Probabilistic Invariant Learning with Randomized Linear Classifiers
Authors: Leonardo Cotta, Gal Yehuda, Assaf Schuster, Chris J. Maddison
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we empirically demonstrate the benefits of this new class of models on invariant tasks where deterministic invariant neural networks are known to struggle. |
| Researcher Affiliation | Academia | Leonardo Cotta Vector Institute leonardo.cotta@vectorinstitute.ai Gal Yehuda Technion, Haifa, Israel ygal@cs.technion.ac.il Assaf Schuster Technion, Haifa, Israel assaf@technion.ac.il Chris J. Maddison University of Toronto and Vector Institute cmaddis@cs.toronto.edu |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Source code is available at: https://github.com/cottascience/invariant-rlcs |
| Open Datasets | Yes | To address Q1 and Q2, we consider the sorting task proposed in [32]. ... We chose the task of deciding whether a graph sampled from a G(n, p) model with p = 1.1 log(n)/n is connected or not. |
| Dataset Splits | Yes | The training sets consisted of 1000 examples, while the the validation and test sets contained 100 examples. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments (e.g., GPU/CPU models, memory, or cloud instance types). |
| Software Dependencies | No | All models were trained with Pytorch [25] using Adagrad [10]. The paper mentions software names but does not specify their version numbers. |
| Experiment Setup | Yes | We tuned all the hyperparameters on the validation set using a patience of 30. The Deep Sets models found a better learning rate of 0.001 and batch size of 250. The GNN model found a better learning rate of 0.01 and batch size 100. The RSet C model used a batch size of 250 and learning rate 0.5. The RGraph C model used a batch size of 100 and a learning rate of 0.5. |