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..
Learning with Explanation Constraints
Authors: Rattana Pukdee, Dylan Sam, J. Zico Kolter, Maria-Florina F. Balcan, Pradeep Ravikumar
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the benefits of our approach over a large array of synthetic and real-world experiments. |
| Researcher Affiliation | Collaboration | Rattana Pukdee Carnegie Mellon University EMAIL Dylan Sam Carnegie Mellon University EMAIL J. Zico Kolter Carnegie Mellon University Bosch Center for AI EMAIL Maria-Florina Balcan Carnegie Mellon University EMAIL Pradeep Ravikumar Carnegie Mellon University EMAIL |
| Pseudocode | Yes | Algorithm 1 Algorithm for identifying parameters of a two layer neural network, given exact gradient constraints |
| Open Source Code | No | code to replicate our experiments will be released with the full paper. |
| Open Datasets | Yes | We present classification tasks (Figure 5) from a weak supervision benchmark [46]. [46] J. Zhang, Y. Yu, Y. Li, Y. Wang, Y. Yang, M. Yang, and A. Ratner. Wrench: A comprehensive benchmark for weak supervision. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2), 2021. |
| Dataset Splits | No | The paper mentions using 'Test splits' from the WRENCH benchmark but does not specify the exact percentages, sample counts, or explicit methodology for these splits within the paper itself. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | Yes | For all of our synthetic and real-world experiments, we use values of m = 1000, k = 20, T = 3, τ = 0, λ = 1, unless otherwise noted. For our synthetic experiments, we use d = 100, σ2 = 5. Our two layer neural networks have hidden dimensions of size 10. They are trained with a learning rate of 0.01 for 50 epochs. For our real-world data, our two layer neural networks have a hidden dimension of size 10 and are trained with a learning rate of 0.1 (You Tube) and 0.1 (Yelp) for 10 epochs. λ = 0.01 and gradient values computed by the smoothed approximation in [? ] has c = 1. |