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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Prospective Learning: Learning for a Dynamic Future
Authors: Ashwin De Silva, Rahul Ramesh, Rubing Yang, Siyu Yu, Joshua T Vogelstein, Pratik Chaudhari
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments illustrate that prospective ERM can learn synthetic and visual recognition problems constructed from MNIST and CIFAR-10. Code at https://github.com/neurodata/prolearn. |
| Researcher Affiliation | Academia | Ashwin De Silva ,1 Rahul Ramesh ,2 Rubing Yang ,2 Siyu Yu1 Joshua T. Vogelstein ,1 Pratik Chaudhari ,2 , Equal Contribution Email: EMAIL, EMAIL |
| Pseudocode | No | The paper describes algorithms and processes textually but does not include any explicitly labeled "Pseudocode" or "Algorithm" blocks or structured code-like formatting. |
| Open Source Code | Yes | Code at https://github.com/neurodata/prolearn. |
| Open Datasets | Yes | Numerical experiments illustrate that prospective ERM can learn synthetic and visual recognition problems constructed from MNIST [10] and CIFAR-10 [11] data. |
| Dataset Splits | No | The paper does not explicitly mention using a separate validation set for hyperparameter tuning or early stopping. It states: "Learners are trained on data from the first t time steps (z t) and prospective risk is computed using samples from the remaining time steps.", and in the NeurIPS checklist: "We have conducted extremely thorough train/test splits, and tuned hyperparameters manually across multiple runs." |
| Hardware Specification | No | The paper mentions "GPU hours" in the NeurIPS checklist (Question 8) but does not provide specific details such as GPU models, CPU models, or memory specifications used for the experiments. |
| Software Dependencies | No | The paper does not provide specific software names with version numbers for its implementation (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | Hyper-parameters All the networks are trained using stochastic gradient descent (SGD) with Nesterov s momentum and cosine-annealed learning rate. The networks are trained at a learning rate of 0.1 for the synthetic tasks, and learning rate of 0.01 for MNIST and CIFAR. The weight-decay is set to 1 10 5. The images from MNIST and CIFAR-10 are normalized to have mean 0.5 and standard deviation 0.25. The models were trained for 100 epochs, which is many epochs after achieving a training accuracy of 1. |