Search Results for author: Alp Kucukelbir

Found 11 papers, 5 papers with code

Variational Inference: A Review for Statisticians

6 code implementations4 Jan 2016 David M. Blei, Alp Kucukelbir, Jon D. McAuliffe

One of the core problems of modern statistics is to approximate difficult-to-compute probability densities.

Stochastic Optimization Variational Inference

Population Empirical Bayes

1 code implementation2 Nov 2014 Alp Kucukelbir, David M. Blei

We develop population empirical Bayes (POP-EB), a hierarchical framework that explicitly models the empirical population distribution as part of Bayesian analysis.

Bayesian Inference regression +1

Robust Probabilistic Modeling with Bayesian Data Reweighting

1 code implementation ICML 2017 Yixin Wang, Alp Kucukelbir, David M. Blei

We propose a way to systematically detect and mitigate mismatch of a large class of probabilistic models.

Variational Deep Q Network

1 code implementation30 Nov 2017 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 to approximate the posterior of the parameters.

Efficient Exploration Variational Inference

Posterior Dispersion Indices

no code implementations24 May 2016 Alp Kucukelbir, David M. Blei

We propose to evaluate a model through posterior dispersion.

Variance Reduction for Evolution Strategies via Structured Control Variates

no code implementations29 May 2019 Yunhao Tang, Krzysztof Choromanski, Alp Kucukelbir

Evolution Strategies (ES) are a powerful class of blackbox optimization techniques that recently became a competitive alternative to state-of-the-art policy gradient (PG) algorithms for reinforcement learning (RL).

Reinforcement Learning (RL)

Hindsight Expectation Maximization for Goal-conditioned Reinforcement Learning

no code implementations13 Jun 2020 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.

reinforcement-learning Reinforcement Learning (RL)

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