3.9k citations · 4.5k across the 10 of their papers we have counts for
10 papers
Treeffuser: Probabilistic Predictions via Conditional Diffusions with Gradient-Boosted Trees
Nicolas Beltran-Velez, Alessandro Antonio Grande, Achille Nazaret +2
Probabilistic prediction aims to compute predictive distributions rather than single point predictions. These distributions enable practitioners to quantify uncertainty, compute ri…
Hindsight Expectation Maximization for Goal-conditioned Reinforcement Learning
Yunhao Tang, Alp Kucukelbir
We propose a graphical model framework for goal-conditioned RL, with an EM algorithm that operates on the lower bound of the RL objective. The E-step provides a natural interpretat…
Variational Deep Q Network
Yunhao Tang, Alp Kucukelbir
We propose a framework that directly tackles the probability distribution of the value function parameters in Deep Q Network (DQN), with powerful variational inference subroutines…
Edward: A library for probabilistic modeling, inference, and criticism
Dustin Tran, Alp Kucukelbir, Adji B. Dieng +3
Probabilistic modeling is a powerful approach for analyzing empirical information. We describe Edward, a library for probabilistic modeling. Edward's design reflects an iterative p…
Robust Probabilistic Modeling with Bayesian Data Reweighting
Yixin Wang, Alp Kucukelbir, David M. Blei
Probabilistic models analyze data by relying on a set of assumptions. Data that exhibit deviations from these assumptions can undermine inference and prediction quality. Robust mod…
Posterior Dispersion Indices
Alp Kucukelbir, David M. Blei
Probabilistic modeling is cyclical: we specify a model, infer its posterior, and evaluate its performance. Evaluation drives the cycle, as we revise our model based on how it perfo…