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Found 20 papers, 10 papers with code
Time-series Generation by Contrastive Imitation
In this work, we study a generative framework that seeks to combine the strengths of both: Motivated by a moment-matching objective to mitigate compounding error, we optimize a local (but forward-looking) transition policy, where the reinforcement signal is provided by a global (but stepwise-decomposable) energy model trained by contrastive estimation.
Invariant Causal Imitation Learning for Generalizable Policies
By leveraging data from multiple environments, we propose Invariant Causal Imitation Learning (ICIL), a novel technique in which we learn a feature representation that is invariant across domains, on the basis of which we learn an imitation policy that matches expert behavior.
Explaining by Imitating: Understanding Decisions by Interpretable Policy Learning
Understanding human behavior from observed data is critical for transparency and accountability in decision-making.
Clairvoyance: A Pipeline Toolkit for Medical Time Series
Despite exponential growth in electronic patient data, there is a remarkable gap between the potential and realized utilization of ML for clinical research and decision support.
Online Decision Mediation
Consider learning a decision support assistant to serve as an intermediary between (oracle) expert behavior and (imperfect) human behavior: At each time, the algorithm observes an action chosen by a fallible agent, and decides whether to *accept* that agent's decision, *intervene* with an alternative, or *request* the expert's opinion.
Inverse Decision Modeling: Learning Interpretable Representations of Behavior
Decision analysis deals with modeling and enhancing decision processes.
Curiosity in Hindsight: Intrinsic Exploration in Stochastic Environments
In this work, we study a natural solution derived from structural causal models of the world: Our key idea is to learn representations of the future that capture precisely the unpredictable aspects of each outcome -- which we use as additional input for predictions, such that intrinsic rewards only reflect the predictable aspects of world dynamics.
HyperImpute: Generalized Iterative Imputation with Automatic Model Selection
Consider the problem of imputing missing values in a dataset.
Closing the loop in medical decision support by understanding clinical decision-making: A case study on organ transplantation
Significant effort has been placed on developing decision support tools to improve patient care.
Inverse Contextual Bandits: Learning How Behavior Evolves over Time
Understanding a decision-maker's priorities by observing their behavior is critical for transparency and accountability in decision processes, such as in healthcare.
The Medkit-Learn(ing) Environment: Medical Decision Modelling through Simulation
Understanding decision-making in clinical environments is of paramount importance if we are to bring the strengths of machine learning to ultimately improve patient outcomes.
Hide-and-Seek Privacy Challenge
The clinical time-series setting poses a unique combination of challenges to data modeling and sharing.
Learning "What-if" Explanations for Sequential Decision-Making
Building interpretable parameterizations of real-world decision-making on the basis of demonstrated behavior -- i. e. trajectories of observations and actions made by an expert maximizing some unknown reward function -- is essential for introspecting and auditing policies in different institutions.
Strictly Batch Imitation Learning by Energy-based Distribution Matching
Through experiments with application to control and healthcare settings, we illustrate consistent performance gains over existing algorithms for strictly batch imitation learning.
Inverse Active Sensing: Modeling and Understanding Timely Decision-Making
Finally, we illustrate how this formulation enables understanding decision-making behavior by quantifying preferences implicit in observed decision strategies (the inverse problem).
Target-Embedding Autoencoders for Supervised Representation Learning
Autoencoder-based learning has emerged as a staple for disciplining representations in unsupervised and semi-supervised settings.
Stepwise Model Selection for Sequence Prediction via Deep Kernel Learning
In this paper, we propose a novel Bayesian optimization (BO) algorithm to tackle the challenge of model selection in this setting.
Time-series Generative Adversarial Networks
A good generative model for time-series data should preserve temporal dynamics, in the sense that new sequences respect the original relationships between variables across time.
Time-series Generative Adversarial Networks
A good generative model for time-series data should preservetemporal dynamics, in the sense that new sequences respect the original relationships between variablesacross time.
MATCH-Net: Dynamic Prediction in Survival Analysis using Convolutional Neural Networks
Accurate prediction of disease trajectories is critical for early identification and timely treatment of patients at risk.
