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..
Learning from Snapshots of Discrete and Continuous Data Streams
Authors: Pramith Devulapalli, Steve Hanneke
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we adopt a learning-theoretic perspective in understanding the fundamental nature of learning different classes of functions from both discrete data streams and continuous data streams. In our first framework, the update-and-deploy setting, a learning algorithm discretely queries from a process to update a predictor designed to make predictions given as input the data stream. We construct a uniform sampling algorithm that can learn with bounded error any concept class with finite Littlestone dimension. Our second framework, known as the blind-prediction setting, consists of a learning algorithm generating predictions independently of observing the process, only engaging with the process when it chooses to make queries. Interestingly, we show a stark contrast in learnability where non-trivial concept classes are unlearnable. However, we show that adaptive learning algorithms are necessary to learn sets of time-dependent and data-dependent functions, called pattern classes, in either framework. Finally, we develop a theory of pattern classes under discrete data streams for the blind-prediction setting. |
| Researcher Affiliation | Academia | Pramith Devulapalli Department of Computer Science Purdue University EMAIL Steve Hanneke Department of Computer Science Purdue University EMAIL |
| Pseudocode | Yes | Algorithm 1 Uniform Sampler(H, ) Algorithm 2 Adaptive Sampler(P) Algorithm 3 BP-SOA(P, Q) |
| Open Source Code | No | Justification: This is a completely theoretical paper so there are no experiments. Guidelines: The answer NA means that paper does not include experiments requiring code. ... Justification: This is a completely theoretical paper so we don t have any code, data, or models. |
| Open Datasets | No | Justification: This is a completely theoretical paper so there are no experiments. Guidelines: The answer NA means that the paper does not include experiments. ... Justification: This is a completely theoretical paper so we don t have any code, data, or models. |
| Dataset Splits | No | Justification: This is a completely theoretical paper so there are no experiments. Guidelines: The answer NA means that the paper does not include experiments. ... Justification: This is a completely theoretical paper so we don t have any code, data, or models. |
| Hardware Specification | No | Justification: This is a completely theoretical paper so there are no experiments. Guidelines: The answer NA means that the paper does not include experiments. ... Justification: This is a completely theoretical paper so there are no experiments. |
| Software Dependencies | No | Justification: This is a completely theoretical paper so there are no experiments. Guidelines: The answer NA means that the paper does not include experiments requiring code. ... Justification: This is a completely theoretical paper so there are no experiments. |
| Experiment Setup | No | Justification: This is a completely theoretical paper so there are no experiments. Guidelines: The answer NA means that the paper does not include experiments. |