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 true Pareto set of solutions by minimizing the total resource cost of experiments.
CALDA synergistically combines the principles of contrastive learning and adversarial learning to robustly support multi-source UDA (MS-UDA) for time series data.
We develop a principled approach for constructing diffusion kernels over hybrid spaces by utilizing the additive kernel formulation, which allows additive interactions of all orders in a tractable manner.
The key idea behind SETGAN for an image generation task is for a given input image, we train a GAN on a remote server and use the trained model on edge devices.
We consider the problem of optimizing expensive black-box functions over discrete spaces (e. g., sets, sequences, graphs).
The overall goal is to approximate the true Pareto set of solutions by minimizing the resources consumed for function evaluations.
The key idea is to select the sequence of input and function approximations for multiple objectives which maximize the information gain per unit cost for the optimal Pareto front.
We consider the problem of constrained multi-objective blackbox optimization using expensive function evaluations, where the goal is to approximate the true Pareto set of solutions satisfying a set of constraints while minimizing the number of function evaluations.
Dynamic resource management has become one of the major areas of research in modern computer and communication system design due to lower power consumption and higher performance demands.
Based on recent advances in submodular relaxation (Ito and Fujimaki, 2016) for solving Binary Quadratic Programs, we study an approach referred as Parametrized Submodular Relaxation (PSR) towards the goal of improving the scalability and accuracy of solving AFO problems for BOCS model.
We consider the problem of constrained multi-objective (MO) blackbox optimization using expensive function evaluations, where the goal is to approximate the true Pareto set of solutions satisfying a set of constraints while minimizing the number of function evaluations.
First, we propose a novel Convolutional deep Domain Adaptation model for Time Series data (CoDATS) that significantly improves accuracy and training time over state-of-the-art DA strategies on real-world sensor data benchmarks.
To address this need, system-on-chips (SoC) that are at the heart of these devices provide a variety of control knobs, such as the number of active cores and their voltage/frequency levels.
First, we define a distance measure between candidate concept representations that improves the efficiency of search for target concept and generalization.
We consider the problem of multi-objective (MO) blackbox optimization using expensive function evaluations, where the goal is to approximate the true Pareto-set of solutions by minimizing the number of function evaluations.
Deep neural networks have seen tremendous success for different modalities of data including images, videos, and speech.
In this paper, we study the problem of active learning to automatically tune ensemble of anomaly detectors to maximize the number of true anomalies discovered.
Our results show that these generalized 3D NoCs only incur a 1. 8% (36-tile system) and 1. 1% (64-tile system) average performance loss compared to application-specific NoCs.
Human analysts that use anomaly detection systems in practice want to retain the use of simple and explainable global anomaly detectors.
First, we present an important insight into how anomaly detector ensembles are naturally suited for active learning.
Tight collaboration between experts of machine learning and manycore system design is necessary to create a data-driven manycore design framework that integrates both learning and expert knowledge.
Agents that communicate back and forth with humans to help them execute non-linguistic tasks are a long sought goal of AI.
The mainstream approach to structured prediction problems in computer vision is to learn an energy function such that the solution minimizes that function.