CounterNet: End-to-End Training of Prediction Aware Counterfactual Explanations

• URL: http://arxiv.org/abs/2109.07557v3
• Date: Thu, 22 Jun 2023 09:17:44 GMT
• Title: CounterNet: End-to-End Training of Prediction Aware Counterfactual Explanations
• Authors: Hangzhi Guo, Thanh Hong Nguyen, Amulya Yadav
• Abstract summary: CounterNet is an end-to-end learning framework which integrates predictive model training and the generation of counterfactual (CF) explanations. Unlike post-hoc methods, CounterNet enables the optimization of the CF explanation generation only once together with the predictive model. Our experiments on multiple real-world datasets show that CounterNet generates high-quality predictions.
• Score: 12.313007847721215
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work presents CounterNet, a novel end-to-end learning framework which integrates Machine Learning (ML) model training and the generation of corresponding counterfactual (CF) explanations into a single end-to-end pipeline. Counterfactual explanations offer a contrastive case, i.e., they attempt to find the smallest modification to the feature values of an instance that changes the prediction of the ML model on that instance to a predefined output. Prior techniques for generating CF explanations suffer from two major limitations: (i) all of them are post-hoc methods designed for use with proprietary ML models -- as a result, their procedure for generating CF explanations is uninformed by the training of the ML model, which leads to misalignment between model predictions and explanations; and (ii) most of them rely on solving separate time-intensive optimization problems to find CF explanations for each input data point (which negatively impacts their runtime). This work makes a novel departure from the prevalent post-hoc paradigm (of generating CF explanations) by presenting CounterNet, an end-to-end learning framework which integrates predictive model training and the generation of counterfactual (CF) explanations into a single pipeline. Unlike post-hoc methods, CounterNet enables the optimization of the CF explanation generation only once together with the predictive model. We adopt a block-wise coordinate descent procedure which helps in effectively training CounterNet's network. Our extensive experiments on multiple real-world datasets show that CounterNet generates high-quality predictions, and consistently achieves 100% CF validity and low proximity scores (thereby achieving a well-balanced cost-invalidity trade-off) for any new input instance, and runs 3X faster than existing state-of-the-art baselines.

Related papers

• Unsupervised Pre-training with Language-Vision Prompts for Low-Data Instance Segmentation [105.23631749213729]
  We propose a novel method for unsupervised pre-training in low-data regimes. Inspired by the recently successful prompting technique, we introduce a new method, Unsupervised Pre-training with Language-Vision Prompts. We show that our method can converge faster and perform better than CNN-based models in low-data regimes.
  arXiv  Detail & Related papers  (2024-05-22T06:48:43Z)
• Faithful Explanations of Black-box NLP Models Using LLM-generated Counterfactuals [67.64770842323966]
  Causal explanations of predictions of NLP systems are essential to ensure safety and establish trust. Existing methods often fall short of explaining model predictions effectively or efficiently. We propose two approaches for counterfactual (CF) approximation.
  arXiv  Detail & Related papers  (2023-10-01T07:31:04Z)
• Boosting Low-Data Instance Segmentation by Unsupervised Pre-training with Saliency Prompt [103.58323875748427]
  This work offers a novel unsupervised pre-training solution for low-data regimes. Inspired by the recent success of the Prompting technique, we introduce a new pre-training method that boosts QEIS models. Experimental results show that our method significantly boosts several QEIS models on three datasets.
  arXiv  Detail & Related papers  (2023-02-02T15:49:03Z)
• VCNet: A self-explaining model for realistic counterfactual generation [52.77024349608834]
  Counterfactual explanation is a class of methods to make local explanations of machine learning decisions. We present VCNet-Variational Counter Net, a model architecture that combines a predictor and a counterfactual generator. We show that VCNet is able to both generate predictions, and to generate counterfactual explanations without having to solve another minimisation problem.
  arXiv  Detail & Related papers  (2022-12-21T08:45:32Z)
• Harnessing the Power of Explanations for Incremental Training: A LIME-Based Approach [6.244905619201076]
  In this work, model explanations are fed back to the feed-forward training to help the model generalize better. The framework incorporates the custom weighted loss with Elastic Weight Consolidation (EWC) to maintain performance in sequential testing sets. The proposed custom training procedure results in a consistent enhancement of accuracy ranging from 0.5% to 1.5% throughout all phases of the incremental learning setup.
  arXiv  Detail & Related papers  (2022-11-02T18:16:17Z)
• RoCourseNet: Distributionally Robust Training of a Prediction Aware Recourse Model [29.057300578765663]
  RoCourseNet is a training framework that jointly optimize predictions and recourses that are robust to future data shifts. We show that RoCourseNet consistently achieves more than 96% robust validity and outperforms state-of-the-art baselines by at least 10% in generating robust explanations.
  arXiv  Detail & Related papers  (2022-06-01T18:18:18Z)
• DualCF: Efficient Model Extraction Attack from Counterfactual Explanations [57.46134660974256]
  Cloud service providers have launched Machine-Learning-as-a-Service platforms to allow users to access large-scale cloudbased models via APIs. Such extra information inevitably causes the cloud models to be more vulnerable to extraction attacks. We propose a novel simple yet efficient querying strategy to greatly enhance the querying efficiency to steal a classification model.
  arXiv  Detail & Related papers  (2022-05-13T08:24:43Z)
• ReLACE: Reinforcement Learning Agent for Counterfactual Explanations of Arbitrary Predictive Models [6.939617874336667]
  We introduce a model-agnostic algorithm to generate optimal counterfactual explanations. Our method is easily applied to any black-box model, as this resembles the environment that the DRL agent interacts with. In addition, we develop an algorithm to extract explainable decision rules from the DRL agent's policy, so as to make the process of generating CFs itself transparent.
  arXiv  Detail & Related papers  (2021-10-22T17:08:49Z)
• Explanation-Guided Training for Cross-Domain Few-Shot Classification [96.12873073444091]
  Cross-domain few-shot classification task (CD-FSC) combines few-shot classification with the requirement to generalize across domains represented by datasets. We introduce a novel training approach for existing FSC models. We show that explanation-guided training effectively improves the model generalization.
  arXiv  Detail & Related papers  (2020-07-17T07:28:08Z)

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.