Here, we apply instantaneous and lag-based measures of conditional linear dependence, based on Granger-Geweke causality (GC), to infer network connections at distinct timescales from functional magnetic resonance imaging (fMRI) data.

In the context of advertising auctions, finding good reserve prices is a notoriously challenging learning problem. This is due to the heterogeneity of ad opportunity types, and the non-convexity of the objective function. In this work, we show how to reduce reserve price optimization to the standard setting of prediction under squared loss, a well understood problem in the learning community. We further bound the gap between the expected bid and revenue in terms of the average loss of the predictor. This is the first result that formally relates the revenue gained to the quality of a standard machine learned model.

Inferring topological and geometrical information from data can offer an alternative perspective on machine learning problems. Methods from topological data analysis, e.g., persistent homology, enable us to obtain such information, typically in the form

We propose a general "noise reduction" framework that can apply to a variety of private empirical risk minimization (ERM) algorithms,

Designing optimal treatment plans for patients with comorbidities requires accurate cause-specific mortality prognosis. Motivated by the recent availability of linked electronic health records, we develop a nonparametric Bayesian model for survival analysis with competing risks, which can be used for jointly assessing a patient's risk of multiple (competing) adverse outcomes. The model views a patient's survival times with respect to the competing risks as the outputs of a deep multi-task Gaussian process (DMGP), the inputs to which are the patients' covari-ates. Unlike parametric survival analysis methods based on Cox and Weibull models, our model uses DMGPs to capture complex non-linear interactions between the patients' covariates and cause-specific survival times, thereby learning flexible patient-specific and cause-specific survival curves, all in a data-driven fashion without explicit parametric assumptions on the hazard rates. We propose a varia-tional inference algorithm that is capable of learning the model parameters from time-to-event data while handling right censoring. Experiments on synthetic and real data show that our model outperforms the state-of-the-art survival models.

We study minimax strategies for the online prediction problem with expert advice.

Well-calibrated uncertainty is crucial for many tasks in deep learning.

Online learning is a general framework for sequential prediction.

Leading the Future said it will spend millions to keep Alex Bores out of Congress. It might be helping him instead. It turns out that when an AI-friendly super PAC with $100 million in backing from Silicon Valley bigwigs identifies you as its first target, it ends up generating a lot of attention. "I want to thank [the PAC] for their partnership in raising up the issue of how we regulate an incredibly powerful technology so that the future is one that benefits all of us," says Alex Bores, a New York Assembly member and Democratic congressional candidate, in an interview with WIRED. "I couldn't imagine a better partner this week."

As a result, the use of group-conditional decision making systems is often prohibited.