2,081 research outputs found
Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations
Post-hoc explanations of machine learning models are crucial for people to
understand and act on algorithmic predictions. An intriguing class of
explanations is through counterfactuals, hypothetical examples that show people
how to obtain a different prediction. We posit that effective counterfactual
explanations should satisfy two properties: feasibility of the counterfactual
actions given user context and constraints, and diversity among the
counterfactuals presented. To this end, we propose a framework for generating
and evaluating a diverse set of counterfactual explanations based on
determinantal point processes. To evaluate the actionability of
counterfactuals, we provide metrics that enable comparison of
counterfactual-based methods to other local explanation methods. We further
address necessary tradeoffs and point to causal implications in optimizing for
counterfactuals. Our experiments on four real-world datasets show that our
framework can generate a set of counterfactuals that are diverse and well
approximate local decision boundaries, outperforming prior approaches to
generating diverse counterfactuals. We provide an implementation of the
framework at https://github.com/microsoft/DiCE.Comment: 13 page
Active Learning based on Data Uncertainty and Model Sensitivity
Robots can rapidly acquire new skills from demonstrations. However, during
generalisation of skills or transitioning across fundamentally different
skills, it is unclear whether the robot has the necessary knowledge to perform
the task. Failing to detect missing information often leads to abrupt movements
or to collisions with the environment. Active learning can quantify the
uncertainty of performing the task and, in general, locate regions of missing
information. We introduce a novel algorithm for active learning and demonstrate
its utility for generating smooth trajectories. Our approach is based on deep
generative models and metric learning in latent spaces. It relies on the
Jacobian of the likelihood to detect non-smooth transitions in the latent
space, i.e., transitions that lead to abrupt changes in the movement of the
robot. When non-smooth transitions are detected, our algorithm asks for an
additional demonstration from that specific region. The newly acquired
knowledge modifies the data manifold and allows for learning a latent
representation for generating smooth movements. We demonstrate the efficacy of
our approach on generalising elementary skills, transitioning across different
skills, and implicitly avoiding collisions with the environment. For our
experiments, we use a simulated pendulum where we observe its motion from
images and a 7-DoF anthropomorphic arm.Comment: Published on 2018 IEEE/RSJ International Conference on Intelligent
Robots and Syste
Hospital efficiency analysis through individual effects: A Bayesian approach
Monte Carlo Technique;Hospitals;Bayesian Statistics;Markov Chains;Panel Data
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