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Skip to Search Results- 3Learning theory
- 2Machine learning
- 1Constrained Markov Decision Process
- 1Independent component analysis
- 1Online learning
- 1Online linear optimization
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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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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 2023
Many real-world tasks in fields such as robotics and control can be formulated as constrained Markov decision processes (CMDPs). In CMDPs, the objective is usually to optimize the return while ensuring some constraints being satisfied at the same time. The primal-dual approach is a common...