[HTML][HTML] Signal processing on higher-order networks: Livin'on the edge... and beyond
In this tutorial, we provide a didactic treatment of the emerging topic of signal processing on
higher-order networks. Drawing analogies from discrete and graph signal processing, we …
higher-order networks. Drawing analogies from discrete and graph signal processing, we …
Massively parallel computation: Algorithms and applications
The algorithms community has been modeling the underlying key features and constraints of
massively parallel frameworks and using these models to discover new algorithmic …
massively parallel frameworks and using these models to discover new algorithmic …
Submodular hypergraphs: p-laplacians, cheeger inequalities and spectral clustering
P Li, O Milenkovic - International Conference on Machine …, 2018 - proceedings.mlr.press
We introduce submodular hypergraphs, a family of hypergraphs that have different
submodular weights associated with different cuts of hyperedges. Submodular hypergraphs …
submodular weights associated with different cuts of hyperedges. Submodular hypergraphs …
Structured optimal transport
D Alvarez-Melis, T Jaakkola… - … conference on artificial …, 2018 - proceedings.mlr.press
Optimal Transport has recently gained interest in machine learning for applications ranging
from domain adaptation to sentence similarities or deep learning. Yet, its ability to capture …
from domain adaptation to sentence similarities or deep learning. Yet, its ability to capture …
Efficient massively parallel methods for dynamic programming
Modern science and engineering is driven by massively large data sets and its advance
heavily relies on massively parallel computing platforms such as Spark, MapReduce, and …
heavily relies on massively parallel computing platforms such as Spark, MapReduce, and …
Let's Make Block Coordinate Descent Converge Faster: Faster Greedy Rules, Message-Passing, Active-Set Complexity, and Superlinear Convergence
Block coordinate descent (BCD) methods are widely used for large-scale numerical
optimization because of their cheap iteration costs, low memory requirements, amenability to …
optimization because of their cheap iteration costs, low memory requirements, amenability to …
Parallel batch-dynamic graphs: Algorithms and lower bounds
In this paper we study the problem of dynamically maintaining graph properties under
batches of edge insertions and deletions in the massively parallel model of computation. In …
batches of edge insertions and deletions in the massively parallel model of computation. In …
Let's make block coordinate descent converge faster: faster greedy rules, message-passing, active-set complexity, and superlinear convergence
Block coordinate descent (BCD) methods are widely used for large-scale numerical
optimization because of their cheap iteration costs, low memory requirements, amenability to …
optimization because of their cheap iteration costs, low memory requirements, amenability to …
Subquadratic submodular function minimization
Submodular function minimization (SFM) is a fundamental discrete optimization problem
which generalizes many well known problems, has applications in various fields, and can be …
which generalizes many well known problems, has applications in various fields, and can be …
Distributed-prover interactive proofs
Interactive proof systems enable a verifier with limited resources to decide an intractable
language (or compute a hard function) by communicating with a powerful but untrusted …
language (or compute a hard function) by communicating with a powerful but untrusted …