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Found 10 papers, 2 papers with code
Convex Two-Layer Modeling with Latent Structure
Unsupervised learning of structured predictors has been a long standing pursuit in machine learning.
Universal Successor Representations for Transfer Reinforcement Learning
The objective of transfer reinforcement learning is to generalize from a set of previous tasks to unseen new tasks.
Few-Shot Self Reminder to Overcome Catastrophic Forgetting
We demonstrate the superiority of our method to the previous ones in two different continual learning settings on popular benchmarks, as well as a new continual learning problem where tasks are designed to be more dissimilar.
Domain Aggregation Networks for Multi-Source Domain Adaptation
In many real-world applications, we want to exploit multiple source datasets of similar tasks to learn a model for a different but related target dataset -- e. g., recognizing characters of a new font using a set of different fonts.
Universal Successor Features for Transfer Reinforcement Learning
Transfer in Reinforcement Learning (RL) refers to the idea of applying knowledge gained from previous tasks to solving related tasks.
Batch Stationary Distribution Estimation
We consider the problem of approximating the stationary distribution of an ergodic Markov chain given a set of sampled transitions.
Characterizing the Gap Between Actor-Critic and Policy Gradient
Actor-critic (AC) methods are ubiquitous in reinforcement learning.
Decentralized Federated Learning through Proxy Model Sharing
Institutions in highly regulated domains such as finance and healthcare often have restrictive rules around data sharing.
A Parametric Class of Approximate Gradient Updates for Policy Optimization
Approaches to policy optimization have been motivated from diverse principles, based on how the parametric model is interpreted (e. g. value versus policy representation) or how the learning objective is formulated, yet they share a common goal of maximizing expected return.
Find Your Friends: Personalized Federated Learning with the Right Collaborators
In the traditional federated learning setting, a central server coordinates a network of clients to train one global model.
