The Mars Curiosity rover is frequently sending back engineering and science data that goes through a pipeline of systems before reaching its final destinati...

Column generation (CG) algorithms are well known to suffer from convergence issues due, mainly, to the degenerate structure of their master problem and the ...

We consider an integrated optimization problem including the production, inventory, and outbound transportation decisions where a central plant fulfills the ...

Restless bandits are a class of sequential resource allocation problems concerned with allocating one or more resources among several alternative processes...

A new business opportunity is emerging with the combination of three key market trends: (1) Increased penetration of residential solar PV; (2) Rapid reductio...

Utility-based shortfall risk measure (SR) effectively captures decision maker’s risk attitude on tail losses by an increasing convex loss function. In this ...

Deep learning has redefined modern standards and performance in several areas such as computer vision and natural language processing. With increasing amou...

Column generation (CG) is widely used for solving large-scale optimization problems. This article presents a new approach based on a machine learning (ML) t...

Artificial Intelligence (AI) is the next society transformation builder. Massive AI-based applications include cloud servers, cell phones, cars, and pandemic...

One of the major challenges in large-scale distributed machine learning involving stochastic gradient methods is the high cost of gradient communication ove...

Pruning methods for deep neural networks based on weight magnitude have shown promise in recent research. We propose a new, highly flexible approach to neura...

The past few years have seen the ability of cooperative Malware Detection Systems (MDS) to detect complex and unknown malware. In a cooperative setting, an M...

The design of compact deep neural networks is a crucial task to enable widespread adoption of deep neural networks in the real-world, particularly for edge a...

Knowledge distillation is a technique that consists in training a student network, usually of a low capacity, to mimic the representation space and the perfo...

In time-to-event data analysis, the main object of interest is the time elapsed between the occurrence of two ordered events, say \(E_1, E_2\). Sampling fr...

Training large-scale deep neural networks is a long, time-consuming operation, often requiring many GPUs to accelerate. In large models, the time spent loadi...

Binary neural networks improve computationally efficiency of deep models with a large margin. However, there is still a performance gap between a successful...

Min-max formulations have attracted great attention in the ML community due to the rise of deep generative models and adversarial methods, while understandin...

The determinantal point process (DPP) provides a promising and attractive alternative to simple random sampling in cluster analysis or classification, for th...

Deep neural networks usually have unnecessarily high complexities and possibly many features of low utility, especially for tasks that they are not designed ...