Search Results for author:
Found 13 papers, 7 papers with code
FedPC: Federated Learning for Language Generation with Personal and Context Preference Embeddings
Federated learning is a training paradigm that learns from multiple distributed users without aggregating data on a centralized server.
Multi-UAV Planning for Cooperative Wildfire Coverage and Tracking with Quality-of-Service Guarantees
Our approach enables UAVs to infer the latent fire propagation dynamics for time-extended coordination in safety-critical conditions.
FedEmbed: Personalized Private Federated Learning
Federated learning enables the deployment of machine learning to problems for which centralized data collection is impractical.
Learning Interpretable, High-Performing Policies for Autonomous Driving
While the performance of these approaches warrants real-world adoption, these policies lack interpretability, limiting deployability in the safety-critical and legally-regulated domain of autonomous driving (AD).
Towards a Comprehensive Understanding and Accurate Evaluation of Societal Biases in Pre-Trained Transformers
The ease of access to pre-trained transformers has enabled developers to leverage large-scale language models to build exciting applications for their users.
Multimodal Punctuation Prediction with Contextual Dropout
Automatic speech recognition (ASR) is widely used in consumer electronics.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Natural Language Specification of Reinforcement Learning Policies through Differentiable Decision Trees
Human-AI policy specification is a novel procedure we define in which humans can collaboratively warm-start a robot's reinforcement learning policy.
Cross-Loss Influence Functions to Explain Deep Network Representations
As machine learning is increasingly deployed in the real world, it is paramount that we develop the tools necessary to analyze the decision-making of the models we train and deploy to end-users.
Interpretable and Personalized Apprenticeship Scheduling: Learning Interpretable Scheduling Policies from Heterogeneous User Demonstrations
Resource scheduling and coordination is an NP-hard optimization requiring an efficient allocation of agents to a set of tasks with upper- and lower bound temporal and resource constraints.
Optimization Methods for Interpretable Differentiable Decision Trees in Reinforcement Learning
Decision trees are ubiquitous in machine learning for their ease of use and interpretability.
Safe Coordination of Human-Robot Firefighting Teams
Wildfires are destructive and inflict massive, irreversible harm to victims' lives and natural resources.
Neural-encoding Human Experts' Domain Knowledge to Warm Start Reinforcement Learning
Deep reinforcement learning has been successful in a variety of tasks, such as game playing and robotic manipulation.
Action2Vec: A Crossmodal Embedding Approach to Action Learning
We describe a novel cross-modal embedding space for actions, named Action2Vec, which combines linguistic cues from class labels with spatio-temporal features derived from video clips.
