Learning algorithms have become an integral component to modern engineering solutions.
Examples range from self-driving cars and recommender systems to finance and even …

We study the problem of system identification and adaptive control in partially observable
linear dynamical systems. Adaptive and closed-loop system identification is a challenging …

We consider the problem of estimating a linear time-invariant (LTI) dynamical system from a
single trajectory via streaming algorithms, which is encountered in several applications …

In this article, we consider the problem of predicting observations generated online by an
unknown, partially observable linear system, which is driven by Gaussian noise. In the linear …

Linear dynamical systems are the foundational statistical model upon which control theory is
built. Both the celebrated Kalman filter and the linear quadratic regulator require knowledge …

We develop algorithms for online linear regression which achieve optimal static and
dynamic regret guarantees\emph {even in the complete absence of prior knowledge}. We …

Identification of a linear time-invariant dynamical system from partial observations is a
fundamental problem in control theory. Particularly challenging are systems exhibiting long …

We present an efficient and practical (polynomial time) algorithm for online prediction in
unknown and partially observed linear dynamical systems (LDS) under stochastic noise …

This work studies training instabilities of behavior cloning with deep neural networks. We
observe that minibatch SGD updates to the policy network during training result in sharp …

Observations from dynamical systems often exhibit irregularities, such as censoring, where
values are recorded only if they fall within a certain range. Censoring is ubiquitous in …