A Two-Stage Algorithm for Cost-Efficient Multi-instance Counterfactual Explanations
- URL: http://arxiv.org/abs/2403.01221v2
- Date: Tue, 21 May 2024 11:34:38 GMT
- Title: A Two-Stage Algorithm for Cost-Efficient Multi-instance Counterfactual Explanations
- Authors: André Artelt, Andreas Gregoriades,
- Abstract summary: We propose a flexible two-stage algorithm for finding groups of instances and computing cost-efficient multi-instance counterfactual explanations.
The paper presents the algorithm and its performance against popular alternatives through a comparative evaluation.
- Score: 2.992602379681373
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Counterfactual explanations constitute among the most popular methods for analyzing black-box systems since they can recommend cost-efficient and actionable changes to the input of a system to obtain the desired system output. While most of the existing counterfactual methods explain a single instance, several real-world problems, such as customer satisfaction, require the identification of a single counterfactual that can satisfy multiple instances (e.g. customers) simultaneously. To address this limitation, in this work, we propose a flexible two-stage algorithm for finding groups of instances and computing cost-efficient multi-instance counterfactual explanations. The paper presents the algorithm and its performance against popular alternatives through a comparative evaluation.
Related papers
- On Speeding Up Language Model Evaluation [48.51924035873411]
Development of prompt-based methods with Large Language Models (LLMs) requires making numerous decisions.
We propose a novel method to address this challenge.
We show that it can identify the top-performing method using only 5-15% of the typically needed resources.
arXiv Detail & Related papers (2024-07-08T17:48:42Z) - Training Greedy Policy for Proposal Batch Selection in Expensive Multi-Objective Combinatorial Optimization [52.80408805368928]
We introduce a novel greedy-style subset selection algorithm for batch acquisition.
Our experiments on the red fluorescent proteins show that our proposed method achieves the baseline performance in 1.69x fewer queries.
arXiv Detail & Related papers (2024-06-21T05:57:08Z) - Task Facet Learning: A Structured Approach to Prompt Optimization [14.223730629357178]
We propose an algorithm that learns multiple facets of a task from a set of training examples.
The resulting algorithm, UniPrompt, consists of a generative model to generate initial candidates for each prompt section.
Empirical evaluation on multiple datasets and a real-world task shows that prompts generated using UniPrompt obtain higher accuracy than human-tuned prompts.
arXiv Detail & Related papers (2024-06-15T04:54:26Z) - FastGAS: Fast Graph-based Annotation Selection for In-Context Learning [53.17606395275021]
In-context learning (ICL) empowers large language models (LLMs) to tackle new tasks by using a series of training instances as prompts.
Existing methods have proposed to select a subset of unlabeled examples for annotation.
We propose a graph-based selection method, FastGAS, designed to efficiently identify high-quality instances.
arXiv Detail & Related papers (2024-06-06T04:05:54Z) - Preference Inference from Demonstration in Multi-objective Multi-agent
Decision Making [0.0]
We propose an algorithm to infer linear preference weights from either optimal or near-optimal demonstrations.
Empirical results demonstrate significant improvements compared to the baseline algorithms.
In future work, we plan to evaluate the algorithm's effectiveness in a multi-agent system.
arXiv Detail & Related papers (2023-04-27T12:19:28Z) - Rethinking Clustering-Based Pseudo-Labeling for Unsupervised
Meta-Learning [146.11600461034746]
Method for unsupervised meta-learning, CACTUs, is a clustering-based approach with pseudo-labeling.
This approach is model-agnostic and can be combined with supervised algorithms to learn from unlabeled data.
We prove that the core reason for this is lack of a clustering-friendly property in the embedding space.
arXiv Detail & Related papers (2022-09-27T19:04:36Z) - Dynamic Proposals for Efficient Object Detection [48.66093789652899]
We propose a simple yet effective method which is adaptive to different computational resources by generating dynamic proposals for object detection.
Our method achieves significant speed-up across a wide range of detection models including two-stage and query-based models.
arXiv Detail & Related papers (2022-07-12T01:32:50Z) - Budgeted Classification with Rejection: An Evolutionary Method with
Multiple Objectives [0.0]
Budgeted, sequential classifiers (BSCs) process inputs through a sequence of partial feature acquisition and evaluation steps.
This allows for an efficient evaluation of inputs that prevents unneeded feature acquisition.
We propose a problem-specific genetic algorithm to build budgeted, sequential classifiers with confidence-based reject options.
arXiv Detail & Related papers (2022-05-01T22:05:16Z) - Efficient Multiple Constraint Acquisition [1.3706331473063877]
Constraint acquisition systems such as QuAcq and MultiAcq can assist non-expert users to model their problems as constraint networks.
We present a technique that boosts the performance of constraint acquisition by reducing the number of queries significantly.
We then turn our attention to query generation which is a significant but rather overlooked part of the acquisition process.
arXiv Detail & Related papers (2021-09-13T12:42:16Z) - Multi-Objective Counterfactual Explanations [0.7349727826230864]
We propose the Multi-Objective Counterfactuals (MOC) method, which translates the counterfactual search into a multi-objective optimization problem.
Our approach not only returns a diverse set of counterfactuals with different trade-offs between the proposed objectives, but also maintains diversity in feature space.
arXiv Detail & Related papers (2020-04-23T13:56:39Z) - Extreme Algorithm Selection With Dyadic Feature Representation [78.13985819417974]
We propose the setting of extreme algorithm selection (XAS) where we consider fixed sets of thousands of candidate algorithms.
We assess the applicability of state-of-the-art AS techniques to the XAS setting and propose approaches leveraging a dyadic feature representation.
arXiv Detail & Related papers (2020-01-29T09:40:58Z)
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.