Vitercik

Ellen Vitercik - Leveraging Reviews: Learning to Price with Buyer and Seller Uncertainty

Institute for Pure & Applied Mathematics (IPAM)

Ellen Vitercik: "How much data is sufficient to learn high-performing algorithms?"

Institute for Pure & Applied Mathematics (IPAM)

M4LA 2021 - Ellen Vitercik - How much data is sufficient to learn high-performing algorithms?

Ellen Vitercik on Estimating approximate incentive compatibility

Estimating Approximate Incentive Compatibility - Ellen Vitercik

Ellen Vitercik on "Sample Complexity of Revenue Maximization"

Ellen Vitercik on Differentially Private Algorithm and Auction Configuration

Theoretical Foundations of Data-Driven Algorithm Design

Simons Institute

Mechanism Design Via Machine Learning: Overfitting, Incentives, and Privacy

Simons Institute

Machine Learning For Algorithm Design

Simons Institute

Learning Combinatorial Functions from Pairwise Comparisons

COLT

EC'22: Automated Algorithm and Mechanism Configuration

EC'18: A General Theory of Sample Complexity for Multi-Item Profit Maximization

Sample Complexity of Automated Mechanism Design

Machine Learning in Automated Mechanism Design for Pricing and Auctions (ICML 2018 tutorial)

Steven Van Vaerenbergh

Refined Bounds for Algorithm Configuration: The Knife-edge of Dual Class Approximability

Machine Learning for Pricing & Auctions - Tutorial ICML2018

The Artificial Intelligence Channel

EC'19: Estimating Approximate Incentive Compatibility

EC'19 Tutorial: New Frontiers of Automated Mechanism Design for Pricing and Auctions (Part 1)