136 citations · 171 across the 6 of their papers we have counts for
6 papers
Deep Reinforcement Learning for Dexterous Manipulation with Concept Networks
Aditya Gudimella, Ross Story, Matineh Shaker +4
Deep reinforcement learning yields great results for a large array of problems, but models are generally retrained anew for each new problem to be solved. Prior learning and knowle…
Peer Prediction with Heterogeneous Tasks
Debmalya Mandal, Matthew Leifer, David C. Parkes +2
Peer prediction is a method to promote contributions of information by users in settings in which there is no way to verify the quality of responses. In multi-task peer prediction,…
Informed Truthfulness in Multi-Task Peer Prediction
Victor Shnayder, Arpit Agarwal, Rafael Frongillo +1
The problem of peer prediction is to elicit information from agents in settings without any objective ground truth against which to score reports. Peer prediction mechanisms seek t…
Truthful Prioritization Schemes for Spectrum Sharing
Victor Shnayder, David C. Parkes, Vikas Kawadia +1
We design a protocol for dynamic prioritization of data on shared routers such as untethered 3G/4G devices. The mechanism prioritizes bandwidth in favor of users with the highest v…
Combining Independent Modules in Lexical Multiple-Choice Problems
Peter D. Turney, Michael L. Littman, Jeffrey Bigham +1
Existing statistical approaches to natural language problems are very coarse approximations to the true complexity of language processing. As such, no single technique will be best…
Combining Independent Modules to Solve Multiple-choice Synonym and Analogy Problems
Peter D. Turney, Michael L. Littman, Jeffrey Bigham +1
Existing statistical approaches to natural language problems are very coarse approximations to the true complexity of language processing. As such, no single technique will be best…