Learning Linear Dynamical Systems via Spectral Filtering

We present an efficient and practical algorithm for the online prediction of discrete-time linear dynamical systems with a symmetric transition matrix. We circumvent the non-convex optimization problem using improper learning: carefully overparameterize the class of LDSs by a polylogarithmic factor, in exchange for convexity of the loss functions. From this arises a polynomial-time algorithm with a near-optimal regret guarantee, with an analogous sample complexity bound for agnostic learning. Our algorithm is based on a novel filtering technique, which may be of independent interest: we convolve the time series with the eigenvectors of a certain Hankel matrix.Comment: Published as a conference paper at NIPS 201

PREDICTING PARALLEL APPLICATION PERFORMANCE VIA MACHINE LEARNING APPROACHES

Consistently growing architectural complexity and machine scales make creating accurate performance models for large-scale applications increasingly challenging. Traditional analytic models are difficult and time-consuming to construct, and are often unable to capture full system and application complexity. To address these challenges, we automatically build models based on execution samples. We use multilayer neural networks, since they can represent arbitrary functions and handle noisy inputs robustly. In this thesis, we focus on two well known parallel applications whose variations in execution times are not well understood: SMG2000, a semicoarsening multigrid solver, and HPL, an open source implementation of LINPACK. We sparsely sample performance data on two radically different platforms across large, multi-dimensional parameter spaces and show that our models based on this data can predict performance within 2% to 7% of actual application runtimes.National Science Foundation Grant Number CCF-0444413; United States Department of Energy Grant Number W-7405-Eng-4

Nets of Maya: Gorakhnath as a Trickster Saint in the Folktale of Raja Bharthari and Gopi Chand

In this paper I explore Gorakhnath as a trickster hero in the North Indian folklore of Raja Bharthari and Gopi Chand. Gorakhnath—a popular yogi figure in many folklores—creates, through his traversal of rigid structural boundaries between social and religious delimitations, a new idiom of social and religious acceptance that results in an acceptance of a higher metaphysical positioning. He holds a unique space in folk imagination as a figure who combines an earthly existence with a saintly core, unveiling nets of illusion and revealing essential unity in dichotomous divisions between entities such as body/soul, sacred/ profane and animate/inanimate