Machine Intelligence

We develop a framework for designing optimal quadratic optimization methods in terms of their average-case runtime. This yields a new class of methods that achieve acceleration through a model of the Hessian's expected spectral density. We develop explicit algorithms for the uniform, Marchenko-Pastur, and exponential distributions. These methods are momentum-based gradient algorithms whose...

Quantifying the value of datum is a fundamental problem in machine learning. Besides building insights about the learning task, data valuation has applications in diverse use-cases, such as domain adaptation, corrupted sample discovery, and robust learning. To adaptively learn data values jointly with the predictive model, we propose a meta learning framework - named Data Valuator using...

We propose a novel inherently interpretable machine learning method that bases decisions on few relevant examples that we call prototypes. Our method, ProtoAttend, can be integrated into a wide range of neural network architectures including pre-trained models. It utilizes an attention mechanism that relates the encoded representations to samples in order to determine prototypes. Without...

Deep convolutional networks have achieved high accuracy on image classification tasks. Due to the complexity of these models, they are considered black boxes as the decisions made by these models are hard to interpret. This lack of interpretation have plagued the wide use of these models in critical application. One class of models that offers interpretations by design are those that use hard...