Ensemble Methods for Structured Prediction
Authors: Corinna Cortes, Vitaly Kuznetsov, Mehryar Mohri
ICML 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also report the results of extensive experiments with these algorithms in several structured prediction tasks. |
| Researcher Affiliation | Collaboration | Corinna Cortes CORINNA@GOOGLE.COM Google Research, 111 8th Avenue, New York, NY 10011 Vitaly Kuznetsov VITALY@CIMS.NYU.EDU Courant Institute of Mathematical Sciences, 251 Mercer Street, New York, NY 10012 Mehryar Mohri MOHRI@CIMS.NYU.EDU Courant Institute and Google Research, 251 Mercer Street, New York, NY 10012 |
| Pseudocode | Yes | Algorithm 1 WMWP algorithm. Inputs: sample {(x1, y1), . . . , (x T , y T )}; set of experts {h1, . . . , hp}; parameter β (0, 1). for j = 1 to p and k = 1 to l do... Algorithm 2 ESPBoost Algorithm. Inputs: S = ((x1, y1), . . . , (xm, ym)); set of experts {h1, . . . , hp}. for i = 1 to m and k = 1 to l do... |
| Open Source Code | No | The paper mentions and links to several third-party software packages used (e.g., CRFsuite, SVMstruct, Stanford Classifier), but it does not state that the authors' own code for the described methodology is open-source or provide a link to it. |
| Open Datasets | Yes | Rob Kassel s OCR data set is available for download from http://ai.stanford.edu/ btaskar/ocr/.; The Penn Treebank 2 data set is available through LDC license at http://www.cis.upenn.edu/ treebank/ and contains 251,854 sentences with a total of 6,080,493 tokens and 45 different parts of speech. |
| Dataset Splits | Yes | For each data set, we performed 10-fold cross-validation with disjoint training sets. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory, or cloud computing instances) used for running the experiments. |
| Software Dependencies | No | The paper mentions several software packages used (e.g., 'CRFsuite', 'SVMstruct', 'Stanford Classifier') with citations, but it does not specify their version numbers. |
| Experiment Setup | Yes | More details on the data set and the experimental parameters can be found in Appendix H.1. Table 1. Average Normalized Hamming Loss, ADS1 and ADS2. βADS1 = 0.95, βADS2 = 0.95, TSLE = 100, δ = 0.05. Table 2. Average Normalized Hamming Loss, PDS1 and PDS2. βPDS1 = 0.85, βPDS2 = 0.97, TSLE = 100, δ = 0.05. Table 3. Average Normalized Hamming Loss, TR1 and TR2. βTR1 = 0.95, βTR2 = 0.98, TSLE = 100, δ = 0.05. |