30 citations · 42 across the 11 of their papers we have counts for
13 papers
Data-efficient Kernel Methods for Learning Hamiltonian Systems
Yasamin Jalalian, Mostafa Samir, Boumediene Hamzi +2
Hamiltonian dynamics describe a wide range of physical systems. As such, data-driven simulations of Hamiltonian systems are important for many scientific and engineering problems.…
Optimal Stopping with Gaussian Processes
Kshama Dwarakanath, Danial Dervovic, Peyman Tavallali +2
We propose a novel group of Gaussian Process based algorithms for fast approximate optimal stopping of time series with specific applications to financial markets. We show that str…
One-Shot Learning of Stochastic Differential Equations with Data Adapted Kernels
Matthieu Darcy, Boumediene Hamzi, Giulia Livieri +2
We consider the problem of learning Stochastic Differential Equations of the form from one sample trajectory. This problem is more challenging than le…
Uncertainty Quantification of the 4th kind; optimal posterior accuracy-uncertainty tradeoff with the minimum enclosing ball
Hamed Hamze Bajgiran, Pau Batlle Franch, Houman Owhadi +5
There are essentially three kinds of approaches to Uncertainty Quantification (UQ): (A) robust optimization, (B) Bayesian, (C) decision theory. Although (A) is robust, it is unfavo…
Deterministic Iteratively Built KD-Tree with KNN Search for Exact Applications
Aryan Naim, Joseph Bowkett, Sisir Karumanchi +2
K-Nearest Neighbors (KNN) search is a fundamental algorithm in artificial intelligence software with applications in robotics, and autonomous vehicles. These wide-ranging applicati…
Decision Theoretic Bootstrapping
Peyman Tavallali, Hamed Hamze Bajgiran, Danial J. Esaid +1
The design and testing of supervised machine learning models combine two fundamental distributions: (1) the training data distribution (2) the testing data distribution. Although t…
Adversarial Poisoning Attacks and Defense for General Multi-Class Models Based On Synthetic Reduced Nearest Neighbors
Pooya Tavallali, Vahid Behzadan, Peyman Tavallali +1
State-of-the-art machine learning models are vulnerable to data poisoning attacks whose purpose is to undermine the integrity of the model. However, the current literature on data…
Discrete linear-complexity reinforcement learning in continuous action spaces for Q-learning algorithms
Peyman Tavallali, Gary B. Doran, Lukas Mandrake
In this article, we sketch an algorithm that extends the Q-learning algorithms to the continuous action space domain. Our method is based on the discretization of the action space.…
Intrinsic Frequency Analysis and Fast Algorithms
Peyman Tavallali, Hana Koorehdavoudi, Joanna Krupa
Intrinsic Frequency (IF) has recently been introduced as an ample signal processing method for analyzing carotid and aortic pulse pressure tracings. The IF method has also been int…
Parameter Selection Algorithm For Continuous Variables
Peyman Tavallali, Marianne Razavi, Sean Brady
In this article, we propose a new algorithm for supervised learning methods, by which one can both capture the non-linearity in data and also find the best subset model. To produce…
On the non-uniqueness of the instantaneous frequency
Peyman Tavallali, Thomas Y. Hou
In this article, we investigate the debated Instantaneous Frequency (IF) topic. Here, we show that IF is non-unique inherently. We explain how this non-uniqueness can be quantified…
Sparse Time Frequency Representations and Dynamical Systems
T. Y. Hou, Z. Shi, P. Tavallali
In this paper, we establish a connection between the recently developed data-driven time-frequency analysis \cite{HS11,HS13-1} and the classical second order differential equations…
Convergence of a data-driven time-frequency analysis method
Thomas Y. Hou, Zuoqiang Shi, Peyman Tavallali
In a recent paper, Hou and Shi introduced a new adaptive data analysis method to analyze nonlinear and non-stationary data. The main idea is to look for the sparsest representation…