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Found 12 papers, 4 papers with code
Modeling User Exposure in Recommendation
The exposure is modeled as a latent variable and the model infers its value from data.
Counterfactual Evaluation of Slate Recommendations with Sequential Reward Interactions
Users of music streaming, video streaming, news recommendation, and e-commerce services often engage with content in a sequential manner.
The Population Posterior and Bayesian Inference on Streams
Many modern data analysis problems involve inferences from streaming data.
The Implicit Delta Method
When the predictive model is simple and its evaluation differentiable, this task is solved by the delta method, where we propagate the asymptotically-normal uncertainty in the predictive model through the evaluation to compute standard errors and Wald confidence intervals.
Variational Tempering
Lastly, we develop local variational tempering, which assigns a latent temperature to each data point; this allows for dynamic annealing that varies across data.
Dynamic Poisson Factorization
Models for recommender systems use latent factors to explain the preferences and behaviors of users with respect to a set of items (e. g., movies, books, academic papers).
Learning Periodic Human Behaviour Models from Sparse Data for Crowdsourcing Aid Delivery in Developing Countries
In many developing countries, half the population lives in rural locations, where access to essentials such as school materials, mosquito nets, and medical supplies is restricted.
An Empirical Bayes Approach to Optimizing Machine Learning Algorithms
EB-Hyp suggests a simpler approach to evaluating and deploying machine learning algorithms that does not require a separate validation data set and hyperparameter selection procedure.
The Population Posterior and Bayesian Modeling on Streams
Many modern data analysis problems involve inferences from streaming data.
Residual Overfit Method of Exploration
The approach, which we term the residual overfit method of exploration (ROME), drives exploration towards actions where the overfit model exhibits the most overfitting compared to the tuned model.
Switching the Loss Reduces the Cost in Batch Reinforcement Learning
We propose training fitted Q-iteration with log-loss (FQI-LOG) for batch reinforcement learning (RL).
Hessian-Free Laplace in Bayesian Deep Learning
The Laplace approximation (LA) of the Bayesian posterior is a Gaussian distribution centered at the maximum a posteriori estimate.
