10 citations · 27 across the 13 of their papers we have counts for
13 papers
Beyond Linear Diffusions: Improved Representations for Rare Conditional Generative Modeling
Kulunu Dharmakeerthi, Yousef El-Laham, Henry H. Wong +3
Diffusion models have emerged as powerful generative frameworks with widespread applications across machine learning and artificial intelligence systems. While current research has…
LSCD: Lomb-Scargle Conditioned Diffusion for Time series Imputation
Elizabeth Fons, Alejandro Sztrajman, Yousef El-Laham +3
Time series with missing or irregularly sampled data are a persistent challenge in machine learning. Many methods operate on the frequency-domain, relying on the Fast Fourier Trans…
Mixup Regularization: A Probabilistic Perspective
Yousef El-Laham, Niccolò Dalmasso, Svitlana Vyetrenko +2
In recent years, mixup regularization has gained popularity as an effective way to improve the generalization performance of deep learning models by training on convex combinations…
Variational Neural Stochastic Differential Equations with Change Points
Yousef El-Laham, Zhongchang Sun, Haibei Zhu +2
In this work, we explore modeling change points in time-series data using neural stochastic differential equations (neural SDEs). We propose a novel model formulation and training…
A Language Model-Guided Framework for Mining Time Series with Distributional Shifts
Haibei Zhu, Yousef El-Laham, Elizabeth Fons +1
Effective utilization of time series data is often constrained by the scarcity of data quantity that reflects complex dynamics, especially under the condition of distributional shi…
Neural Stochastic Differential Equations with Change Points: A Generative Adversarial Approach
Zhongchang Sun, Yousef El-Laham, Svitlana Vyetrenko
Stochastic differential equations (SDEs) have been widely used to model real world random phenomena. Existing works mainly focus on the case where the time series is modeled by a s…
Augment on Manifold: Mixup Regularization with UMAP
Yousef El-Laham, Elizabeth Fons, Dillon Daudert +1
Data augmentation techniques play an important role in enhancing the performance of deep learning models. Despite their proven benefits in computer vision tasks, their application…
Synthetic Data Applications in Finance
Vamsi K. Potluru, Daniel Borrajo, Andrea Coletta +17
Synthetic data has made tremendous strides in various commercial settings including finance, healthcare, and virtual reality. We present a broad overview of prototypical applicatio…
MADS: Modulated Auto-Decoding SIREN for time series imputation
Tom Bamford, Elizabeth Fons, Yousef El-Laham +1
Time series imputation remains a significant challenge across many fields due to the potentially significant variability in the type of data being modelled. Whilst traditional impu…
Deep Gaussian Mixture Ensembles
Yousef El-Laham, Niccolò Dalmasso, Elizabeth Fons +1
This work introduces a novel probabilistic deep learning technique called deep Gaussian mixture ensembles (DGMEs), which enables accurate quantification of both epistemic and aleat…
DSLOB: A Synthetic Limit Order Book Dataset for Benchmarking Forecasting Algorithms under Distributional Shift
Defu Cao, Yousef El-Laham, Loc Trinh +2
In electronic trading markets, limit order books (LOBs) provide information about pending buy/sell orders at various price levels for a given security. Recently, there has been a g…
StyleTime: Style Transfer for Synthetic Time Series Generation
Yousef El-Laham, Svitlana Vyetrenko
Neural style transfer is a powerful computer vision technique that can incorporate the artistic "style" of one image to the "content" of another. The underlying theory behind the a…
HyperTime: Implicit Neural Representation for Time Series
Elizabeth Fons, Alejandro Sztrajman, Yousef El-laham +2
Implicit neural representations (INRs) have recently emerged as a powerful tool that provides an accurate and resolution-independent encoding of data. Their robustness as general a…