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Found 18 papers, 9 papers with code
Efficient and Sharp Off-Policy Evaluation in Robust Markov Decision Processes
We characterize the sharp bounds on policy value under this model, that is, the tightest possible bounds given by the transition observations from the original MDP, and we study the estimation of these bounds from such transition observations.
Low-Rank MDPs with Continuous Action Spaces
Low-Rank Markov Decision Processes (MDPs) have recently emerged as a promising framework within the domain of reinforcement learning (RL), as they allow for provably approximately correct (PAC) learning guarantees while also incorporating ML algorithms for representation learning.
Source Condition Double Robust Inference on Functionals of Inverse Problems
We consider estimation of parameters defined as linear functionals of solutions to linear inverse problems.
Minimax Instrumental Variable Regression and $L_2$ Convergence Guarantees without Identification or Closedness
In this paper, we study nonparametric estimation of instrumental variable (IV) regressions.
Provable Safe Reinforcement Learning with Binary Feedback
Many existing approaches to safe RL rely on receiving numeric safety feedback, but in many cases this feedback can only take binary values; that is, whether an action in a given state is safe or unsafe.
Inference on Strongly Identified Functionals of Weakly Identified Functions
In a variety of applications, including nonparametric instrumental variable (NPIV) analysis, proximal causal inference under unmeasured confounding, and missing-not-at-random data with shadow variables, we are interested in inference on a continuous linear functional (e. g., average causal effects) of nuisance function (e. g., NPIV regression) defined by conditional moment restrictions.
Future-Dependent Value-Based Off-Policy Evaluation in POMDPs
Finally, we extend our methods to learning of dynamics and establish the connection between our approach and the well-known spectral learning methods in POMDPs.
Proximal Reinforcement Learning: Efficient Off-Policy Evaluation in Partially Observed Markov Decision Processes
To answer these, we extend the framework of proximal causal inference to our POMDP setting, providing a variety of settings where identification is made possible by the existence of so-called bridge functions.
Have you tried Neural Topic Models? Comparative Analysis of Neural and Non-Neural Topic Models with Application to COVID-19 Twitter Data
Topic models are widely used in studying social phenomena.
The Variational Method of Moments
The conditional moment problem is a powerful formulation for describing structural causal parameters in terms of observables, a prominent example being instrumental variable regression.
Off-policy Evaluation in Infinite-Horizon Reinforcement Learning with Latent Confounders
We study an OPE problem in an infinite-horizon, ergodic Markov decision process with unobserved confounders, where states and actions can act as proxies for the unobserved confounders.
Efficient Policy Learning from Surrogate-Loss Classification Reductions
We show that, under a correct specification assumption, the weighted classification formulation need not be efficient for policy parameters.
Policy Evaluation with Latent Confounders via Optimal Balance
We study the question of policy evaluation when we instead have proxies for the latent confounders and develop an importance weighting method that avoids fitting a latent outcome regression model.
Deep Generalized Method of Moments for Instrumental Variable Analysis
Instrumental variable analysis is a powerful tool for estimating causal effects when randomization or full control of confounders is not possible.
Preferred Answer Selection in Stack Overflow: Better Text Representations ... and Metadata, Metadata, Metadata
Community question answering (cQA) forums provide a rich source of data for facilitating non-factoid question answering over many technical domains.
Mapping Instructions to Actions in 3D Environments with Visual Goal Prediction
We propose to decompose instruction execution to goal prediction and action generation.
Following High-level Navigation Instructions on a Simulated Quadcopter with Imitation Learning
We introduce a method for following high-level navigation instructions by mapping directly from images, instructions and pose estimates to continuous low-level velocity commands for real-time control.
