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Fall 2021
The optimization of non-convex objective functions is a topic of central interest in machine learning. Remarkably, it has recently been shown that simple gradient-based optimization can achieve globally optimal solutions in important non-convex problems that arise in machine learning, including...
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2012
Bowling, Michael, Zinkevich, Martin
Online learning aims to perform nearly as well as the best hypothesis in hindsight. For some hypothesis classes, though, even finding the best hypothesis offline is challenging. In such offline cases, local search techniques are often employed and only local optimality guaranteed. For online...
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Spring 2021
It has gotten increasingly harder for laypersons to determine the veracity of online health information. This is because of the explosion of content in health social media, allowing anyone with an Internet connection to create and propagate health-related content. This includes both innocuous and...
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Fall 2019
We present two provably optimal differentially private algorithms for the stochastic multi-arm bandit problem, as opposed to the private analogue of the UCB-algorithm (Mishra and Thakurta 2015; Tossou and Dimitrakakis 2016) which doesn’t meet the recently discovered lower-bound of Ω( K log(T) /...
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Spring 2019
For many years, traditional boardgames such as Chess, Checkers or Go havebeen the standard environments to test new Artificial Intelligence (AI) algorithms for achieving robust game-playing agents capable of defeating the best human players. Presently, the focus has shifted...
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Fall 2024
Perceptual factors in vision can facilitate the development of more effective multimedia algorithms. In particular, the wide dynamic range of the human vision system is a motivation for developing image lighting enhancement algorithms. Image lighting enhancement can be achieved by capturing...
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Spring 2022
During the past 10 years, we have witnessed the proliferation of cloud computing services and their adoption in the industry. This rapid growth has been mainly due to economies of scale, improving resource utilization, infinite computing resources on demand, and pay per use cost model. However,...
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Fall 2023
Krishna Guruvayur Sasikumar, Aakash
The application of reinforcement learning (RL) to the optimal control of building systems has gained traction in recent years as it can reduce building energy consumption and improve human comfort, without requiring the knowledge of the building model. However, existing RL solutions for building...
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2006
Poulin, Brett, Wan, Xiang, Kolacz, Tom
Technical report TR06-03. Single nucleotide polymorphisms (SNPs) are genetic markers that may be used to identify the causes and risks of cancer. The sheer volume of data generated by SNP studies is difficult to analyze by hand. Machine learning techniques have been developed to address the types...