Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Rethinking Teacher-Student Curriculum Learning through the Cooperative Mechanics of Experience
Authors: Manfred Diaz, Liam Paull, Andrea Tacchetti
TMLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through experiments covering supervised learning, reinforcement learning, and classical games, we estimate the cooperative values of experiences and use value-proportional curriculum mechanisms to construct curricula, even in cases where TSCL struggles. |
| Researcher Affiliation | Collaboration | Manfred Diaz EMAIL Mila, University of Montreal Liam Paull EMAIL Mila, University of Montreal Canada CIFAR AI Chair Andrea Tacchetti EMAIL Google Deep Mind |
| Pseudocode | Yes | Algorithm 1 Generalized Teacher-Student Curriculum Learning. |
| Open Source Code | No | Regardless, we plan to release the complete source code of all our experiments. |
| Open Datasets | Yes | For instance, in the MNIST dataset (Le Cun & Cortes, 2010) |
| Dataset Splits | No | For both MNIST and CIFAR10 (Krizhevsky, 2009), we trained a model on the complete dataset (e.g., for 200 epochs), extracted the confusion matrix on validation, and identify the top-k most confused pairs of classes. |
| Hardware Specification | Yes | All models and architectures are implemented with PYTORCH (Paszke et al., 2019), are configured using HYDRA (Yadan, 2019), and fit on a workstation equipped with a 16 GB NVIDIA RTX A4000 GPU, 32 GB of RAM, and 32 CPU cores. |
| Software Dependencies | No | All models and architectures are implemented with PYTORCH (Paszke et al., 2019), are configured using HYDRA (Yadan, 2019), and fit on a workstation equipped with a 16 GB NVIDIA RTX A4000 GPU, 32 GB of RAM, and 32 CPU cores. |
| Experiment Setup | Yes | Specification of the model architecture and hyper-parameters selection are provided in Table 2. |