Risk-averse optimization plays a major role in the design of safety for machine learning applications. In this talk, we will present a set of tools to enhance the robustness of models and algorithms to potentially harmful data shifts.

Lunch from 12:15 p.m., RSVP to [email protected]

In the context of first-order algorithms subject to random gradient noise, we study the trade-offs between the convergence rate (which quantifies how fast the initial conditions are forgotten) and the "risk" of suboptimality, i.e., deviations from the expected suboptimality.

Lunch from 12:15 p.m., RSVP to [email protected]

Sparse statistical estimators are increasingly prevalent due to their ease of interpretability and superior out-of-sample performance. However, sparse estimation problems with an L0 constraint, restricting the support of the estimators, are challenging (typically NP-hard, but not always) non-convex optimization problems.

The trend of building ever larger language models has dominated much research in NLP over the last few years. In this talk, I will discuss our recent efforts to (at least partially) answer two key questions in this area: Will we be able to keep scaling? And, how will we actually use the models, if we do?

Earth’s climate is chaotic and noisy. Finding usable signals amidst all of the noise can be challenging. Here, I will demonstrate how explainable artificial intelligence (XAI) techniques can sift through vast amounts of climate data and push the bounds of scientific discovery. Examples include extracting robust indicator patterns of climate…

In this talk, we provide an overview of recent advances in data-driven modeling and control of human-water-energy systems and showcase how Machine Learning techniques can help (i) infer natural and anthropogenic drivers of observed hydroclimatic patterns and improve their predictability in space and time, (ii) understand and conceptualize the mutual influences between human behaviors and water-energy systems; and (iii) design strategic planning and management policies optimizing multiple and conflicting objectives with different dynamics and informed by heterogeneous information. 

The plane and space groups are groups that specify how to tile two- or three-dimensional Euclidean space with a shape: They enumerate all possible ways in which a shape can be isometrically replicated across the space. I will describe how to explicitly compute approximate eigenfunctions of the Laplace-Beltrami operator on the orbifold defined by any such group.

Multi-site studies are common, and intervention effect estimates frequently differ across sites. We discuss reasons why effect estimates may differ across sites and relate these to transportability. In scenarios where transport is possible, we develop transport estimators to better understand why intervention effects differ across sites/population.

Shifting attachments to social groups are a constant in the modern era.They are especially pronounced in the contemporary workplace. What accounts for variation in the strength of organizational identification?

Suppose you observe very few entries from a large matrix. Can we predict the missing entries, say assuming the matrix is (approximately) low rank ? We describe a very simple method to solve this matrix completion problem. We show our method is able to recover matrices from very few entries and/or with ill conditioned matrices, where many other popular methods fail.