59 citations · 61 across the 4 of their papers we have counts for
4 papers
The Ladder: A Reliable Leaderboard for Machine Learning Competitions
Avrim Blum, Moritz Hardt
The organizer of a machine learning competition faces the problem of maintaining an accurate leaderboard that faithfully represents the quality of the best submission of each compe…
Learning What's going on: reconstructing preferences and priorities from opaque transactions
Avrim Blum, Yishay Mansour, Jamie Morgenstern
We consider a setting where buyers, with combinatorial preferences over items, and a seller, running a priority-based allocation mechanism, repeatedly interact. Our goal, f…
Learning Valuation Distributions from Partial Observation
Avrim Blum, Yishay Mansour, Jamie Morgenstern
Auction theory traditionally assumes that bidders' valuation distributions are known to the auctioneer, such as in the celebrated, revenue-optimal Myerson auction. However, this th…
The Johnson-Lindenstrauss Transform Itself Preserves Differential Privacy
Jeremiah Blocki, Avrim Blum, Anupam Datta +1
This paper proves that an "old dog", namely -- the classical Johnson-Lindenstrauss transform, "performs new tricks" -- it gives a novel way of preserving differential privacy. We s…