1 code implementation • 31 Oct 2022 • Thomas Cook, Harsh Vardhan Dubey, Ji Ah Lee, Guangyu Zhu, Tingting Zhao, Patrick Flaherty
We extend cost-aware ERO investing to finite-horizon testing which enables the decision rule to allocate samples in a non-myopic manner.
no code implementations • 12 Jun 2021 • Aaron Schein, Anjali Nagulpally, Hanna Wallach, Patrick Flaherty
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.
no code implementations • 14 Apr 2021 • Craig S. Greenberg, Sebastian Macaluso, Nicholas Monath, Avinava Dubey, Patrick Flaherty, Manzil Zaheer, Amr Ahmed, Kyle Cranmer, Andrew McCallum
In those cases, hierarchical clustering can be seen as a combinatorial optimization problem.
1 code implementation • 26 Feb 2020 • Craig S. Greenberg, Sebastian Macaluso, Nicholas Monath, Ji-Ah Lee, Patrick Flaherty, Kyle Cranmer, Andrew Mcgregor, Andrew McCallum
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.
no code implementations • 8 Nov 2019 • Patrick Flaherty, Pitchaya Wiratchotisatian, Ji Ah Lee, Zhou Tang, Andrew C. Trapp
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).
no code implementations • NeurIPS 2018 • Craig Greenberg, Nicholas Monath, Ari Kobren, Patrick Flaherty, Andrew Mcgregor, Andrew McCallum
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).
no code implementations • 21 Mar 2017 • Hachem Saddiki, Andrew C. Trapp, Patrick Flaherty
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.
no code implementations • 19 Oct 2016 • Fan Zhang, Chuangqi Wang, Andrew Trapp, Patrick Flaherty
Mixed membership factorization is a popular approach for analyzing data sets that have within-sample heterogeneity.
no code implementations • 14 Apr 2016 • Fan Zhang, Patrick Flaherty
The detection of rare variants is important for understanding the genetic heterogeneity in mixed samples.