Campuses:

machine learning

Friday, June 21, 2019 - 11:10am - 12:00pm
Steven Wu (University of Minnesota, Twin Cities)
We study the prediction of a real-valued target, such as a risk score or recidivism rate, while guaranteeing a quantitative notion of fairness with respect to a protected attribute such as gender or race. We call this class of problems fair regression.
Monday, April 22, 2019 - 1:25pm - 2:25pm
Tom Goldstein (University of Maryland)
Neural networks solve complex computer vision problems with human-like accuracy. However, it has recently been observed that neural nets are easily fooled and manipulated by adversarial examples, in which an attacker manipulates the network by making tiny changes to its inputs. In this talk, I give a high-level overview of adversarial examples, and then discuss a newer type of attack called data poisoning, in which a network is manipulated at train time rather than test time.
Wednesday, November 7, 2018 - 4:30pm - 5:00pm
Yuanjia Wang (Columbia University)
Current guidelines for treatment decision making largely rely on data from ran- domized controlled trials (RCTs) studying average treatment effects. They may be inadequate to make individualized treatment decisions in real-world settings. Large- scale electronic health records (EHR) provide opportunities to fulfill the goals of personalized medicine and learn individualized treatment rules (ITRs) depending on patient-specific characteristics from real-world patient data.
Thursday, October 4, 2018 - 11:00am - 11:45am
Adam Elmachtoub (Columbia University)
Many real-world analytics problems involve two significant challenges: prediction and optimization. Due to the typically complex nature of each challenge, the standard paradigm is to predict, then optimize. By and large, machine learning tools are intended to minimize prediction error and do not account for how the predictions will be used in a downstream optimization problem.
Wednesday, October 3, 2018 - 2:00pm - 2:45pm
Hamsa Bastani (Wharton School of the University of Pennsylvania)
Predictive analytics is increasingly used to guide decision-making in many applications. However, in practice, we often have limited data on the true outcome that we wish to predict, but copious data on an intermediate or proxy outcome. Practitioners often train predictive models on proxies since it achieves more accurate predictions.
Wednesday, April 25, 2018 - 1:30pm - 2:00pm
Brian Reich (North Carolina State University)
Forensic analyses are often concerned with identifying the spatial source of biological residue. Using recent advances in high-throughput sequencing technologies, dust collected from nearly any object can be shown to harbor DNA fragments from thousands of bacteria and fungi species which may be informative of the source of the dust. We show that training collections of deep neural network classifiers on random Voronoi partitions of a spatial domain yields remarkably accurate geolocation predictions.
Saturday, September 16, 2017 - 11:20am - 11:50am
Yi-Hsiang Hsu (Harvard Medical School)
The fast moving of the cancer immunotherapy field has generated tremendous excitement regarding new therapeutic strategies and will likely change the paradigm of therapeutic interventions for cancer. Neoantigens, generated by tumor-specific DNA alterations that result in the formation of novel protein sequences and only in cancer cells, represent an optimal target for the immune system and make possible a new class of highly personalized vaccines with the potential for significant efficacy with reduced side effects.
Friday, September 15, 2017 - 9:00am - 10:00am
Michael Kosorok (University of North Carolina, Chapel Hill)
Estimating individualized treatment rules is a central task of personalized or precision medicine. In this presentation, we review several new developments in outcome weighted learning for identifying individualized treatment rules for two challenging settings: dose finding and treatment selection under right censoring. In the former, we develop an approach which involves the use of two different kernels; and in the later, we use random forests to address censoring before applying outcome weighted learning.
Monday, February 22, 2016 - 1:15pm - 2:00pm
Stephen Wright (University of Wisconsin, Madison)
We survey some developments in machine learning and data analysis,
focusing on those in which optimization is an important
component. Some of these have possible relevance for industrial and
energy applications, for example, constraints and covariances could be
learned from process data rather than specified a priori. Some
possibilities along these lines will be proposed.
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