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Found 11 papers, 8 papers with code
Variational Shapley Network: A Probabilistic Approach to Self-Explaining Shapley values with Uncertainty Quantification
Shapley values have emerged as a foundational tool in machine learning (ML) for elucidating model decision-making processes.
A Coreset-based, Tempered Variational Posterior for Accurate and Scalable Stochastic Gaussian Process Inference
We present a novel stochastic variational Gaussian process ($\mathcal{GP}$) inference method, based on a posterior over a learnable set of weighted pseudo input-output points (coresets).
Multi-armed bandits for resource efficient, online optimization of language model pre-training: the use case of dynamic masking
We propose a multi-armed bandit framework for the sequential selection of TLM pre-training hyperparameters, aimed at optimizing language model performance, in a resource efficient manner.
A generative, predictive model for menstrual cycle lengths that accounts for potential self-tracking artifacts in mobile health data
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.
Multi-Task Gaussian Processes and Dilated Convolutional Networks for Reconstruction of Reproductive Hormonal Dynamics
We present an end-to-end statistical framework for personalized, accurate, and minimally invasive modeling of female reproductive hormonal patterns.
Phenotyping Endometriosis through Mixed Membership Models of Self-Tracking Data
We investigate the use of self-tracking data and unsupervised mixed-membership models to phenotype endometriosis.
Nonparametric Gaussian Mixture Models for the Multi-Armed Bandit
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.
Sequential Monte Carlo Bandits
We here utilize SMC for estimation of the statistics Bayesian MAB agents compute, and devise flexible policies that can address a rich class of bandit problems: i. e., MABs with nonlinear, stateless- and context-dependent reward distributions that evolve over time.
Towards Personalized Modeling of the Female Hormonal Cycle: Experiments with Mechanistic Models and Gaussian Processes
The motivation for this work is to model the hormonal cycle and predict its phases in time, both for healthy individuals and for those with disorders of the reproductive system.
Variational inference for the multi-armed contextual bandit
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.
Bayesian bandits: balancing the exploration-exploitation tradeoff via double sampling
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.
