In order to advance action generation and creation in robots beyond simple learned schemas we need computational tools that allow us to automatically interpret and represent human...
In a classic result in the mechanism design literature, Cremer and McLean (1985) show that if buyers’ valuations are sufficiently correlated, a mechanism exists that allows the...
In this paper we present a plan-plan distance metric based on Kolmogorov (Algorithmic) complexity. Generating diverse sets of plans is useful for tasks such as probing user prefer...
Study of the human brain through fMRI can potentially benefit the pursuit of artificial intelligence. Four examples are presented. First, fMRI decoding of the brain activity of ...
In online bandit learning, the learner aims to minimize a sequence of losses, while only observing the value of each loss at a single point. Although various algorithms and theori...
Lijun Zhang 0005, Tianbao Yang, Rong Jin, Zhi-Hua ...
The majority of machine learning research has been focused on building models and inference techniques with sound mathematical properties and cutting edge performance. Little atte...
Been Kim, Kayur Patel, Afshin Rostamizadeh, Julie ...
In the proposed thesis, we study Distributed Constraint Optimization Problems (DCOPs), which are problems where several agents coordinate with each other to optimize a global cost...
Unlike unsupervised approaches such as autoencoders that learn to reconstruct their inputs, this paper introduces an alternative approach to unsupervised feature learning called d...
Paul A. Szerlip, Gregory Morse, Justin K. Pugh, Ke...
Heuristic search is a state-of-the-art approach to classical planning. Several heuristic families were developed over the years to automatically estimate goal distance information...
Alexander Shleyfman, Michael Katz 0001, Malte Helm...