Search Results for author: Scott Duke Kominers

Found 6 papers, 2 papers with code

Shill-Proof Auctions

no code implementations30 Mar 2024 Andrew Komo, Scott Duke Kominers, Tim Roughgarden

The Dutch auction (with suitable reserve) is the unique optimal and strongly shill-proof auction.

The Harvard USPTO Patent Dataset: A Large-Scale, Well-Structured, and Multi-Purpose Corpus of Patent Applications

1 code implementation NeurIPS 2023 Mirac Suzgun, Luke Melas-Kyriazi, Suproteem K. Sarkar, Scott Duke Kominers, Stuart M. Shieber

Innovation is a major driver of economic and social development, and information about many kinds of innovation is embedded in semi-structured data from patents and patent applications.

Binary Classification Language Modelling +1

Recommending with Recommendations

no code implementations2 Dec 2021 Naveen Durvasula, Franklyn Wang, Scott Duke Kominers

In our setting, the user's (potentially sensitive) information belongs to a high-dimensional latent space, and the ideal recommendations for the source and target tasks (which are non-sensitive) are given by unknown linear transformations of the user information.

Recommendation Systems

Generalization by Recognizing Confusion

1 code implementation13 Jun 2020 Daniel Chiu, Franklyn Wang, Scott Duke Kominers

A recently-proposed technique called self-adaptive training augments modern neural networks by allowing them to adjust training labels on the fly, to avoid overfitting to samples that may be mislabeled or otherwise non-representative.

Smarter Parking: Using AI to Identify Parking Inefficiencies in Vancouver

no code implementations21 Mar 2020 Devon Graham, Satish Kumar Sarraf, Taylor Lundy, Ali MohammadMehr, Sara Uppal, Tae Yoon Lee, Hedayat Zarkoob, Scott Duke Kominers, Kevin Leyton-Brown

To see where this might be true in downtown Vancouver, we used artificial intelligence techniques to estimate the amount of time it would take drivers to both park on and off street for destinations throughout the city.

Ridesharing with Driver Location Preferences

no code implementations30 May 2019 Duncan Rheingans-Yoo, Scott Duke Kominers, Hongyao Ma, David C. Parkes

We study revenue-optimal pricing and driver compensation in ridesharing platforms when drivers have heterogeneous preferences over locations.

Multiagent Systems Computer Science and Game Theory

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