Generalization Analysis for Ranking Using Integral Operator
Authors: Yong Liu, Shizhong Liao, Hailun Lin, Yinliang Yue, Weiping Wang
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We finally conduct experiments to empirically analyze the performance of our proposed bounds. Our bound, the standard algorithmic stability bound (Agarwal and Niyogi 2009), Rademacher bound (Cl emenc on, Lugosi, and Vayatis 2005) and the state-of-the-art U-process bound (Rejchel 2012) are plotted in Figure 1 (see Section 4 in detail). The plot shows that our bound is sharper than the stability bound, Rademacher bound and Uprocess bound, which demonstrates the effectiveness of using the eigenvalues of integral operator to estimate the generalization error for ranking. |
| Researcher Affiliation | Academia | Yong Liu,1 Shizhong Liao,2 Hailun Lin,1 Yinliang Yue,1 Weiping Wang1 1Institute of Information Engineering, CAS 2School of Computer Science and Technology, Tianjin University |
| Pseudocode | No | The paper describes algorithms and theoretical concepts but does not include any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to open-source code for the described methodology. There are no links to repositories or explicit statements about code release. |
| Open Datasets | No | For all experiments, we simulate data from the true ranking model y = f (x) + ε for x [0, 1], where f (x) = x2, the noise variables ε N(0, σ2) are normally distributed with variance σ2 = 0.1, and the samples xi Uni[0, 1]. We use the the regularized ranking algorithms on Hinge loss. The regularization parameter is set to be λ = 0.01 for all experiments. Since the data is simulated, there is no public dataset with access information provided. |
| Dataset Splits | No | The paper mentions simulated datasets and varying the 'Size of data set' (n) in figures. However, it does not specify any train, validation, or test dataset splits (e.g., percentages, sample counts, or cross-validation setup) for reproducing the data partitioning. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as CPU/GPU models, memory, or cloud computing resources used for running the experiments. |
| Software Dependencies | No | The paper does not list any specific software dependencies or libraries with their version numbers, which would be necessary to replicate the experiment environment. |
| Experiment Setup | Yes | The regularization parameter is set to be λ = 0.01 for all experiments. In our bounds, we set k = log(n) for all experiments. We set γ = 1 for U-process bound and set β = 1 for our bound. |