6 papers
Sparse Koopman Autoencoders Identify Local Dynamical Regimes in Multibasin Systems
Aidan Li, Uday Kiran Reddy Tadipatri, Mahan Fathi +2
Koopman autoencoders (KAEs) seek a higher-dimensional latent representation in which nonlinear dynamics evolve linearly. However, many interesting systems have multiple basins of a…
Recovery Guarantees for Continual Learning of Dependent Tasks: Memory, Data-Dependent Regularization, and Data-Dependent Weights
Liangzu Peng, Uday Kiran Reddy Tadipatri, Ziqing Xu +2
Continual learning (CL) is concerned with learning multiple tasks sequentially without forgetting previously learned tasks. Despite substantial empirical advances over recent years…
BLISS: Global Blind Identification of Linear Systems with Sparse Inputs
Kyle Poe, Uday Kiran Reddy Tadipatri, Benjamin D. Haeffele +1
Linear system identification and sparse dictionary learning can both be seen as structured matrix factorization problems. However, these two problems have historically been studied…
Nonconvex Linear System Identification with Minimal State Representation
Uday Kiran Reddy Tadipatri, Benjamin D. Haeffele, Joshua Agterberg +2
Low-order linear System IDentification (SysID) addresses the challenge of estimating the parameters of a linear dynamical system from finite samples of observations and control inp…
A Convex Relaxation Approach to Generalization Analysis for Parallel Positively Homogeneous Networks
Uday Kiran Reddy Tadipatri, Benjamin D. Haeffele, Joshua Agterberg +1
We propose a general framework for deriving generalization bounds for parallel positively homogeneous neural networks--a class of neural networks whose input-output map decomposes…
Convergence of the Stochastic Heavy Ball Method With Approximate Gradients and/or Block Updating
Uday Kiran Reddy Tadipatri, Mathukumalli Vidyasagar
In this paper, we establish the convergence of the stochastic Heavy Ball (SHB) algorithm under more general conditions than in the current literature. Specifically, (i) The stochas…