78 citations · 273 across the 16 of their papers we have counts for
6 papers · 1 filter
ProportionNet: Balancing Fairness and Revenue for Auction Design with Deep Learning
Kevin Kuo, Anthony Ostuni, Elizabeth Horishny +5
The design of revenue-maximizing auctions with strong incentive guarantees is a core concern of economic theory. Computational auctions enable online advertising, sourcing, spectru…
WrapNet: Neural Net Inference with Ultra-Low-Resolution Arithmetic
Renkun Ni, Hong-min Chu, Oscar Castañeda +3
Low-resolution neural networks represent both weights and activations with few bits, drastically reducing the multiplication complexity. Nonetheless, these products are accumulated…
Detection as Regression: Certified Object Detection by Median Smoothing
Ping-yeh Chiang, Michael J. Curry, Ahmed Abdelkader +3
Despite the vulnerability of object detectors to adversarial attacks, very few defenses are known to date. While adversarial training can improve the empirical robustness of image…
Certifying Strategyproof Auction Networks
Michael J. Curry, Ping-Yeh Chiang, Tom Goldstein +1
Optimal auctions maximize a seller's expected revenue subject to individual rationality and strategyproofness for the buyers. Myerson's seminal work in 1981 settled the case of auc…
Certified Defenses for Adversarial Patches
Ping-Yeh Chiang, Renkun Ni, Ahmed Abdelkader +3
Adversarial patch attacks are among one of the most practical threat models against real-world computer vision systems. This paper studies certified and empirical defenses against…
Improving the Tightness of Convex Relaxation Bounds for Training Certifiably Robust Classifiers
Chen Zhu, Renkun Ni, Ping-yeh Chiang +3
Convex relaxations are effective for training and certifying neural networks against norm-bounded adversarial attacks, but they leave a large gap between certifiable and empirical…