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 84 for “"Sample Complexity"”.
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Refining the sample complexity of comparative learning
… the statistical (and sometimes computational) complexity of machine learning tasks. Comparative learning is a recently introduced variation of the PAC framework that interpolates between the two standard extreme settings of realizable and agnostic PAC learning. In comparative learning the …
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System identification for the bar model: Algorithms, consistency and sample complexity
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-05-01
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On the Sample Complexity of Imitation Learning for Smoothed Model Predictive Control
Recent work in imitation learning has shown that having an expert controller that is both suitably smooth and stable enables much stronger guarantees on the performance of the approximating learned controller. Constructing such smoothed expert controllers for arbitrary systems remains challenging, …
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Sample Complexity of Incremental Policy Gradient Methods for Solving Multi-Task Reinforcement Learning
We consider a multi-task learning problem, where an agent is presented a number of N reinforcement learning tasks. To solve this problem, we are interested in studying the gradient approach, which iteratively updates an estimate of the optimal policy using the gradients of the value functions. The …
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On the finite sample complexity of causal discovery and the value of domain expertise
… of a CI oracle. In this thesis, we analyze the sample complexity of causal discovery algorithms without a CI oracle: given a certain level of confidence, how many data points are needed for a causal discovery algorithm to identify a causal structure? Furthermore, our methods allow us to quantify …
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Understanding neural network sample complexity and interpretable convergence-guaranteed deep learning with polynomial regression
We first study the sample complexity of one-layer neural networks, namely the number of examples that are needed in the training set for such models to be able to learn meaningful information out-of-sample. We empirically derive quantitative relationships between the sample complexity and the …
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Learning to teach and meta-learning for sample-efficient multiagent reinforcement learning
… thesis introduces two frameworks to reduce the sample complexity in MARL. The first framework presented in this thesis provides a method to reduce the sample complexity by exchanging knowledge between agents. In particular, recent work on agents that learn to teach other teammates has …
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Testing, Learning, and Optimization in High Dimensions
… we study two separate problems: (1) What is the sample complexity of testing the class of Determinantal Point Processes? and (2) Introducing a new analysis for optimization and generalization of deep neural networks beyond their linear approximation. For the first problem, we characterize the …
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Testing properties of Ising models
Given samples from an unknown multivariate distribution p, is it possible to distinguish whether p is the product of its marginals versus p being [epsilon]-far from every product distribution? Similarly, is it possible to distinguish whether p equals a given distribution q versus p and q being …
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Reinforcement Actor-Critic Learning As A Rehearsal In MicroRTS
… remains a data-hungry approach featuring a high sample complexity. In this thesis, we focus on a sample complexity reduction technique called reinforcement learning as a rehearsal (RLaR), and on the RTS game of MicroRTS to formulate and evaluate it. RLaR has been formulated in the context of …
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Testing shape restriction properties of probability distributions in a unified way
… of discrete distributions. Specifically, given sample access to an arbitrary distribution D over [n] and a property P, the goal is to distinguish between ... Building on a result of [9], we develop a general algorithm for this question, which applies to a large range of "shape-constrained" …
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Learning and testing junta distributions over hypercubes
… to learn k-junta distributions with a number of samples that depends only logarithmically on the total number n of dimensions. We give two proofs of this result; one using the cover method and one by developing a Fourier-based learning algorithm inspired by the Low-Degree Algorithm of Linial, …
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Derivative-Free Meta-Blackbox Optimization on Manifold
… to improve the computational efficiency and sample complexity of derivative-free optimization. Based on the observation that most practical high-dimensional functions lie on a latent low-dimensional manifold, which can be further shared among problem instances, the proposed method jointly …
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Statistical Limits and Efficient Algorithms for Learning-Enabled Control
… control continues to grow, the development of sample-efficient algorithms becomes increasingly critical. However, even in the simplest settings, we often do not know algorithms which achieve optimal sample complexity with respect to particular problem instances. This thesis discusses recent …
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On feature selection : learning with exponentially many irreverent features as training examples
… idealization of performing search exactly, has sample complexity ( and error) that grows logarithmically in the number of "irrelevant" features - which means it can tolerate having a number of "irrelevant" features exponential in the number of training examples - and search heuristics are again …
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Learning Pattern Languages from a Small Number of Helpfully Chosen Examples
… over, determining the number of data points (sample complexity) and the amount of computational time (time complexity) required for learning a particular class of languages in a particular model is a main goal in computational learning theory. In this thesis we focus on learning classes of …
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ONLINE LEARNING, UNIFORM CONVERGENCE, AND A THEORY OF INTERPRETABILITY
… for multiple online learning problems, the sample complexity for uniform convergence, and a learning-theoretic approach to interpretable machine learning. First, we focus on online learning and investigate variants of the multi-armed bandit problem, including settings with feedback graphs, …
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Sample Efficient Reinforcement Learning with Partial Dynamics Knowledge
The problem of sample complexity of online reinforcement learning is often studied in the literature without taking into account any partial knowledge about the system dynamics that could potentially accelerate the learning process. In this thesis, we study the sample complexity of online …
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TARGET MODIFICATION FOR ENHANCED PERFORMANCE MATRIX ASSISTED LASER DESORPTION IONIZATION (MALDI) MASS SPECTROMETRY
… MALDI can be improved by either simplifying the sample complexity, modifying the sample preparation approach to increase the ionization efficiency of mixture components or seeking further enhancements to instrument performance. In this work these improvements are pursued through modifications to …
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Learning-based optimal and robust control: A policy optimization perspective
… and serve as benchmarks for studying the sample complexity of RL algorithms, which learn to control systems through repeated interactions. The first part of this dissertation focuses on two optimal control problems. The first problem addresses the linear quadratic Gaussian (LQG) control …
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