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Found 9 papers, 4 papers with code
PAC-Bayes Bounds for Bandit Problems: A Survey and Experimental Comparison
In many of these applications, principled algorithms with strong performance guarantees would be very much appreciated.
Validation of Composite Systems by Discrepancy Propagation
Assessing the validity of a real-world system with respect to given quality criteria is a common yet costly task in industrial applications due to the vast number of required real-world tests.
Utilizing Expert Features for Contrastive Learning of Time-Series Representations
We therefore devise ExpCLR, a novel contrastive learning approach built on an objective that utilizes expert features to encourage both properties for the learned representation.
PAC-Bayesian Lifelong Learning For Multi-Armed Bandits
We present a PAC-Bayesian analysis of lifelong learning.
SOSP: Efficiently Capturing Global Correlations by Second-Order Structured Pruning
We validate SOSP-H by comparing it to our second method SOSP-I that uses a well-established Hessian approximation, and to numerous state-of-the-art methods.
Which Minimizer Does My Neural Network Converge To?
The loss surface of an overparameterized neural network (NN) possesses many global minima of zero training error.
Beyond the Mean-Field: Structured Deep Gaussian Processes Improve the Predictive Uncertainties
Deep Gaussian Processes learn probabilistic data representations for supervised learning by cascading multiple Gaussian Processes.
Wide Neural Networks are Interpolating Kernel Methods: Impact of Initialization on Generalization
The recently developed link between strongly overparametrized neural networks (NNs) and kernel methods has opened a new way to understand puzzling features of NNs, such as their convergence and generalization behaviors.
Learning Gaussian Processes by Minimizing PAC-Bayesian Generalization Bounds
Gaussian Processes (GPs) are a generic modelling tool for supervised learning.
