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].
On Characterizing the Trade-off in Invariant Representation Learning
Authors: Bashir Sadeghi, Sepehr Dehdashtian, Vishnu Boddeti
TMLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also numerically quantify the trade-off on representative problems and compare them to those achieved by baseline IRep L algorithms. Code is available at https://github. com/human-analysis/tradeoff-invariant-representation-learning. [...] In this section, we numerically quantify our K TOpt through the closed-form solution for the encoder obtained in Section 5 on an illustrative toy example and two real-world datasets, Folktables and Celeb A. |
| Researcher Affiliation | Academia | Bashir Sadeghi EMAIL Sepehr Dehdashtian EMAIL Vishnu Naresh Boddeti EMAIL Department of Computer Science and Engineering Michigan State University |
| Pseudocode | No | The paper describes mathematical derivations and theoretical framework for the optimization problem and its solution, but does not present any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github. com/human-analysis/tradeoff-invariant-representation-learning. |
| Open Datasets | Yes | We numerically quantify our K TOpt through the closed-form solution for the encoder obtained in Section 5 on an illustrative toy example and two real-world datasets, Folktables (Ding et al., 2021) and Celeb A (Liu et al., 2015). |
| Dataset Splits | Yes | We sample 18, 000 instances from p X,Y,S independently and split these samples equally into training, validation, and testing partitions. [...] We randomly split the data into training (70%), validation (15%), and testing (15%) partitions. [...] Celeb A dataset (Liu et al., 2015) contains 202, 599 face images of 10, 177 different celebrities with standard training, validation, and testing splits. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (GPU, CPU, memory, etc.) used for running the experiments. |
| Software Dependencies | No | The paper mentions using MLPs, AdamW optimizer, and stochastic gradient descent, but it does not specify any software versions (e.g., Python, PyTorch, TensorFlow, or specific library versions) for these components. |
| Experiment Setup | Yes | For all methods, we pick different values of λ (100 λs for the Gaussian toy example and 70 λs for Folktables and Celeb A datasets) between zero and one for obtaining the utility-invariance trade-off. [...] We optimize the regularization parameter γ in the disentanglement set (10) by minimizing the corresponding target losses over γs in {10 6, 10 5, 10 4, 10 3, 10 2, 10 1, 1} on validation sets. [...] The final RFF dimensionality is 100 for the Gaussian dataset, 5000 for the Folktables dataset, and 1000 for the Celeb A dataset. [...] We use a batch size of 500 for Gaussian data; and 128 for Folktables and Celeb A. Then, the corresponding learning rates are optimized over 10 2, 10 3, 5 10 4, 10 4, 10 5 by minimizing the target loss on the corresponding validation sets. |