Unifying lower bounds on prediction dimension of convex surrogates
Talk, Neural Information Processing Systems 2021, Virtual
Talks and presentations
Fairness and Discrimination through the Dual Lens of Mechanism Design and Machine Learning
Tutorial, ACM Conference on Economics and Computation, Budapest, Hungary
Designing surrogate loss functions for general prediction tasks
Talk, Qualifying PhD Exam, Boulder, CO
Property Elicitation as a tool for understanding Consistent Polyhedral Losses
Presentation, RIKEN AIP Learning Theory Seminar, Tokyo, Japan/Virtual
Fairness and Discrimination in Mechanism Design and Machine Learning
Talk, AI for Social Good (AI4SG) Workshop at Harvard CRCS 2020, Virtual
Embedding Dimension of Polyhedral Losses
Presentation, Conference on Learning Theory (COLT) 2020, Virtual
Calibrated Surrogates for the Abstain Property
Poster, CU Boulder Institute of Cognitive Science , Boulder, Colorado
An Embedding Framework for Consistent Polyhedral Surrogates
Poster, Neural Information Processing Systems 2019, Vancouver, Canada
Calibration of Convex Surrogate Losses via Property Elicitation
Talk, Preliminary PhD Exam, Boulder, CO
Convex Elicitation of Continuous Properties
Talk, Neural Information Processing Systems 2018, Montreal, CA
Convex Elicitation of Continuous Properties
Talk, 2018 ACM Conference on Economics and Computation, Ithaca, NY