We propose a fast, model agnostic method for finding interpretable counterfactual explanations of classifier predictions by using class prototypes. We show that class prototypes, obtained using either an encoder or through class specific k-d trees, significantly speed up the the search for counterfactual instances and result in more interpretable explanations. We introduce two novel metrics to quantitatively evaluate local interpretability at the instance level. We use these metrics to illustrate the effectiveness of our method on an image and tabular dataset, respectively MNIST and Breast Cancer Wisconsin (Diagnostic). The method also eliminates the computational bottleneck that arises because of numerical gradient evaluation for @math models.
The problem of local, instance level model explanations for classification can be approached from various angles. Feature attribution methods assign importance to each input feature for a given prediction. Attribution methods can be fully model agnostic @cite_18 @cite_31 or require knowledge of the architecture of the underlying model @cite_10 @cite_3 @cite_2 . Alternatively, we can also assess the impact of individual training data instances on a specific prediction by using influence functions @cite_12 @cite_14 @cite_7 .
@cite_18 @cite_14 @cite_7 @cite_3 @cite_2 @cite_31 @cite_10 @cite_12
2125327503 2166886337 2067624665 2311233238
It is difficult to apply machine learning to new domains because often we lack labeled problem instances. In this paper, we provide a solution to this problem that leverages domain knowledge in the form of affinities between input features and classes. For example, in a baseball vs. hockey text classification problem, even without any labeled data, we know that the presence of the word puck is a strong indicator of hockey. We refer to this type of domain knowledge as a labeled feature. In this paper, we propose a method for training discriminative probabilistic models with labeled features and unlabeled instances. Unlike previous approaches that use labeled features to create labeled pseudo-instances, we use labeled features directly to constrain the model's predictions on unlabeled instances. We express these soft constraints using generalized expectation (GE) criteria --- terms in a parameter estimation objective function that express preferences on values of a model expectation. In this paper we train multinomial logistic regression models using GE criteria, but the method we develop is applicable to other discriminative probabilistic models. The complete objective function also includes a Gaussian prior on parameters, which encourages generalization by spreading parameter weight to unlabeled features. Experimental results on text classification data sets show that this method outperforms heuristic approaches to training classifiers with labeled features. Experiments with human annotators show that it is more beneficial to spend limited annotation time labeling features rather than labeling instances. For example, after only one minute of labeling features, we can achieve 80 accuracy on the ibm vs. mac text classification problem using GE-FL, whereas ten minutes labeling documents results in an accuracy of only 77 We propose a new problem formulation which is similar to, but more informative than, the binary multiple-instance learning problem. In this setting, we are given groups of instances (described by feature vectors) along with estimates of the fraction of positively-labeled instances per group. The task is to learn an instance level classifier from this information. That is, we are trying to estimate the unknown binary labels of individuals from knowledge of group statistics. We propose a principled probabilistic model to solve this problem that accounts for uncertainty in the parameters and in the unknown individual labels. This model is trained with an efficient MCMC algorithm. Its performance is demonstrated on both synthetic and real-world data arising in general object recognition. In many classification problems labels are relatively scarce. One context in which this occurs is where we have labels for groups of instances but not for the instances themselves, as in multi-instance learning. Past work on this problem has typically focused on learning classifiers to make predictions at the group level. In this paper we focus on the problem of learning classifiers to make predictions at the instance level. To achieve this we propose a new objective function that encourages smoothness of inferred instance-level labels based on instance-level similarity, while at the same time respecting group-level label constraints. We apply this approach to the problem of predicting labels for sentences given labels for reviews, using a convolutional neural network to infer sentence similarity. The approach is evaluated using three large review data sets from IMDB, Yelp, and Amazon, and we demonstrate the proposed approach is both accurate and scalable compared to various alternatives. In supervised learning, it is typically assumed that the labeled training data comes from the same distribution as the test data to which the system will be applied. In recent years, machine-learning researchers have investigated methods to handle mismatch between the training and test domains, with the goal of building a classifier using the labeled data in the old domain that will perform well on the test data in the new domain. This problem is called domain adaptation or transfer learning, and it is a common scenario in speech processing applications. Labeled training data are often produced by an expensive hand-annotation process, and may consist of only one or two annotated corpora which are used to train virtually all systems regardless of the target domain. Often little or no labeled data is available for the new domain. In this work, we review the statistical machine learning literature dealing with the problem of “domain adaptation” or “transfer learning”. We focus on unsupervised domain adaptation methods, as opposed to model adaptation or supervised adaptation in which some labeled data is available from the test distribution. We consider four main classes of approaches in the literature: instance weighting for covariate shift; selflabeling methods; changes in feature representation; and cluster-based learning. Covariate shift methods re-weight training samples in the old domain to try to match the new domain, putting more weight on samples in populous regions in the new domain. Self-labeling methods incorporate unlabeled target domain examples into the training algorithm by making an initial guess about their labels and then iteratively refining the guesses or labeling more examples. Feature representation approaches try to find a new feature representation of the data, either to make the new and old distributions look similar, or to find an abstracted representation for domain-specific features. Cluster-based methods rely on the assumption that samples connected by high-density paths are likely to have the same label. Domain adaptation is a large area of research, with related work under several frameworks (and several names). A limited review from March 2008 can be found in [1], and one from Oct 2010 can be found in [2]. A recent book [3] investigates train test distribution mismatch in machine learning (particularly focused on covariate shift.) Some of the organization here roughly follows that in [1].