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Found 7 papers, 1 papers with code
GARHCX-NoVaS: A Model-free Approach to Incorporate Exogenous Variables
In this work, we further explore the forecasting ability of a recently proposed normalizing and variance-stabilizing (NoVaS) transformation after wrapping exogenous variables.
An NLP-Assisted Bayesian Time Series Analysis for Prevalence of Twitter Cyberbullying During the COVID-19 Pandemic
COVID-19 has brought about many changes in social dynamics.
Towards Size-Independent Generalization Bounds for Deep Operator Nets
In recent times machine learning methods have made significant advances in becoming a useful tool for analyzing physical systems.
An Empirical Study of the Occurrence of Heavy-Tails in Training a ReLU Gate
while training a $\relu$ gate (in the realizable and in the binary classification setup) and for a variant of S. G. D.
Long-term prediction intervals with many covariates
After validating our approach using simulations we also propose a novel bootstrap based method that can boost the coverage of the theoretical intervals.
Prediction Intervals
Time Series Analysis
Methodology
Econometrics
Statistics Theory
Statistics Theory
Provable Training of a ReLU Gate with an Iterative Non-Gradient Algorithm
In this work, we demonstrate provable guarantees on the training of a single ReLU gate in hitherto unexplored regimes.
Depth-2 Neural Networks Under a Data-Poisoning Attack
In this class of networks, we attempt to learn the network weights in the presence of a malicious oracle doing stochastic, bounded and additive adversarial distortions on the true output during training.
