We introduce a novel algorithmic approach to content recommendation based on adaptive clustering of exploration-exploitation ("bandit") strategies. W…

We provide a novel notion of what it means to be interpretable, looking past the usual association with human understanding. Our key insight is that …

We introduce a novel training principle for probabilistic models that is an alternative to maximum likelihood. The proposed Generative Stochastic Net…

In this paper, we consider regression problems with one-hidden-layer neural networks (1NNs). We distill some properties of activation functions that …

While the authors of Batch Normalization (BN) identify and address an important problem involved in training deep networks-- Internal Covariate Shift…

The popular Alternating Least Squares (ALS) algorithm for tensor decomposition is efficient and easy to implement, but often converges to poor local …

This paper presents the input convex neural network architecture. These are scalar-valued (potentially deep) neural networks with constraints on the …

Supervised machine learning models boast remarkable predictive capabilities. But can you trust your model? Will it work in deployment? What else can …

In this paper we present a new dataset and user simulator e-QRAQ (explainable Query, Reason, and Answer Question) which tests an Agent's ability to r…

Deep neural networks have been shown to be very successful at learning feature hierarchies in supervised learning tasks. Generative models, on the ot…

Stochastic variational inference (SVI) is emerging as the most promising candidate for scaling inference in Bayesian probabilistic models to large da…

Many applications require recovering a ground truth low-rank matrix from noisy observations of the entries, which in practice is typically formulated…

In this paper, we analyze a generic algorithm scheme for sequential global optimization using Gaussian processes. The upper bounds we derive on the c…

Recently, researchers have made significant progress combining the advances in deep learning for learning feature representations with reinforcement …

We present a new view of Gaussian belief propagation (GaBP) based on a representation of the determinant as a product over orbits of a graph. We show…

Deciding effective and timely preventive measures against complex social problems affecting relatively low income geographies is a difficult challeng…

Tree ensembles, such as random forest and boosted trees, are renowned for their high prediction performance, whereas their interpretability is critic…

Hand in hand with deep learning advancements, algorithms of music composition increase in performance. However, most of the successful models are des…

Opponent modeling is necessary in multi-agent settings where secondary agents with competing goals also adapt their strategies, yet it remains challe…

Along with the recent advances in scalable Markov Chain Monte Carlo methods, sampling techniques that are based on Langevin diffusions have started r…

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