Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 39 for “"Convergence guarantees"”.
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Parameter estimation in HMMs with guaranteed convergence
… widely used in practice, the theoretical guarantees associated with EM are quite weak. We study the setting of a hidden Markov model (HMM) with two hidden states, where the (symmetric) transition matrix [mu] is unknown and observations are Gaussian with known covariance and unknown mean …
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Proximal Gradient Algorithms for Gaussian Variational Inference:Optimization in the Bures–Wasserstein Space
… proposed algorithm, we obtain state-of-the-art convergence guarantees when π is log-smooth and log-concave, as well as the first convergence guarantees to first-order stationary solutions when π is only log-smooth. Additionally, in the setting where the potential admits a representation as the …
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Reinforcement learning for multi-agent and robust control systems
… for these settings, supported by theoretical convergence guarantees. In setting i, a team of collaborative MARL agents is connected via a communication network, without the coordination of any central controller. With only neighbor-to-neighbor communications, we introduce decentralized …
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Optimization Methods for Machine Learning under Structural Constraints
… and active set methods, and derive novel linear convergence guarantees for our proposed algorithms. Empirically, our framework can approximately solve instances with n=100,000 and d=10 within minutes. In the second chapter, we develop a new computational framework for computing log-concave …
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Learning to teach and meta-learning for sample-efficient multiagent reinforcement learning
… dimensionality of the problems, and the lack of convergence guarantees. As a result, many experiences are often required to learn effective multiagent policies. This thesis introduces two frameworks to reduce the sample complexity in MARL. The first framework presented in this thesis provides a …
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Algorithms for Gibbs states of quantum many-body systems
… matter systems. For these algorithms, we provide convergence guarantees in various restricted settings. Such restrictions are necessary due to hardness results from Hamiltonian complexity. However, in part of this work, we take a step further, presenting algorithms without a priori convergence …
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Operational decisions and learning for multiproduct retail
… via regression. We establish finite-sample convergence guarantees on the model parameters. The parameter convergence guarantees are then extended to out-of-sample performance guarantees in terms of revenue, in the form of a high-probability bound on the gap between the expected revenue of …
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Upper and Lower Bounds for Sampling
… of sampling as optimization [JKO98], and give convergence guarantees for them. We also obtain state-of-the-art convergence results for the popular Metopolis-Adjusted Langevin Algorithm. On the lower bound side, we establish the query complexity for strongly log-concave sampling in all constant …
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Efficient Hierarchical Global Motion Planning for Autonomous Vehicles
… non-trivial, including issues such as algorithm convergence, its correspondence to system controllability, optimality of solution, computational complexity, and dynamic environments. In response to these challenges, a hierarchical algorithm will be introduced that provides (a) decreased …
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Solving Large MDPs Quickly with Partitioned Value Iteration
… problems, while preserving accuracy and convergence guarantees.
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Multiple aspect ranking for opinion analysis
… models, yet our training algorithm preserves the convergence guarantees of perceptron rankers. Our empirical results further confirm the strength of the model: the algorithm provides significant improvement over both individual rankers, a state-of-the-art joint ranking model, and ad-hoc methods …
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High-order tuners for convex optimization : stability and accelerated learning
… methods, with accelerated learning guarantees, have received a lot of attention due to their provable guarantees of fast learning in certain classes of problems and multiple algorithms have been derived. However, properties for these methods hold true only for constant regressors. …
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Understanding neural network sample complexity and interpretable convergence-guaranteed deep learning with polynomial regression
… that existing approaches. It also offers more convergence guarantees during training. Finally, we empirically show that the widely-used Stochastic Gradient Descent algorithm makes the weights of the trained neural networks converge to the optimal polynomial regression weights.
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Geometric numerical integration for optimisation
… of the discrete gradient update equation, convergence rates, convergence of the iterates, and propose methods for solving the discrete gradient update equation with superior stability and convergence rates. Furthermore, we present results from numerical experiments which support the theory. …
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Multi-Player Zero-Sum Markov Games with Networked Separable Interactions
… games, for zero-sum NMGs, and establish convergence guarantees to Markov stationary NE under a star-shaped network structure. Finally, in light of the hardness result, we focus on computing a Markov non-stationary NE and provide finite-iteration guarantees for a series of …
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NEW PARADIGMS IN SOURCE CODING AND CHANNEL CODING
… is the gold standard because it gives uniform convergence guarantees, even under adversarial conditions. In the minimax universal setting, the same $\Theta\left(\frac{\log n}{n} \right)$ rate redundancy result has been proven for lossless codes since 1981. However, minimax universal results for …
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Scalable Methodologies for Optimizing Over Probability Distributions
… model by learning proximal operators with global convergence guarantees; and 3) solving mass-conserving differential equations of probability flows without temporal or spatial discretization by leveraging the self-consistency of the dynamical system.
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Aircraft System Identification Approach for Control Surface Fault Diagnosis
… methods with exponential or finite-time convergence guarantees under a persistence of excitation condition, and an augmented-state extended Kalman filter. These methods were applied to flight data containing an artificially injected stuck left-aileron fault, implemented through a custom …
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Reinforcement Learning-based Optimization of Multiple Access in Wireless Networks
… of coordinated learning and focus on deriving convergence guarantees for learning while minimizing the complexity of coordination. We provide simulations that showcase how coordination can help achieve a fine balance, in terms of complexity and performance, between fully decentralized and …
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Contraction maps and applications to the analysis of iterative algorithms
… Banach's fixed point theorem to establish the convergence of iterative methods when pairing it with carefully designed metrics. Our first result is a strong converse of Banach's theorem, showing that it is a universal analysis tool for establishing uniqueness of fixed points and convergence of …
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