Learning with Explanation Constraints
Authors: Rattana Pukdee, Dylan Sam, J. Zico Kolter, Maria-Florina F. Balcan, Pradeep Ravikumar
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | 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 rpukdee@cs.cmu.edu Dylan Sam Carnegie Mellon University dylansam@andrew.cmu.edu J. Zico Kolter Carnegie Mellon University Bosch Center for AI zkolter@cs.cmu.edu Maria-Florina Balcan Carnegie Mellon University ninamf@cs.cmu.edu Pradeep Ravikumar Carnegie Mellon University pkr@cs.cmu.edu |
| 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. |