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Found 14 papers, 0 papers with code
Partial Inference in Structured Prediction
In this paper, we examine the problem of partial inference in the context of structured prediction.
Exact Inference in High-order Structured Prediction
In this paper, we study the problem of inference in high-order structured prediction tasks.
Provable Guarantees for Sparsity Recovery with Deterministic Missing Data Patterns
We study the problem of consistently recovering the sparsity pattern of a regression parameter vector from correlated observations governed by deterministic missing data patterns using Lasso.
Dual Convexified Convolutional Neural Networks
To overcome this, we propose a highly novel weight recovery algorithm, which takes the dual solution and the kernel information as the input, and recovers the linear weight and the output of convolutional layer, instead of weight parameter.
Federated Myopic Community Detection with One-shot Communication
We provide an efficient algorithm, which computes a consensus signed weighted graph from clients evidence, and recovers the underlying network structure in the central server.
A Thorough View of Exact Inference in Graphs from the Degree-4 Sum-of-Squares Hierarchy
Performing inference in graphs is a common task within several machine learning problems, e. g., image segmentation, community detection, among others.
Information Theoretic Limits of Exact Recovery in Sub-hypergraph Models for Community Detection
Our bounds are tight and pertain to the community detection problems in various models such as the planted hypergraph stochastic block model, the planted densest sub-hypergraph model, and the planted multipartite hypergraph model.
Exact Partitioning of High-order Planted Models with a Tensor Nuclear Norm Constraint
We study the problem of efficient exact partitioning of the hypergraphs generated by high-order planted models.
Exact Partitioning of High-order Models with a Novel Convex Tensor Cone Relaxation
In this paper we propose an algorithm for exact partitioning of high-order models.
Exact Inference with Latent Variables in an Arbitrary Domain
We analyze the necessary and sufficient conditions for exact inference of a latent model.
Information-theoretic Limits for Community Detection in Network Models
For the Latent Space Model, the non-recoverability condition depends on the dimension of the latent space, and how far and spread are the communities in the latent space.
On The Projection Operator to A Three-view Cardinality Constrained Set
The cardinality constraint is an intrinsic way to restrict the solution structure in many domains, for example, sparse learning, feature selection, and compressed sensing.
Prognostics of Surgical Site Infections using Dynamic Health Data
Since existing prediction models of SSI have quite limited capacity to utilize the evolving clinical data, we develop the corresponding solution to equip these mHealth tools with decision-making capabilities for SSI prediction with a seamless assembly of several machine learning models to tackle the analytic challenges arising from the spatial-temporal data.
