Many structured prediction tasks involve complex models where inference is computationally intractable, but where it can be well approximated using a linear programming relaxation...
Ofer Meshi, David Sontag, Tommi Jaakkola, Amir Glo...
In Smart Spaces (SSs), the capability of learning from experience is fundamental for autonomous adaptation to environmental changes and for proactive interaction with users. New re...
Alessio Dore, Andrea F. Cattoni, Carlo S. Regazzon...
We propose a new approach to value function approximation which combines linear temporal difference reinforcement learning with subspace identification. In practical applications...
Abstract. The Bayesian approach to machine learning amounts to inferring posterior distributions of random variables from a probabilistic model of how the variables are related (th...
— The paper proposes a hypernetwork-based method for stock market prediction through a binary time series problem. Hypernetworks are a random hypergraph structure of higher-order...
Elena Bautu, Sun Kim, Andrei Bautu, Henri Luchian,...