1 code implementation • 26 Sep 2024 • Ruijiang Gao, Mingzhang Yin, James McInerney, Nathan Kallus
Conformal Prediction methods have finite-sample distribution-free marginal coverage guarantees.
no code implementations • 15 Mar 2024 • James McInerney, Nathan Kallus
The Laplace approximation (LA) of the Bayesian posterior is a Gaussian distribution centered at the maximum a posteriori estimate.
no code implementations • 8 Mar 2024 • Alex Ayoub, Kaiwen Wang, Vincent Liu, Samuel Robertson, James McInerney, Dawen Liang, Nathan Kallus, Csaba Szepesvári
We propose training fitted Q-iteration with log-loss (FQI-LOG) for batch reinforcement learning (RL).
1 code implementation • 11 Nov 2022 • Nathan Kallus, James McInerney
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.
no code implementations • 6 Oct 2021 • James McInerney, Nathan Kallus
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.
1 code implementation • 25 Jul 2020 • James McInerney, Brian Brost, Praveen Chandar, Rishabh Mehrotra, Ben Carterette
Users of music streaming, video streaming, news recommendation, and e-commerce services often engage with content in a sequential manner.
no code implementations • NeurIPS 2017 • James Mcinerney
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.
no code implementations • NeurIPS 2015 • James Mcinerney, Rajesh Ranganath, David Blei
Many modern data analysis problems involve inferences from streaming data.
1 code implementation • 23 Oct 2015 • Dawen Liang, Laurent Charlin, James McInerney, David M. Blei
The exposure is modeled as a latent variable and the model infers its value from data.
no code implementations • 15 Sep 2015 • Laurent Charlin, Rajesh Ranganath, James McInerney, David M. Blei
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).
2 code implementations • 19 Jul 2015 • James McInerney, Rajesh Ranganath, David M. Blei
Many modern data analysis problems involve inferences from streaming data.
no code implementations • 7 Nov 2014 • Stephan Mandt, James McInerney, Farhan Abrol, Rajesh Ranganath, David Blei
Lastly, we develop local variational tempering, which assigns a latent temperature to each data point; this allows for dynamic annealing that varies across data.
no code implementations • 26 Sep 2013 • James McInerney, Alex Rogers, Nicholas R. Jennings
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.