How to Boost Any Loss Function
Authors: Richard Nock, Yishay Mansour
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | 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 richardnock@google.com Yishay Mansour Tel Aviv University Google Research mansour@google.com |
| 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. |