Topic models have been used extensively as a tool for corpus exploration, and a cottage industry has developed to tweak topic models to better encode human intuitions or to better...
Yuening Hu, Jordan L. Boyd-Graber, Brianna Satinof...
Motivated by a machine learning perspective—that gametheoretic equilibria constraints should serve as guidelines for predicting agents’ strategies, we introduce maximum causal...
Traditional approaches to secure computation begin by representing the function f being computed as a circuit. For any function f that depends on each of its inputs, this implies ...
S. Dov Gordon, Jonathan Katz, Vladimir Kolesnikov,...
Since software systems are becoming increasingly more concurrent and distributed, modeling and analysis of interactions among their components is a crucial problem. In several app...
One of the central challenges in reinforcement learning is to balance the exploration/exploitation tradeoff while scaling up to large problems. Although model-based reinforcement ...