People + AI Research (PAIR)
The past few years have seen rapid advances in machine learning, with new technologies achieving dramatic improvements in technical performance. But we can go beyond optimizing objective functions. By building AI systems with users in mind from the ground up, we open up entire new areas of design and interaction. PAIR is devoted to advancing the research and design of people-centric AI systems. We’re interested in the full spectrum of human interaction with machine intelligence, from supporting engineers to understanding everyday experiences with AI. Our goal is to do fundamental research, invent new technology, and create frameworks for design in order to drive a human-centered approach to artificial intelligence. And we want to be as open as possible: we’re building open source tools that everyone can use, hosting public events, and supporting academics in advancing the state of the art. …

Differentiable Subset Sampling
Many machine learning tasks require sampling a subset of items from a collection. Due to the non-differentiability of subset sampling, the procedure is usually not included in end-to-end deep learning models. We show that through a connection to weighted reservoir sampling, the Gumbel-max trick can be extended to produce exact subset samples, and that a recently proposed top-k relaxation can be used to differentiate through the subset sampling procedure. We test our method on end-to-end tasks requiring subset sampling, including a differentiable k-nearest neighbors task and an instance-wise feature selection task for model interpretability. …

Masked Convolutional Generative Flow (MaCow)
Flow-based generative models, conceptually attractive due to tractability of both the exact log-likelihood computation and latent-variable inference, and efficiency of both training and sampling, has led to a number of impressive empirical successes and spawned many advanced variants and theoretical investigations. Despite their computational efficiency, the density estimation performance of flow-based generative models significantly falls behind those of state-of-the-art autoregressive models. In this work, we introduce masked convolutional generative flow (MaCow), a simple yet effective architecture of generative flow using masked convolution. By restricting the local connectivity in a small kernel, MaCow enjoys the properties of fast and stable training, and efficient sampling, while achieving significant improvements over Glow for density estimation on standard image benchmarks, considerably narrowing the gap to autoregressive models. …

FIRE-DES++
Despite being very effective in several classification tasks, Dynamic Ensemble Selection (DES) techniques can select classifiers that classify all samples in the region of competence as being from the same class. The Frienemy Indecision REgion DES (FIRE-DES) tackles this problem by pre-selecting classifiers that correctly classify at least one pair of samples from different classes in the region of competence of the test sample. However, FIRE-DES applies the pre-selection for the classification of a test sample if and only if its region of competence is composed of samples from different classes (indecision region), even though this criterion is not reliable for determining if a test sample is located close to the borders of classes (true indecision region) when the region of competence is obtained using classical nearest neighbors approach. Because of that, FIRE-DES mistakes noisy regions for true indecision regions, leading to the pre-selection of incompetent classifiers, and mistakes true indecision regions for safe regions, leaving samples in such regions without any pre-selection. To tackle these issues, we propose the FIRE-DES++, an enhanced FIRE-DES that removes noise and reduces the overlap of classes in the validation set; and defines the region of competence using an equal number of samples of each class, avoiding selecting a region of competence with samples of a single class. Experiments are conducted using FIRE-DES++ with 8 different dynamic selection techniques on 64 classification datasets. Experimental results show that FIRE-DES++ increases the classification performance of all DES techniques considered in this work, outperforming FIRE-DES with 7 out of the 8 DES techniques, and outperforming state-of-the-art DES frameworks. …