The concepts of risk-aversion, chance-constrained optimization, and robust optimization
have developed significantly over the last decade. Statistical learning community has also …

As robots and other intelligent agents move from simple environments and problems to more
complex, unstructured settings, manually programming their behavior has become …

Artificial intelligence (AI) agents driven by machine learning algorithms are rapidly
transforming the business world, generating heightened interest from researchers. In this …

Multi-label image classification is the task of predicting a set of labels corresponding to
objects, attributes or other entities present in an image. In this work we propose the …

Visual tracking is fundamentally the problem of regressing the state of the target in each
video frame. While significant progress has been achieved, trackers are still prone to failures …

Accurate predictions of RNA secondary structures can help uncover the roles of functional
non-coding RNAs. Although machine learning-based models have achieved high …

Due to the importance of zero-shot learning, ie, classifying images where there is a lack of
labeled training data, the number of proposed approaches has recently increased steadily …

We present a new technique for learning visual-semantic embeddings for cross-modal
retrieval. Inspired by hard negative mining, the use of hard negatives in structured …

Many real-world analytics problems involve two significant challenges: prediction and
optimization. Because of the typically complex nature of each challenge, the standard …

This paper presents OptNet, a network architecture that integrates optimization problems
(here, specifically in the form of quadratic programs) as individual layers in larger end-to-end …