Regularized Learning for Domain Adaptation under Label Shifts
Authors: Kamyar Azizzadenesheli, Anqi Liu, Fanny Yang, Animashree Anandkumar
ICLR 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on the CIFAR-10 and MNIST datasets show that RLLS improves classification accuracy, especially in the low sample and large-shift regimes, compared to previous methods. |
| Researcher Affiliation | Academia | Kamyar Azizzadenesheli University of California, Irvine kazizzad@uci.edu Anqi Liu California Institute of Technology anqiliu@caltech.edu Fanny Yang Institute of Theoretical Studies, ETH Zürich fan.yang@stat.math.ethz.ch Animashree Anandkumar California Institute of Technology anima@caltech.edu |
| Pseudocode | Yes | Algorithm 1 Regularized Learning of Label Shift (RLLS) |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | Experiments on the CIFAR-10 and MNIST datasets... MNIST (Le Cun & Cortes, 2010) and CIFAR10 (Krizhevsky & Hinton, 2009) datasets. |
| Dataset Splits | No | The paper describes dividing the source data into subsets for weight estimation and classifier training (e.g., "divide it into two sets where we use (1 β)np samples in set Dweight p to compute the estimate bw and the remaining n = βnp in the set Dclass p to find the classifier"). It also mentions that a hyperparameter 'can be chosen using standard cross validation methods'. However, it does not explicitly specify a conventional train/validation/test dataset split percentages or sample counts used for model selection or hyperparameter tuning in their reported experiments. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions using a 'two-layer fully connected neural network for MNIST and a Res Net-18 (He et al., 2016) for CIFAR10,' but does not specify any software dependencies or their version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | In order to compute bω = 1+bθ in Eq. (3), we call a built-in solver to directly solve the low dimensional problem minθ b Cθ bb 2 + C θ 2 where we empirically observer that 0.01 times of the true C yields in a better estimator on various levels of label shift pre-computed beforehand. It is worth noting that 0.001 makes the theoretical bound in Lemma. 1 O(1/0.01) times bigger. We thus treat it as a hyperparameter that can be chosen using standard cross validation methods. Finally, we train a classifier on the source samples weighted by bω, where we use a two-layer fully connected neural network for MNIST and a Res Net-18 (He et al., 2016) for CIFAR10. |