Upcoming Seminars

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Previous Seminars

Bayesian Risk Optimization (BRO): A New Approach to Data-driven Stochastic Optimization

Wed, Dec 1, 2021, 4:30 pm
A large class of stochastic optimization problems involves optimizing an expectation taken with respect to an underlying distribution that is unknown in practice. One popular approach to addressing the distributional uncertainty, known as the distributionally robust optimization (DRO), is to hedge against the worst case among an ambiguity set of...

MCMC vs. Variational Inference -- for Credible Learning and Decision-Making at Scale

Tue, Nov 23, 2021, 4:30 pm
I will introduce some recent progress towards understanding the scalability of Markov chain Monte Carlo (MCMC) methods and their comparative advantage with respect to variational inference. I will discuss an optimization perspective on the infinite dimensional probability space, where MCMC leverages stochastic sample paths while variational...

Optimal No-Regret Learning in Repeated First-Price Auctions

Wed, Nov 17, 2021, 4:30 pm
First-price auctions have very recently swept the online advertising industry, replacing second-price auctions as the predominant auction mechanism on many platforms for display ads bidding. This shift has brought forth important challenges for a bidder: how should one bid in a first-price auction, where unlike in second-price auctions, it is no...

CITP Seminar: Matt Weinberg – A Crash Course on Algorithmic Mechanism Design

Tue, Nov 16, 2021, 12:30 pm
Matt is an assistant professor at Princeton University in the Department of Computer Science. His primary research interest is in Algorithmic Mechanism Design: algorithm design in settings where users have their own incentives. He is also interested more broadly in Algorithmic Game Theory, Algorithms Under Uncertainty, and Theoretical Computer...

From Shallow to Deep Representation Learning: Global Nonconvex Theory and Algorithms

Fri, Nov 12, 2021, 4:30 pm
In this talk, we consider two fundamental problems in signal processing and machine learning: (convolutional) dictionary learning and deep network training. For both problems, we provide the first global nonconvex landscape analysis of the learned representations, which will in turn provide new guiding principles for better model/architecture...

CITP Seminar: Alex Hanna – Beyond Bias: Algorithmic Unfairness, Infrastructure, and Genealogies of Data

Tue, Nov 9, 2021, 12:30 pm
Problems of algorithmic bias are often framed in terms of lack of representative data or formal fairness optimization constraints to be applied to automated decision-making systems. However, these discussions sidestep deeper issues with data used in AI, including problematic categorizations and the extractive logics of crowdwork and data mining....

Pranay Anchuri – Insights into Predictability of Life Outcomes: A Data-Driven Approach

Tue, Oct 26, 2021, 12:30 pm to 1:30 pm

Predicting life outcomes is a challenging task even for advanced machine learning (ML) algorithms. At the same time, accurately predicting these outcomes has important implications in providing targeted assistance and in improving policy making.


Bridging the Gap Between AI and Clinical Neuroscience via Deep-Generative Fusion Models

Thu, Oct 14, 2021, 4:30 pm
Deep learning has disrupted nearly every major field of study from computer vision to genomics. The unparalleled success of these models has, in many cases, been fueled by an explosion of data. Millions of labeled images, thousands of annotated ICU admissions, and hundreds of hours of transcribed speech are common standards in the literature....

Quantitative Social Science Colloquium: Measuring Housing Activeness from Multi-source Big Data and Machine Learning

Fri, Oct 8, 2021, 11:30 am
Measuring timely high-resolution socioeconomic outcomes is critical for policy-making and evaluation, but hard to reliably obtain. With the help of machine learning and cheaply available data such as social media and nightlight, it is now possible to predict such indices in fine granularity. This paper demonstrates an adaptive way to measure the...

Optimization Theory on Model-Agnostic Meta-Learning

Wed, Oct 6, 2021, 4:30 pm to 6:00 pm
Meta-learning or learning to learn has been shown to be a powerful tool for fast learning over unseen tasks by efficiently extracting the knowledge from a range of observed tasks. Such empirical success thus highly motivates theoretical understanding of the performance guarantee of meta-learning, which will serve to guide the better design of meta...