In this talk, we present recent progress towards developing learning-based control strategies for the design of safe and robust autonomous systems. Our approach is to recognize that machine learning algorithms produce inherently uncertain estimates or predictions, and that this uncertainty must be explicitly quantified (e.g., using non-asymptotic guarantees of contemporary …
The bulk of research in psychology and neuroscience has focused on studying processes within single individuals. However, outside of the laboratory people rarely operate independently but rather in the context of a complex constellation of social relationships. Social interactions form a critical aspect of the human experience from romantic relationships, …
In many limited budget experimental settings, such as A/B testing or Protein design, there is a need for adaptive sampling to guide the discovery of as many true positives as possible subject to a low rate of false discoveries (i.e. false alarms). Like active learning for binary classification, this experimental …
The computing industry has a power problem: the days of ideal transistor scaling are over, and chips now have more devices than can be fully powered simultaneously, limiting performance. New architecture-level solutions are needed to continue scaling performance, and specialized hardware accelerators are one such solution. While accelerators promise to …
Learning algorithms typically assume their input data is good natured. If one takes this input data and trains an agent with it, then the agent should, given enough time and compute, eventually learn how to solve the intended task. But this is not always a realistic expectation. Sometimes, the data …
We address the problem of identifying the structure of an undirected graph from the observation of signals defined on its nodes. Fundamentally, the unknown graph encodes direct relationships between signal elements, which we aim to recover from observable indirect relationships generated by a diffusion process on the graph. Our approach …
Is it possible to learn the laws of music theory directly from raw sheet music in the same human-interpretable form as a music theory textbook? How little prior knowledge needs to be encoded to do so? We consider these and similar questions in other topical domains, in developing a general …
Accurate and efficient reduced-order models are essential to understand, predict, estimate, and control complex, multiscale, and nonlinear dynamical systems. These models should ideally be generalizable, interpretable, and based on limited training data. This work develops a general framework to discover the governing equations underlying a dynamical system simply from data …
Data collection and sharing are pervasive aspects of modern society. This process can either be voluntary, as in the case of a person taking a facial image to unlock his/her phone, or inciden-tal, such as traf?c cameras collecting videos on pedestrians. An undesirable side effect of these processes is that …
Traditional multistage stochastic optimization assumes the underlying probability distribution is known. However, in practice, the probability distribution is often not known or cannot be accurately approximated. One way to address such distributional ambiguity is to use distributionally robust optimization (DRO), which minimizes the worst-case expected cost with respect to a …