Learning Disentangled Textual Representations via Statistical Measures of Similarity

• URL: http://arxiv.org/abs/2205.03589v1
• Date: Sat, 7 May 2022 08:06:22 GMT
• Title: Learning Disentangled Textual Representations via Statistical Measures of Similarity
• Authors: Pierre Colombo, Guillaume Staerman, Nathan Noiry, Pablo Piantanida
• Abstract summary: We introduce a family of regularizers for learning disentangled representations that do not require training. Our novel regularizers do not require additional training, are faster and do not involve additional tuning.
• Score: 35.74568888409149
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: When working with textual data, a natural application of disentangled representations is fair classification where the goal is to make predictions without being biased (or influenced) by sensitive attributes that may be present in the data (e.g., age, gender or race). Dominant approaches to disentangle a sensitive attribute from textual representations rely on learning simultaneously a penalization term that involves either an adversarial loss (e.g., a discriminator) or an information measure (e.g., mutual information). However, these methods require the training of a deep neural network with several parameter updates for each update of the representation model. As a matter of fact, the resulting nested optimization loop is both time consuming, adding complexity to the optimization dynamic, and requires a fine hyperparameter selection (e.g., learning rates, architecture). In this work, we introduce a family of regularizers for learning disentangled representations that do not require training. These regularizers are based on statistical measures of similarity between the conditional probability distributions with respect to the sensitive attributes. Our novel regularizers do not require additional training, are faster and do not involve additional tuning while achieving better results both when combined with pretrained and randomly initialized text encoders.

Related papers

• Improving Fairness using Vision-Language Driven Image Augmentation [60.428157003498995]
  Fairness is crucial when training a deep-learning discriminative model, especially in the facial domain. Models tend to correlate specific characteristics (such as age and skin color) with unrelated attributes (downstream tasks) This paper proposes a method to mitigate these correlations to improve fairness.
  arXiv  Detail & Related papers  (2023-11-02T19:51:10Z)
• Target Variable Engineering [0.0]
  We compare the predictive performance of regression models trained to predict numeric targets vs. classifiers trained to predict their binarized counterparts. We find that regression requires significantly more computational effort to converge upon the optimal performance.
  arXiv  Detail & Related papers  (2023-10-13T23:12:21Z)
• ASPEST: Bridging the Gap Between Active Learning and Selective Prediction [56.001808843574395]
  Selective prediction aims to learn a reliable model that abstains from making predictions when uncertain. Active learning aims to lower the overall labeling effort, and hence human dependence, by querying the most informative examples. In this work, we introduce a new learning paradigm, active selective prediction, which aims to query more informative samples from the shifted target domain.
  arXiv  Detail & Related papers  (2023-04-07T23:51:07Z)
• Hyper-parameter Tuning for Fair Classification without Sensitive Attribute Access [12.447577504758485]
  We propose a framework to train fair classifiers without access to sensitive attributes on either training or validation data. We show theoretically and empirically that these proxy labels can be used to maximize fairness under average accuracy constraints.
  arXiv  Detail & Related papers  (2023-02-02T19:45:50Z)
• Invariant Representations with Stochastically Quantized Neural Networks [5.7923858184309385]
  We propose a methodology for direct computation of the mutual information between a neural layer and a sensitive attribute. We show that this method compares favorably with the state of the art in fair representation learning.
  arXiv  Detail & Related papers  (2022-08-04T13:36:06Z)
• Uncertainty Modeling for Out-of-Distribution Generalization [56.957731893992495]
  We argue that the feature statistics can be properly manipulated to improve the generalization ability of deep learning models. Common methods often consider the feature statistics as deterministic values measured from the learned features. We improve the network generalization ability by modeling the uncertainty of domain shifts with synthesized feature statistics during training.
  arXiv  Detail & Related papers  (2022-02-08T16:09:12Z)
• Learning Invariances in Neural Networks [51.20867785006147]
  We show how to parameterize a distribution over augmentations and optimize the training loss simultaneously with respect to the network parameters and augmentation parameters. We can recover the correct set and extent of invariances on image classification, regression, segmentation, and molecular property prediction from a large space of augmentations.
  arXiv  Detail & Related papers  (2020-10-22T17:18:48Z)
• Category-Learning with Context-Augmented Autoencoder [63.05016513788047]
  Finding an interpretable non-redundant representation of real-world data is one of the key problems in Machine Learning. We propose a novel method of using data augmentations when training autoencoders. We train a Variational Autoencoder in such a way, that it makes transformation outcome predictable by auxiliary network.
  arXiv  Detail & Related papers  (2020-10-10T14:04:44Z)
• Predicting What You Already Know Helps: Provable Self-Supervised Learning [60.27658820909876]
  Self-supervised representation learning solves auxiliary prediction tasks (known as pretext tasks) without requiring labeled data. We show a mechanism exploiting the statistical connections between certain em reconstruction-based pretext tasks that guarantee to learn a good representation. We prove the linear layer yields small approximation error even for complex ground truth function class.
  arXiv  Detail & Related papers  (2020-08-03T17:56:13Z)
• PushNet: Efficient and Adaptive Neural Message Passing [1.9121961872220468]
  Message passing neural networks have recently evolved into a state-of-the-art approach to representation learning on graphs. Existing methods perform synchronous message passing along all edges in multiple subsequent rounds. We consider a novel asynchronous message passing approach where information is pushed only along the most relevant edges until convergence.
  arXiv  Detail & Related papers  (2020-03-04T18:15:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.