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Found 11 papers, 3 papers with code
Kernel Aware Resampler
This renders our model applicable for different types of data not seen during the training such as normals.
Generalization Comparison of Deep Neural Networks via Output Sensitivity
We find a rather strong empirical relation between the output sensitivity and the variance in the bias-variance decomposition of the loss function, which hints on using sensitivity as a metric for comparing the generalization performance of networks, without requiring labeled data.
Learning Hawkes Processes from a Handful of Events
It is also able to take into account the uncertainty in the model parameters by learning a posterior distribution over them.
On the Reflection of Sensitivity in the Generalization Error
We find a rather strong empirical relation between the output sensitivity and the variance in the bias-variance decomposition of the loss function, which hints on using sensitivity as a metric for comparing generalization performance of networks, without requiring labeled data.
Augmenting and Tuning Knowledge Graph Embeddings
Knowledge graph embeddings rank among the most successful methods for link prediction in knowledge graphs, i. e., the task of completing an incomplete collection of relational facts.
Ranked #5 on
Link Prediction
on FB15k
Probabilistic Knowledge Graph Embeddings
We develop a probabilistic extension of state-of-the-art embedding models for link prediction in relational knowledge graphs.
An Algorithmic Framework to Control Bias in Bandit-based Personalization
Personalization is pervasive in the online space as it leads to higher efficiency and revenue by allowing the most relevant content to be served to each user.
Coordinate Descent with Bandit Sampling
Ideally, we would update the decision variable that yields the largest decrease in the cost function.
Stochastic Optimization with Bandit Sampling
This approach for sampling datapoints is general, and can be used in conjunction with any algorithm that uses an unbiased gradient estimation -- we expect it to have broad applicability beyond the specific examples explored in this work.
Dictionary Learning Based on Sparse Distribution Tomography
We propose a new statistical dictionary learning algorithm for sparse signals that is based on an $\alpha$-stable innovation model.
Lean From Thy Neighbor: Stochastic & Adversarial Bandits in a Network
We provide algorithms for this setting, both for stochastic and adversarial bandits, and show that their regret smoothly interpolates between the regret in the classical bandit setting and that of the full-information setting as a function of the neighbors' exploration.
