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Found 6 papers, 1 papers with code
Scalable Belief Propagation via Relaxed Scheduling
The ability to leverage large-scale hardware parallelism has been one of the key enablers of the accelerated recent progress in machine learning.
Towards Tight Communication Lower Bounds for Distributed Optimisation
We focus on the communication complexity of this problem: our main result provides the first fully unconditional bounds on total number of bits which need to be sent and received by the $N$ machines to solve this problem under point-to-point communication, within a given error-tolerance.
Improved Communication Lower Bounds for Distributed Optimisation
Motivated by the interest in communication-efficient methods for distributed machine learning, we consider the communication complexity of minimising a sum of $d$-dimensional functions $\sum_{i = 1}^N f_i (x)$, where each function $f_i$ is held by one of the $N$ different machines.
Relaxed Scheduling for Scalable Belief Propagation
The ability to leverage large-scale hardware parallelism has been one of the key enablers of the accelerated recent progress in machine learning.
Bayesian Network Structure Learning with Integer Programming: Polytopes, Facets, and Complexity
The challenging task of learning structures of probabilistic graphical models is an important problem within modern AI research.
Tractable Bayesian Network Structure Learning with Bounded Vertex Cover Number
Both learning and inference tasks on Bayesian networks are NP-hard in general.
