Sequential Domain Adaptation by Synthesizing Distributionally Robust Experts
Authors: Bahar Taskesen, Man-Chung Yue, Jose Blanchet, Daniel Kuhn, Viet Anh Nguyen
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments on real data show that the robust strategies may outperform non-robust interpolations of the empirical least squares estimators. and 6. Numerical Experiments and Table 1 shows the average cumulative loss of each aggregated expert obtained by the BOA algorithm for all datasets and for J = {5, 10, 50, 100} across 100 independent runs. |
| Researcher Affiliation | Collaboration | Bahar Taskesen 1 Man-Chung Yue 2 Jos e Blanchet 3 Daniel Kuhn 1 Viet Anh Nguyen 3 4 1Risk Analytics and Optimization Chair, Ecole Polytechnique F ed erale de Lausanne 2Department of Applied Mathematics, The Hong Kong Polytechnic University 3Department of Management Science and Engineering, Stanford University 4Vin AI Research, Vietnam. |
| Pseudocode | No | The paper describes algorithms in text (e.g., Bernstein Online Aggregation) but does not provide structured pseudocode blocks or algorithm listings. |
| Open Source Code | Yes | The corresponding codes are available at https: //github.com/RAO-EPFL/DR-DA.git. |
| Open Datasets | Yes | We compare the performance of our model against the above non-robust benchmarks on 5 Kaggle datasets:3 and footnote 3 Descriptions and download links are provided in the appendix. |
| Dataset Splits | No | We use all samples from the source domain for training, and we form the target training set by drawing NT =d samples from the target dataset. Later, we randomly sample J = 1000 data points from the remaining target samples to form the sequentially arriving target test samples. No explicit validation split is mentioned. |
| Hardware Specification | Yes | All experiments are run on an Intel i7-8700 CPU (3.2 GHz) computer with 16GB RAM. |
| Software Dependencies | Yes | The second-order cone and semidefinite programs are modelled in MATLAB via YALMIP (L ofberg, 2004) and solved with MOSEK Ap S (2019). |
| Experiment Setup | Yes | We set the regularization parameter of the ridge regression problem to η = 10 6 and the learning rate of the BOA algorithm to υ = 0.5. |