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..
How to Boost Any Loss Function
Authors: Richard Nock, Yishay Mansour
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our paper is a theory paper: all claims are properly formalized and used. |
| Researcher Affiliation | Collaboration | Richard Nock Google Research EMAIL Yishay Mansour Tel Aviv University Google Research EMAIL |
| Pseudocode | Yes | Algorithm 1 SECBOOST(S, T) ... Algorithm 2 SOLVEα(S, w, h) ... Algorithm 3 SOLVE_extended(S, w, h, M) ... Algorithm 4 OO_simple(F, et, et 1, z, Z) |
| Open Source Code | No | Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material? Answer: [No] Justification: Our paper is a theory paper. All algorithms we introduce are either in the main file or the appendix. |
| Open Datasets | Yes | We provide an experiment on public domain UCI tictactoe [23] (using a 10-fold stratified crossvalidation to estimate test errors). |
| Dataset Splits | Yes | We provide an experiment on public domain UCI tictactoe [23] (using a 10-fold stratified crossvalidation to estimate test errors). |
| Hardware Specification | No | For each experiment, does the paper provide sufficient information on the computer resources (type of compute workers, memory, time of execution) needed to reproduce the experiments? Answer: [NA] . Justification: Our paper is a theory paper. |
| Software Dependencies | No | Does the paper provide SPECIFIC ANCILLARY SOFTWARE DETAILS (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment? Answer: [NA] Justification: Our paper is a theory paper. |
| Experiment Setup | Yes | the size of the trees (either they have a single internal node = stumps or at most 20 nodes) and, to give one example of how changing a (key) hyperparameter can change the result, we have tested for a scale of changes on the initial value of δ in (60). ... δ 0.1 δ 1.0 ... We flip each label in the training sample with probability η. |