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Found 16 papers, 5 papers with code
Pearl: A Production-ready Reinforcement Learning Agent
Reinforcement learning (RL) is a versatile framework for optimizing long-term goals.
IQL-TD-MPC: Implicit Q-Learning for Hierarchical Model Predictive Control
Model-based reinforcement learning (RL) has shown great promise due to its sample efficiency, but still struggles with long-horizon sparse-reward tasks, especially in offline settings where the agent learns from a fixed dataset.
Offline RL With Resource Constrained Online Deployment
We address this performance gap with a policy transfer algorithm which first trains a teacher agent using the offline dataset where features are fully available, and then transfers this knowledge to a student agent that only uses the resource-constrained features.
Offline Reinforcement Learning with Resource Constrained Online Deployment
We address this performance gap with a policy transfer algorithm which first trains a teacher agent using the offline dataset where features are fully available, and then transfers this knowledge to a student agent that only uses the resource-constrained features.
Representation Learning for Clustering via Building Consensus
Recent advances in deep clustering and unsupervised representation learning are based on the idea that different views of an input image (generated through data augmentation techniques) must be close in the representation space (exemplar consistency), and/or similar images must have similar cluster assignments (population consistency).
Ranked #4 on
Image Clustering
on ImageNet-10
Semantic Hashing with Locality Sensitive Embeddings
Semantic hashing methods have been explored for learning transformations into binary vector spaces.
Consensus Clustering With Unsupervised Representation Learning
Recent advances in deep clustering and unsupervised representation learning are based on the idea that different views of an input image (generated through data augmentation techniques) must either be closer in the representation space, or have a similar cluster assignment.
Zero Shot Domain Generalization
Domain generalization (DG) methods try to learn a model that when trained on data from multiple domains, would generalize to a new unseen domain.
Self-Supervised Contextual Bandits in Computer Vision
In the usual self-supervision, we learn implicit labels from the training data for a secondary task.
Data Transformation Insights in Self-supervision with Clustering Tasks
We show faster convergence rate with valid transformations for convex as well as certain family of non-convex objectives along with the proof of convergence to the original set of optima.
Label-similarity Curriculum Learning
We propose a novel curriculum learning approach for image classification that adapts the loss function by changing the label representation.
A Generalization Error Bound for Multi-class Domain Generalization
Domain generalization is the problem of assigning labels to an unlabeled data set, given several similar data sets for which labels have been provided.
Domain Generalization by Marginal Transfer Learning
In the problem of domain generalization (DG), there are labeled training data sets from several related prediction problems, and the goal is to make accurate predictions on future unlabeled data sets that are not known to the learner.
Data-dependent Generalization Bounds for Multi-class Classification
In this paper, we study data-dependent generalization error bounds exhibiting a mild dependency on the number of classes, making them suitable for multi-class learning with a large number of label classes.
Multi-Task Learning for Contextual Bandits
Contextual bandits are a form of multi-armed bandit in which the agent has access to predictive side information (known as the context) for each arm at each time step, and have been used to model personalized news recommendation, ad placement, and other applications.
Distributed Optimization of Multi-Class SVMs
Training of one-vs.-rest SVMs can be parallelized over the number of classes in a straight forward way.
