a wide range of estimation problems, enabling a more systematic approach to data-derived guarantees. |
| Researcher Affiliation | Academia | Narayana Santhanam EMAIL Department of Electrical Engineering, University of Hawaii, Manoa Honolulu, HI 96822, USA Venkat Anantharam EMAIL Department of Electrical Engineering and Computer Science, University of California, Berkeley Berkeley, CA 94720, USA Wojciech Szpankowski EMAIL Department of Computer Science, Purdue University W. Lafayette, IN 47907, USA |
| Pseudocode | No | The paper describes methods and proofs using mathematical formulations and textual descriptions, but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code, nor does it provide links to code repositories or mention code in supplementary materials. |
| Open Datasets | No | The paper is theoretical and uses abstract mathematical constructs such as 'probability distributions on N' and 'countable alphabets' to illustrate concepts. It does not use or refer to any concrete datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with datasets; therefore, the concept of dataset splits is not applicable and no such information is provided. |
| Hardware Specification | No | The paper focuses on theoretical contributions and does not describe any computational experiments or implementations, so no hardware specifications are provided. |
| Software Dependencies | No | The paper is purely theoretical, presenting mathematical proofs and concepts. It does not mention any software, programming languages, or libraries, nor their versions, as part of any experimental setup. |
| Experiment Setup | No | The paper is theoretical and presents mathematical proofs and characterizations. It does not include any experimental setup details such as hyperparameters or training configurations. |