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Found 10 papers, 4 papers with code
Tighter Confidence Bounds for Sequential Kernel Regression
In this capacity, they can inform the exploration-exploitation trade-off and form a core component in many sequential learning and decision-making algorithms.
PAC-Bayes Bounds for Bandit Problems: A Survey and Experimental Comparison
On the one hand, we found that PAC-Bayes bounds are a useful tool for designing offline bandit algorithms with performance guarantees.
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.
