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Found 12 papers, 0 papers with code
Meta Temporal Point Processes
In this work, we propose to train TPPs in a meta learning framework, where each sequence is treated as a different task, via a novel framing of TPPs as neural processes (NPs).
Gumbel-Softmax Selective Networks
This commonly encountered operational context calls for principled techniques for training ML models with the option to abstain from predicting when uncertain.
Latent Bottlenecked Attentive Neural Processes
We demonstrate that LBANPs can trade-off the computational cost and performance according to the number of latent vectors.
Towards Better Selective Classification
We tackle the problem of Selective Classification where the objective is to achieve the best performance on a predetermined ratio (coverage) of the dataset.
Monotonicity Regularization: Improved Penalties and Novel Applications to Disentangled Representation Learning and Robust Classification
We study settings where gradient penalties are used alongside risk minimization with the goal of obtaining predictors satisfying different notions of monotonicity.
Monotonicity as a requirement and as a regularizer: efficient methods and applications
We study the setting where risk minimization is performed over general classes of models and consider two cases where monotonicity is treated as either a requirement to be satisfied everywhere or a useful property.
Point Process Flows
Event sequences can be modeled by temporal point processes (TPPs) to capture their asynchronous and probabilistic nature.
Human Intracranial EEG Quantitative Analysis and Automatic Feature Learning for Epileptic Seizure Prediction
2) The traditional PCA is not a reliable method for iEEG data reduction in seizure prediction.
Combining Bayesian Optimization and Lipschitz Optimization
Indeed, in a particular setting, we prove that using the Lipschitz information yields the same or a better bound on the regret compared to using Bayesian optimization on its own.
StopWasting My Gradients: Practical SVRG
We present and analyze several strategies for improving the performance ofstochastic variance-reduced gradient (SVRG) methods.
Stop Wasting My Gradients: Practical SVRG
We present and analyze several strategies for improving the performance of stochastic variance-reduced gradient (SVRG) methods.
Non-Uniform Stochastic Average Gradient Method for Training Conditional Random Fields
We apply stochastic average gradient (SAG) algorithms for training conditional random fields (CRFs).
