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Found 6 papers, 4 papers with code
Generative Marginalization Models
We introduce marginalization models (MaMs), a new family of generative models for high-dimensional discrete data.
Sparse Bayesian Optimization
Bayesian optimization (BO) is a powerful approach to sample-efficient optimization of black-box objective functions.
ProBF: Learning Probabilistic Safety Certificates with Barrier Functions
In practice, we have inaccurate knowledge of the system dynamics, which can lead to unsafe behaviors due to unmodeled residual dynamics.
Task-Agnostic Amortized Inference of Gaussian Process Hyperparameters
One of the appeals of the GP framework is that the marginal likelihood of the kernel hyperparameters is often available in closed form, enabling optimization and sampling procedures to fit these hyperparameters to data.
The Landscape of Matrix Factorization Revisited
We show that, in contrast to the general situation, the minimum eigenvalue of strict saddles in $\mathcal{M}_{0}$ is uniformly bounded below zero.
Distributed Multi-Task Relationship Learning
Due to heavy communication caused by transmitting the data and the issue of data privacy and security, it is impossible to send data of different task to a master machine to perform multi-task learning.
