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15 papers · 1 filter
Pareto Front-Diverse Batch Multi-Objective Bayesian Optimization
Alaleh Ahmadianshalchi, Syrine Belakaria, Janardhan Rao Doppa
We consider the problem of multi-objective optimization (MOO) of expensive black-box functions with the goal of discovering high-quality and diverse Pareto fronts where we are allo…
Training Robust Deep Models for Time-Series Domain: Novel Algorithms and Theoretical Analysis
Taha Belkhouja, Yan Yan, Janardhan Rao Doppa
Despite the success of deep neural networks (DNNs) for real-world applications over time-series data such as mobile health, little is known about how to train robust DNNs for time-…
Adversarial Framework with Certified Robustness for Time-Series Domain via Statistical Features
Taha Belkhouja, Janardhan Rao Doppa
Time-series data arises in many real-world applications (e.g., mobile health) and deep neural networks (DNNs) have shown great success in solving them. Despite their success, littl…
On the Stochastic Stability of Deep Markov Models
Ján Drgoňa, Sayak Mukherjee, Jiaxin Zhang +2
Deep Markov models (DMM) are generative models that are scalable and expressive generalization of Markov models for representation, learning, and inference problems. However, the f…
Output Space Entropy Search Framework for Multi-Objective Bayesian Optimization
Syrine Belakaria, Aryan Deshwal, Janardhan Rao Doppa
We consider the problem of black-box multi-objective optimization (MOO) using expensive function evaluations (also referred to as experiments), where the goal is to approximate the…
Stress Classification and Personalization: Getting the most out of the least
Ramesh Kumar Sah, Hassan Ghasemzadeh
Stress detection and monitoring is an active area of research with important implications for the personal, professional, and social health of an individual. Current approaches for…