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Found 12 papers, 2 papers with code
Doubly Non-Central Beta Matrix Factorization for Stable Dimensionality Reduction of Bounded Support Matrix Data
Empirical results show that our method has similar performance as other state-of-the-art approaches in terms of held-out prediction and computational complexity, but has significantly better performance in terms of stability to changes in hyper-parameters.
Maximum a Posteriori Inference for Factor Graphs via Benders' Decomposition
A Lagrangian relaxation of the dual yields a statistical inference algorithm as a linear programming problem.
Hedging in Sequential Experiments
Together, these instruments enable an investigator to hedge the risk of ruin and they enable a investigator to efficiently hedge experimental risk.
Cost-aware Generalized $α$-investing for Multiple Hypothesis Testing
We extend cost-aware ERO investing to finite-horizon testing which enables the decision rule to allocate samples in a non-myopic manner.
Doubly Non-Central Beta Matrix Factorization for DNA Methylation Data
We present a new non-negative matrix factorization model for $(0, 1)$ bounded-support data based on the doubly non-central beta (DNCB) distribution, a generalization of the beta distribution.
Exact and Approximate Hierarchical Clustering Using A*
In those cases, hierarchical clustering can be seen as a combinatorial optimization problem.
Data Structures & Algorithms for Exact Inference in Hierarchical Clustering
In contrast to existing methods, we present novel dynamic-programming algorithms for \emph{exact} inference in hierarchical clustering based on a novel trellis data structure, and we prove that we can exactly compute the partition function, maximum likelihood hierarchy, and marginal probabilities of sub-hierarchies and clusters.
MAP Clustering under the Gaussian Mixture Model via Mixed Integer Nonlinear Optimization
We present a global optimization approach for solving the maximum a-posteriori (MAP) clustering problem under the Gaussian mixture model. Our approach can accommodate side constraints and it preserves the combinatorial structure of the MAP clustering problem by formulating it asa mixed-integer nonlinear optimization problem (MINLP).
Compact Representation of Uncertainty in Clustering
For many classic structured prediction problems, probability distributions over the dependent variables can be efficiently computed using widely-known algorithms and data structures (such as forward-backward, and its corresponding trellis for exact probability distributions in Markov models).
A Deterministic Global Optimization Method for Variational Inference
Variational inference methods for latent variable statistical models have gained popularity because they are relatively fast, can handle large data sets, and have deterministic convergence guarantees.
A global optimization algorithm for sparse mixed membership matrix factorization
Mixed membership factorization is a popular approach for analyzing data sets that have within-sample heterogeneity.
Variational inference for rare variant detection in deep, heterogeneous next-generation sequencing data
The detection of rare variants is important for understanding the genetic heterogeneity in mixed samples.
