Search Results for author: Chris H. Wiggins

Found 9 papers, 6 papers with code

A generative, predictive model for menstrual cycle lengths that accounts for potential self-tracking artifacts in mobile health data

1 code implementation24 Feb 2021 Kathy Li, Iñigo Urteaga, Amanda Shea, Virginia J. Vitzthum, Chris H. Wiggins, Noémie Elhadad

Our model offers several advantages: 1) accounting explicitly for self-tracking artifacts yields better prediction accuracy as likelihood of skipping increases; 2) because it is a generative model, predictions can be updated online as a given cycle evolves, and we can gain interpretable insight into how these predictions change over time; and 3) its hierarchical nature enables modeling of an individual's cycle length history while incorporating population-level information.

Dose-response modeling in high-throughput cancer drug screenings: An end-to-end approach

1 code implementation13 Dec 2018 Wesley Tansey, Kathy Li, Haoran Zhang, Scott W. Linderman, Raul Rabadan, David M. Blei, Chris H. Wiggins

Personalized cancer treatments based on the molecular profile of a patient's tumor are an emerging and exciting class of treatments in oncology.

Applications

(Sequential) Importance Sampling Bandits

1 code implementation8 Aug 2018 Iñigo Urteaga, Chris H. Wiggins

This work extends existing multi-armed bandit (MAB) algorithms beyond their original settings by leveraging advances in sequential Monte Carlo (SMC) methods from the approximate inference community.

Thompson Sampling

Nonparametric Gaussian Mixture Models for the Multi-Armed Bandit

1 code implementation8 Aug 2018 Iñigo Urteaga, Chris H. Wiggins

The proposed Bayesian nonparametric mixture model Thompson sampling sequentially learns the reward model that best approximates the true, yet unknown, per-arm reward distribution, achieving successful regret performance.

Density Estimation Multi-Armed Bandits +1

Bayesian bandits: balancing the exploration-exploitation tradeoff via double sampling

1 code implementation10 Sep 2017 Iñigo Urteaga, Chris H. Wiggins

Reinforcement learning studies how to balance exploration and exploitation in real-world systems, optimizing interactions with the world while simultaneously learning how the world operates.

Thompson Sampling

Variational inference for the multi-armed contextual bandit

1 code implementation10 Sep 2017 Iñigo Urteaga, Chris H. Wiggins

One general class of algorithms for optimizing interactions with the world, while simultaneously learning how the world operates, is the multi-armed bandit setting and, in particular, the contextual bandit case.

Multi-Armed Bandits Thompson Sampling +1

Stylistic Clusters and the Syrian/South Syrian Tradition of First-Millennium BCE Levantine Ivory Carving: A Machine Learning Approach

no code implementations5 Jan 2014 Amy Rebecca Gansell, Jan-Willem van de Meent, Sakellarios Zairis, Chris H. Wiggins

Thousands of first-millennium BCE ivory carvings have been excavated from Neo-Assyrian sites in Mesopotamia (primarily Nimrud, Khorsabad, and Arslan Tash) hundreds of miles from their Levantine production contexts.

BIG-bench Machine Learning Descriptive +1

Hierarchically-coupled hidden Markov models for learning kinetic rates from single-molecule data

no code implementations15 May 2013 Jan-Willem van de Meent, Jonathan E. Bronson, Frank Wood, Ruben L. Gonzalez Jr., Chris H. Wiggins

We address the problem of analyzing sets of noisy time-varying signals that all report on the same process but confound straightforward analyses due to complex inter-signal heterogeneities and measurement artifacts.

Time Series Time Series Analysis

A Bayesian Approach to Network Modularity

no code implementations21 Sep 2007 Jake M. Hofman, Chris H. Wiggins

We present an efficient, principled, and interpretable technique for inferring module assignments and for identifying the optimal number of modules in a given network.

Model Selection

Cannot find the paper you are looking for? You can Submit a new open access paper.