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].
Multi-Objective Non-parametric Sequential Prediction
Authors: Guy Uziel, Ran El-Yaniv
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we extend the multi-objective framework to the case of stationary and ergodic processes, thus allowing dependencies among observations. We first identify an asymptomatic lower bound for any prediction strategy and then present an algorithm whose predictions achieve the optimal solution while fulfilling any continuous and convex constraining criterion. |
| Researcher Affiliation | Academia | Guy Uziel Computer Science Department Technion Israel Institute of Technology EMAIL Ran El-Yaniv Computer Science Department Technion Israel Institute of Technology EMAIL |
| Pseudocode | Yes | Algorithm 1 Minimax Histogram Based Aggregation (MHA) |
| Open Source Code | No | The paper does not provide any statements or links indicating that open-source code for the methodology is available. |
| Open Datasets | No | The paper is theoretical and does not use or refer to any specific publicly available datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve experimental data splits for training, validation, or testing. |
| Hardware Specification | No | The paper describes theoretical work and does not mention any hardware specifications used for experiments. |
| Software Dependencies | No | The paper describes theoretical work and does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not include details about an experimental setup, such as hyperparameters or system-level training settings. |