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Found 10 papers, 1 papers with code
Q2R: A Query-to-Resolution System for Natural-Language Queries
We present a system for document retrieval that combines direct classification with standard content-based retrieval approaches to significantly improve the relevance of the retrieved documents.
Neural-Progressive Hedging: Enforcing Constraints in Reinforcement Learning with Stochastic Programming
We propose a framework, called neural-progressive hedging (NP), that leverages stochastic programming during the online phase of executing a reinforcement learning (RL) policy.
Order Constraints in Optimal Transport
Optimal transport is a framework for comparing measures whereby a cost is incurred for transporting one measure to another.
Efficient Reinforcement Learning in Resource Allocation Problems Through Permutation Invariant Multi-task Learning
One of the main challenges in real-world reinforcement learning is to learn successfully from limited training samples.
Distributional Reinforcement Learning for Risk-Sensitive Policies
We address the problem of learning a risk-sensitive policy based on the CVaR risk measure using distributional reinforcement learning.
Distributional Reinforcement Learning
reinforcement-learning
+1
Robustness and Personalization in Federated Learning: A Unified Approach via Regularization
We present a class of methods for robust, personalized federated learning, called Fed+, that unifies many federated learning algorithms.
Variational Bayesian Inference for Crowdsourcing Predictions
Crowdsourcing has emerged as an effective means for performing a number of machine learning tasks such as annotation and labelling of images and other data sets.
A Deep Ensemble Multi-Agent Reinforcement Learning Approach for Air Traffic Control
Air traffic control is an example of a highly challenging operational problem that is readily amenable to human expertise augmentation via decision support technologies.
Clustering from Labels and Time-Varying Graphs
Our theoretical results cover and subsume a wide range of existing graph clustering results including planted partition, weighted clustering and partially observed graphs.
Reinforcement Learning in Robust Markov Decision Processes
An important challenge in Markov decision processes is to ensure robustness with respect to unexpected or adversarial system behavior while taking advantage of well-behaving parts of the system.
