Easy Learning from Label Proportions
Authors: Róbert Busa-Fekete, Heejin Choi, Travis Dick, Claudio Gentile, Andres Munoz Medina
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section we empirically evaluate EASYLLP, PROPMATCH, and two baseline methods to characterize how their performance depends on the bag size for a range of different learning tasks and underlying learning models. ... Results. Figure 2 depicts the accuracy achieved by each method on a selection of datasets and models for a range of bag sizes. |
| Researcher Affiliation | Industry | Robert Busa-Fekete Google Research busarobi@google.com; Heejin Choi Coupang Inc hechoi53@coupang.com; Travis Dick Google Research tdick@google.com; Claudio Gentile Google Research cgentilek@google.com; Andres Munoz Medina Google Research ammmedina@google.com |
| Pseudocode | Yes | We study a version of projected SGD that picks one example per bag and uses the soft-label corrected gradient estimates (pseudocode is given in in Algorithm 1 in the Appendix). |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-source code of their methodology. |
| Open Datasets | Yes | We carry out experiments on four (binary classification) datasets: Binarized versions of MNIST [13] and CIFAR-10 [12], as well as the Higgs [3] and UCI adult datasets [11]. |
| Dataset Splits | No | The paper mentions tuning learning rates and hyperparameters, which implies the use of a validation set, but does not explicitly provide specific details about training/test/validation dataset splits (e.g., percentages or sample counts) for reproducibility. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU or CPU models, memory specifications) used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'Adam [10] optimizer' and refers to frameworks like 'Tensorflow, JAX and Py Torch' but does not specify exact version numbers for any software dependencies. |
| Experiment Setup | Yes | To tune the learning rate for each method, we report the highest accuracy achieved for learning rates in {0.00001, 0.0001, 0.0005, 0.001, 0.005, 0.01, 0.05}. ... In all cases we use the Adam [10] optimizer, binary crossentropy loss, minibatches of size 512, and 20 training passes through the data. Finally, for the two image datasets, we decay the learning rate after 40%, 60%, 80%, and 90% of the training passes by factors 10, 100, 1000, and 5000, respectively. |