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Found 17 papers, 8 papers with code
Nonparametric Discrete Choice Experiments with Machine Learning Guided Adaptive Design
Cross-pollinating the machine learning and experiment design, GBS is scalable to products with hundreds of attributes and can design personalized products for heterogeneous consumers.
Confounding-Robust Policy Improvement with Human-AI Teams
In addition, we propose a personalized deferral collaboration system to leverage the diverse expertise of different human decision-makers.
Gradient Estimation for Binary Latent Variables via Gradient Variance Clipping
Gradient estimation is often necessary for fitting generative models with discrete latent variables, in contexts such as reinforcement learning and variational autoencoder (VAE) training.
Probabilistic Conformal Prediction Using Conditional Random Samples
This paper proposes probabilistic conformal prediction (PCP), a predictive inference algorithm that estimates a target variable by a discontinuous predictive set.
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
This paper proposes a novel optimization method to solve the exact L0-regularized regression problem, which is also known as the best subset selection.
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.
