CF-OPT: Counterfactual Explanations for Structured Prediction

• URL: http://arxiv.org/abs/2405.18293v2
• Date: Mon, 3 Jun 2024 15:07:01 GMT
• Title: CF-OPT: Counterfactual Explanations for Structured Prediction
• Authors: Germain Vivier-Ardisson, Alexandre Forel, Axel Parmentier, Thibaut Vidal,
• Abstract summary: optimization layers in deep neural networks have enjoyed a growing popularity in structured learning, improving the state of the art on a variety of applications. Yet, these pipelines lack interpretability since they are made of two opaque layers: a highly non-linear prediction model, such as a deep neural network, and an optimization layer, which is typically a complex black-box solver. Our goal is to improve the transparency of such methods by providing counterfactual explanations.
• Score: 47.36059095502583
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Optimization layers in deep neural networks have enjoyed a growing popularity in structured learning, improving the state of the art on a variety of applications. Yet, these pipelines lack interpretability since they are made of two opaque layers: a highly non-linear prediction model, such as a deep neural network, and an optimization layer, which is typically a complex black-box solver. Our goal is to improve the transparency of such methods by providing counterfactual explanations. We build upon variational autoencoders a principled way of obtaining counterfactuals: working in the latent space leads to a natural notion of plausibility of explanations. We finally introduce a variant of the classic loss for VAE training that improves their performance in our specific structured context. These provide the foundations of CF-OPT, a first-order optimization algorithm that can find counterfactual explanations for a broad class of structured learning architectures. Our numerical results show that both close and plausible explanations can be obtained for problems from the recent literature.

Related papers

• Training morphological neural networks with gradient descent: some theoretical insights [0.40792653193642503]
  We investigate the potential and limitations of differentiation based approaches and back-propagation applied to morphological networks. We provide insights and first theoretical guidelines, in particular regarding learning rates.
  arXiv  Detail & Related papers  (2024-02-05T12:11:15Z)
• Operator Learning Meets Numerical Analysis: Improving Neural Networks through Iterative Methods [2.226971382808806]
  We develop a theoretical framework grounded in iterative methods for operator equations. We demonstrate that popular architectures, such as diffusion models and AlphaFold, inherently employ iterative operator learning. Our work aims to enhance the understanding of deep learning by merging insights from numerical analysis.
  arXiv  Detail & Related papers  (2023-10-02T20:25:36Z)
• Iterative Soft Shrinkage Learning for Efficient Image Super-Resolution [91.3781512926942]
  Image super-resolution (SR) has witnessed extensive neural network designs from CNN to transformer architectures. This work investigates the potential of network pruning for super-resolution iteration to take advantage of off-the-shelf network designs and reduce the underlying computational overhead. We propose a novel Iterative Soft Shrinkage-Percentage (ISS-P) method by optimizing the sparse structure of a randomly network at each and tweaking unimportant weights with a small amount proportional to the magnitude scale on-the-fly.
  arXiv  Detail & Related papers  (2023-03-16T21:06:13Z)
• Initialization and Regularization of Factorized Neural Layers [23.875225732697142]
  We show how to initialize and regularize Factorized layers in deep nets. We show how these schemes lead to improved performance on both translation and unsupervised pre-training.
  arXiv  Detail & Related papers  (2021-05-03T17:28:07Z)
• Reframing Neural Networks: Deep Structure in Overcomplete Representations [41.84502123663809]
  We introduce deep frame approximation, a unifying framework for representation learning with structured overcomplete frames. We quantify structural differences with the deep frame potential, a data-independent measure of coherence linked to representation uniqueness and stability. This connection to the established theory of overcomplete representations suggests promising new directions for principled deep network architecture design.
  arXiv  Detail & Related papers  (2021-03-10T01:15:14Z)
• Dual-constrained Deep Semi-Supervised Coupled Factorization Network with Enriched Prior [80.5637175255349]
  We propose a new enriched prior based Dual-constrained Deep Semi-Supervised Coupled Factorization Network, called DS2CF-Net. To ex-tract hidden deep features, DS2CF-Net is modeled as a deep-structure and geometrical structure-constrained neural network. Our network can obtain state-of-the-art performance for representation learning and clustering.
  arXiv  Detail & Related papers  (2020-09-08T13:10:21Z)
• A Differential Game Theoretic Neural Optimizer for Training Residual Networks [29.82841891919951]
  We propose a generalized Differential Dynamic Programming (DDP) neural architecture that accepts both residual connections and convolution layers. The resulting optimal control representation admits a gameoretic perspective, in which training residual networks can be interpreted as cooperative trajectory optimization on state-augmented systems.
  arXiv  Detail & Related papers  (2020-07-17T10:19:17Z)
• A Flexible Framework for Designing Trainable Priors with Adaptive Smoothing and Game Encoding [57.1077544780653]
  We introduce a general framework for designing and training neural network layers whose forward passes can be interpreted as solving non-smooth convex optimization problems. We focus on convex games, solved by local agents represented by the nodes of a graph and interacting through regularization functions. This approach is appealing for solving imaging problems, as it allows the use of classical image priors within deep models that are trainable end to end.
  arXiv  Detail & Related papers  (2020-06-26T08:34:54Z)
• Dynamic Hierarchical Mimicking Towards Consistent Optimization Objectives [73.15276998621582]
  We propose a generic feature learning mechanism to advance CNN training with enhanced generalization ability. Partially inspired by DSN, we fork delicately designed side branches from the intermediate layers of a given neural network. Experiments on both category and instance recognition tasks demonstrate the substantial improvements of our proposed method.
  arXiv  Detail & Related papers  (2020-03-24T09:56:13Z)
• MSE-Optimal Neural Network Initialization via Layer Fusion [68.72356718879428]
  Deep neural networks achieve state-of-the-art performance for a range of classification and inference tasks. The use of gradient combined nonvolutionity renders learning susceptible to novel problems. We propose fusing neighboring layers of deeper networks that are trained with random variables.
  arXiv  Detail & Related papers  (2020-01-28T18:25:15Z)

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.