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Found 11 papers, 1 papers with code
Identifying the Reward Function by Anchor Actions
Moreover, the method allows for the state transitions to be stochastic.
Reinforcement Learning Paycheck Optimization for Multivariate Financial Goals
We study paycheck optimization, which examines how to allocate income in order to achieve several competing financial goals.
Improving Offline RL by Blending Heuristics
We propose Heuristic Blending (HUBL), a simple performance-improving technique for a broad class of offline RL algorithms based on value bootstrapping.
A Data-Driven State Aggregation Approach for Dynamic Discrete Choice Models
We use these estimated Q-functions, along with a clustering algorithm, to select a subset of states that are the most pivotal for driving changes in Q-functions.
Deep PQR: Solving Inverse Reinforcement Learning using Anchor Actions
We name our method PQR, as it sequentially estimates the Policy, the $Q$-function, and the Reward function by deep learning.
Stochastic Learning for Sparse Discrete Markov Random Fields with Controlled Gradient Approximation Error
We study the $L_1$-regularized maximum likelihood estimator/estimation (MLE) problem for discrete Markov random fields (MRFs), where efficient and scalable learning requires both sparse regularization and approximate inference.
Temporal Poisson Square Root Graphical Models
We propose temporal Poisson square root graphical models (TPSQRs), a generalization of Poisson square root graphical models (PSQRs) specifically designed for modeling longitudinal event data.
Partially Linear Additive Gaussian Graphical Models
We propose a partially linear additive Gaussian graphical model (PLA-GGM) for the estimation of associations between random variables distorted by observed confounders.
Joint Nonparametric Precision Matrix Estimation with Confounding
Empirical results are presented using simulated and real brain imaging data, which suggest that our approach improves precision matrix estimation, as compared to baselines, when confounding is present.
A Screening Rule for l1-Regularized Ising Model Estimation
We discover a screening rule for l1-regularized Ising model estimation.
An Efficient Pseudo-likelihood Method for Sparse Binary Pairwise Markov Network Estimation
In this way, many insights and optimization procedures for sparse logistic regression can be applied to the learning of discrete Markov networks.
