Efficient Conditionally Invariant Representation Learning
Authors: Roman Pogodin, Namrata Deka, Yazhe Li, Danica J. Sutherland, Victor Veitch, Arthur Gretton
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments addressing two settings: (1) synthetic data of moderate dimension...; and (2) high dimensional image data... We compare performance over all experiments with HSCIC (Quinzan et al., 2022) and GCM (Shah & Peters, 2020). |
| Researcher Affiliation | Collaboration | Roman Pogodin Gatsby Unit, UCL rmn.pogodin@gmail.com Namrata Deka UBC dnamrata@cs.ubc.ca Yazhe Li Deep Mind & Gatsby Unit, UCL yazhe@google.com Danica J. Sutherland UBC & Amii dsuth@cs.ubc.ca Victor Veitch UChicago & Google Brain victorveitch@google.com Arthur Gretton Gatsby Unit, UCL arthur.gretton@gmail.com |
| Pseudocode | Yes | Algorithm 1 Estimation of CIRCE |
| Open Source Code | Yes | Code for image data experiments is available at github.com/namratadeka/circe |
| Open Datasets | Yes | d-Sprites (Matthey et al. (2017)) which contains images of 2D shapes generated from six independent latent factors; and the Extended Yale-B Face dataset (Georghiades et al. (2001)) of faces of 28 individuals under varying camera poses and illumination. |
| Dataset Splits | Yes | We select the hyper-parameters of the optimizer and scheduler via a grid search to minimize the in-domain validation set loss. |
| Hardware Specification | No | The paper discusses the training process and various parameters but does not provide specific hardware details such as GPU/CPU models, processor types, or detailed computer specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions software components like 'Adam W (Loshchilov & Hutter (2019)) optimizer' and 'ResNet-18 (He et al., 2016) model pre-trained on ImageNet (Deng et al., 2009)' but does not provide specific version numbers for the programming language, libraries, or other ancillary software dependencies used to replicate the experiments. |
| Experiment Setup | Yes | We used Adam (Kingma & Ba, 2015) for optimization with batch size 256, and trained the network for 100 epochs. For experiments on univariate datasets, the learning rate was 1e-4 and weight decay was 0.3; for experiments on multivariate datasets, the learning rate was 3e-4 and weight decay was 0.1. |