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Found 13 papers, 7 papers with code
Partial Identification with Noisy Covariates: A Robust Optimization Approach
Directly adjusting for these imperfect measurements of the covariates can lead to biased causal estimates.
Optimization-based Causal Estimation from Heterogenous Environments
This paper presents a new optimization approach to causal estimation.
Probabilistic Best Subset Selection via Gradient-Based Optimization
In high-dimensional statistics, variable selection is an optimization problem aiming to recover the latent sparse pattern from all possible covariate combinations.
Methodology
Pairwise Supervised Hashing with Bernoulli Variational Auto-Encoder and Self-Control Gradient Estimator
Semantic hashing has become a crucial component of fast similarity search in many large-scale information retrieval systems, in particular, for text data.
Discrete Action On-Policy Learning with Action-Value Critic
Reinforcement learning (RL) in discrete action space is ubiquitous in real-world applications, but its complexity grows exponentially with the action-space dimension, making it challenging to apply existing on-policy gradient based deep RL algorithms efficiently.
Meta-Learning without Memorization
If this is not done, the meta-learner can ignore the task training data and learn a single model that performs all of the meta-training tasks zero-shot, but does not adapt effectively to new image classes.
Semi-Implicit Generative Model
To combine explicit and implicit generative models, we introduce semi-implicit generator (SIG) as a flexible hierarchical model that can be trained in the maximum likelihood framework.
ARSM: Augment-REINFORCE-Swap-Merge Estimator for Gradient Backpropagation Through Categorical Variables
To address the challenge of backpropagating the gradient through categorical variables, we propose the augment-REINFORCE-swap-merge (ARSM) gradient estimator that is unbiased and has low variance.
Augment-Reinforce-Merge Policy Gradient for Binary Stochastic Policy
Due to the high variance of policy gradients, on-policy optimization algorithms are plagued with low sample efficiency.
ARM: Augment-REINFORCE-Merge Gradient for Stochastic Binary Networks
To backpropagate the gradients through stochastic binary layers, we propose the augment-REINFORCE-merge (ARM) estimator that is unbiased, exhibits low variance, and has low computational complexity.
Semi-Implicit Variational Inference
Semi-implicit variational inference (SIVI) is introduced to expand the commonly used analytic variational distribution family, by mixing the variational parameter with a flexible distribution.
Convergence of Gradient EM on Multi-component Mixture of Gaussians
In this paper, we study convergence properties of the gradient variant of Expectation-Maximization algorithm~\cite{lange1995gradient} for Gaussian Mixture Models for arbitrary number of clusters and mixing coefficients.
Convergence Analysis of Gradient EM for Multi-component Gaussian Mixture
In this paper, we study convergence properties of the gradient Expectation-Maximization algorithm \cite{lange1995gradient} for Gaussian Mixture Models for general number of clusters and mixing coefficients.
