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Found 8 papers, 4 papers with code
PDE-Based Optimal Strategy for Unconstrained Online Learning
Unconstrained Online Linear Optimization (OLO) is a practical problem setting to study the training of machine learning models.
Distributionally Robust Multiclass Classification and Applications in Deep Image Classifiers
We develop a Distributionally Robust Optimization (DRO) formulation for Multiclass Logistic Regression (MLR), which could tolerate data contaminated by outliers.
Distributionally Robust Multi-Output Regression Ranking
We show that this is equivalent to a regularized regression problem with a matrix norm regularizer.
Enhancing Clinical BERT Embedding using a Biomedical Knowledge Base
Domain knowledge is important for building Natural Language Processing (NLP) systems for low-resource settings, such as in the clinical domain.
Provable Hierarchical Imitation Learning via EM
Due to recent empirical successes, the options framework for hierarchical reinforcement learning is gaining increasing popularity.
Selecting Optimal Decisions via Distributionally Robust Nearest-Neighbor Regression
This paper develops a prediction-based prescriptive model for optimal decision making that (i) predicts the outcome under each action using a robust nonlinear model, and (ii) adopts a randomized prescriptive policy determined by the predicted outcomes.
Learning Optimal Personalized Treatment Rules Using Robust Regression Informed K-NN
We develop a prediction-based prescriptive model for learning optimal personalized treatments for patients based on their Electronic Health Records (EHRs).
Sequential Dynamic Decision Making with Deep Neural Nets on a Test-Time Budget
Deep neural network (DNN) based approaches hold significant potential for reinforcement learning (RL) and have already shown remarkable gains over state-of-art methods in a number of applications.
