Learning Interpretable Models Using an Oracle

We look at a specific aspect of model interpretability: models often need to be constrained in size for them to be considered interpretable. But smaller models also tend to have high bias. This suggests a trade-off between interpretability and accuracy. Our work addresses this by: (a) showing that learning a training distribution (often different from the test distribution) can often increase accuracy of small models, and therefore may be used as a strategy to compensate for small sizes, and (b) providing a model-agnostic algorithm to learn such training distributions. We pose the distribution learning problem as one of optimizing parameters for an Infinite Beta Mixture Model based on a Dirichlet Process, so that the held-out accuracy of a model trained on a sample from this distribution is maximized. To make computation tractable, we project the training data onto one dimension: prediction uncertainty scores as provided by a highly accurate oracle model. A Bayesian Optimizer is used for learning the parameters. Empirical results using multiple real world datasets, various oracles and interpretable models with different notions of model sizes, are presented. We observe significant relative improvements in the F1-score in most cases, occasionally seeing improvements greater than 100% over baselines. Additionally we show that the proposed algorithm provides the following benefits: (a) its a framework which allows for flexibility in implementation, (b) it can be used across feature spaces, e.g., the text classification accuracy of a Decision Tree using character n-grams is shown to improve when using a Gated Recurrent Unit as an oracle, which uses a sequence of characters as its input, (c) it can be used to train models that have a non-differentiable training loss, e.g., Decision Trees, and (d) reasonable defaults exist for most parameters of the algorithm, which makes it convenient to use

Abduction-Based Explanations for Machine Learning Models

The growing range of applications of Machine Learning (ML) in a multitude of settings motivates the ability of computing small explanations for predictions made. Small explanations are generally accepted as easier for human decision makers to understand. Most earlier work on computing explanations is based on heuristic approaches, providing no guarantees of quality, in terms of how close such solutions are from cardinality- or subset-minimal explanations. This paper develops a constraint-agnostic solution for computing explanations for any ML model. The proposed solution exploits abductive reasoning, and imposes the requirement that the ML model can be represented as sets of constraints using some target constraint reasoning system for which the decision problem can be answered with some oracle. The experimental results, obtained on well-known datasets, validate the scalability of the proposed approach as well as the quality of the computed solutions

Conditional Similarity Networks

What makes images similar? To measure the similarity between images, they are typically embedded in a feature-vector space, in which their distance preserve the relative dissimilarity. However, when learning such similarity embeddings the simplifying assumption is commonly made that images are only compared to one unique measure of similarity. A main reason for this is that contradicting notions of similarities cannot be captured in a single space. To address this shortcoming, we propose Conditional Similarity Networks (CSNs) that learn embeddings differentiated into semantically distinct subspaces that capture the different notions of similarities. CSNs jointly learn a disentangled embedding where features for different similarities are encoded in separate dimensions as well as masks that select and reweight relevant dimensions to induce a subspace that encodes a specific similarity notion. We show that our approach learns interpretable image representations with visually relevant semantic subspaces. Further, when evaluating on triplet questions from multiple similarity notions our model even outperforms the accuracy obtained by training individual specialized networks for each notion separately.Comment: CVPR 201