Linking losses for density ratio and class-probability estimation
Authors: Aditya Menon, Cheng Soon Ong
ICML 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 8. Experimental results We present experiments2 evincing three aspects of our analysis: first, that a loss weight function w DR(ρ) dictates the range of density ratio values it focusses on; second, that existing proper losses are viable for DRE in the context of covariate shift adaptation; third; that the new application of the LSIF loss to ranking the best problems holds promise. |
| Researcher Affiliation | Collaboration | Aditya Krishna Menon ADITYA.MENON@DATA61.CSIRO.AU Cheng Soon Ong CHENGSOON.ONG@ANU.EDU.AU Data61 and the Australian National University, Canberra, ACT, Australia |
| Pseudocode | No | The paper contains mathematical derivations and equations but does not include any explicit pseudocode blocks or algorithm listings. |
| Open Source Code | Yes | 2Scripts available at first author s webpage. |
| Open Datasets | Yes | The first dataset (poly) follows the example from Shimodaira (2000); Huang et al. (2007). ... The second dataset (amazon) is the real-world Amazon review data from Blitzer et al. (2007); we used the processed data as provided by Chen et al. (2012). ... We compare these losses on several standard benchmark datasets with binary labels. |
| Dataset Splits | Yes | We set n Src = 200, n Tar = 200, and n Eval = 2000. ... We train on n Src = 3000 samples from the book domain, and test on n Tar = 3000, n Eval = 2000 samples from the electronics domain. ... Each dataset was split in the ratio 2:1, with all instances normalised to lie in the ℓ2 ball. ... for each split, we performed 5-fold cross-validation to tune the strength of regularisation... |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models, memory, or specific computing environments used for running the experiments. |
| Software Dependencies | No | The paper describes various statistical methods and models but does not list any specific software dependencies or libraries with their version numbers (e.g., 'PyTorch 1.9' or 'scikit-learn 0.24'). |
| Experiment Setup | Yes | We set n Src = 200, n Tar = 200, and n Eval = 2000. ... For each loss, we find min θ Θ 1 n Src x S ℓ1( θ, Φ(x) ) + 1 n Tar x S ℓ1( θ, Φ(x ) ) + λDR 2 ||θ||2 2, where Θ is unconstrained for the standard CPE losses, and Θ = {θ | ( z S S ) θ, Φ(z) 0} otherwise. ... for λWLS = 10 6 and λDR = 10 4. ... Each dataset was split in the ratio 2:1, with all instances normalised to lie in the ℓ2 ball. A regularised linear model trained to score instances, where for each split, we performed 5-fold cross-validation to tune the strength of regularisation from λ {2 20, 2 19, . . . , 215}. |