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07/24/2023
Extending Path-Dependent NJ-ODEs to Noisy Observations and a Dependent Observation Framework
The Path-Dependent Neural Jump ODE (PD-NJ-ODE) is a model for predicting...
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06/05/2023
Global universal approximation of functional input maps on weighted spaces
We introduce so-called functional input neural networks defined on a pos...
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03/20/2023
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 lay...
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06/28/2022
Optimal Estimation of Generic Dynamics by Path-Dependent Neural Jump ODEs
This paper studies the problem of forecasting general stochastic process...
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01/07/2022
Applications of Signature Methods to Market Anomaly Detection
Anomaly detection is the process of identifying abnormal instances or ev...
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01/02/2022
Randomized Signature Layers for Signal Extraction in Time Series Data
Time series analysis is a widespread task in Natural Sciences, Social Sc...
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12/31/2021
Infinite width (finite depth) neural networks benefit from multi-task learning unlike shallow Gaussian Processes – an exact quantitative macroscopic characterization
We prove in this paper that optimizing wide ReLU neural networks (NNs) w...
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04/28/2021
Optimal Stopping via Randomized Neural Networks
This paper presents new machine learning approaches to approximate the s...
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02/26/2021
NOMU: Neural Optimization-based Model Uncertainty
We introduce a new approach for capturing model uncertainty for neural n...
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02/03/2021
Deep Hedging under Rough Volatility
We investigate the performance of the Deep Hedging framework under train...
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09/17/2020
Discrete-time signatures and randomness in reservoir computing
A new explanation of geometric nature of the reservoir computing phenome...
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06/16/2020
Consistent Recalibration Models and Deep Calibration
Consistent Recalibration models (CRC) have been introduced to capture in...
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06/08/2020
Theoretical Guarantees for Learning Conditional Expectation using Controlled ODE-RNN
Continuous stochastic processes are widely used to model time series tha...
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05/05/2020
A generative adversarial network approach to calibration of local stochastic volatility models
We propose a fully data driven approach to calibrate local stochastic vo...
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04/28/2020
Denise: Deep Learning based Robust PCA for Positive Semidefinite Matrices
We introduce Denise, a deep learning based algorithm for decomposing pos...
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04/27/2020
Estimating Full Lipschitz Constants of Deep Neural Networks
We estimate the Lipschitz constants of the gradient of a deep neural net...
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11/07/2019
