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Skip to Search Results- 15Szepesvari, Csaba (Computing Science)
- 4Schuurmans, Dale (Computing Science)
- 3Hegde, Nidhi (Computing Science)
- 1Andras Gyorgy (Imperial College London, U.K.)
- 1Bowling, Michael (Computing Science)
- 1Fyshe, Alona (Computing Science)
- 2Joulani, Pooria
- 1Abbasi-Yadkori, Yasin
- 1Afkanpour, Arash
- 1Aslan,Ozlem
- 1Balazs, Gabor
- 1Chandak, Kushagra
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Spring 2017
Optimizing an objective function over convex sets is a key problem in many different machine learning models. One of the various kinds of well studied objective functions is the convex function, where any local minimum must be the global mini- mum over the domain. To find the optimal point that...
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Beyond Static Classification: Long-term Fairness for Minority Groups via Performative Prediction and Distributionally Robust Optimization
DownloadFall 2022
In recent years machine learning (ML) models have begun to be deployed at enormous scales, but too often without adequate concern for whether or not an ML model will make fair decisions. Fairness in ML is a burgeoning research area, but work to define formal fairness criteria has some serious...
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Spring 2017
Most machine learning problems can be posed as solving a mathematical program that describes the structure of the prediction problem, usually expressed in terms of carefully chosen losses and regularizers. However, many machine learning problems yield mathematical programs that are not convex in...
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Fall 2016
This thesis explores theoretical, computational, and practical aspects of convex (shape-constrained) regression, providing new excess risk upper bounds, a comparison of convex regression techniques with theoretical guarantee, a novel heuristic training algorithm for max-affine representations,...
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Fall 2023
A matroid bandit is the online version of combinatorial optimization on a matroid, in which the learner chooses $K$ actions from a set of $L$ actions that can form a matroid basis. Many real-world applications such as recommendation systems can be modeled as matroid bandits. In such learning...
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Fall 2023
Federated learning is in widespread use for learning a global model when data is distributed across various distributed clients. In much of the prior work, the data is assumed to consist of independent data points. However, there is often an underlying graph that structures the data points. Such...
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Fall 2017
On the one hand, theoretical analyses of machine learning algorithms are typically performed based on various probabilistic assumptions about the data. While these probabilistic assumptions are important in the analyses, it is debatable whether such assumptions actually hold in practice. Another...
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Fall 2012
In this thesis, the multi-armed bandit (MAB) problem in online learning is studied, when the feedback information is not observed immediately but rather after arbitrary, unknown, random delays. In the stochastic" setting when the rewards come from a fixed distribution, an algorithm is given that...
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Spring 2013
Multiple kernel learning (MKL) addresses the problem of learning the kernel function from data. Since a kernel function is associated with an underlying feature space, MKL can be considered as a systematic approach to feature selection. Many of the existing MKL algorithms perform kernel learning...