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Found 8 papers, 6 papers with code
Machine Learning-Powered Combinatorial Clock Auction
In this paper, we address this shortcoming by designing an ML-powered combinatorial clock auction that elicits information from the bidders only via demand queries (i. e., ``At prices $p$, what is your most preferred bundle of items?'').
Extending Path-Dependent NJ-ODEs to Noisy Observations and a Dependent Observation Framework
The Path-Dependent Neural Jump Ordinary Differential Equation (PD-NJ-ODE) is a model for predicting continuous-time stochastic processes with irregular and incomplete observations.
How (Implicit) Regularization of ReLU Neural Networks Characterizes the Learned Function -- Part II: the Multi-D Case of Two Layers with Random First Layer
Randomized neural networks (randomized NNs), where only the terminal layer's weights are optimized constitute a powerful model class to reduce computational time in training the neural network model.
Bayesian Optimization-based Combinatorial Assignment
In this paper, we address this shortcoming by presenting a Bayesian optimization-based combinatorial assignment (BOCA) mechanism.
How Infinitely Wide Neural Networks Can Benefit from Multi-task Learning -- an Exact Macroscopic Characterization
In practice, multi-task learning (through learning features shared among tasks) is an essential property of deep neural networks (NNs).
NOMU: Neural Optimization-based Model Uncertainty
To isolate the effect of model uncertainty, we focus on a noiseless setting with scarce training data.
Reducing the number of neurons of Deep ReLU Networks based on the current theory of Regularization
We introduce a new Reduction Algorithm which makes use of the properties of ReLU neurons to reduce significantly the number of neurons in a trained Deep Neural Network.
How Implicit Regularization of ReLU Neural Networks Characterizes the Learned Function -- Part I: the 1-D Case of Two Layers with Random First Layer
In this paper, we consider one dimensional (shallow) ReLU neural networks in which weights are chosen randomly and only the terminal layer is trained.
