LeArning Abstract
Learning, in the context of these papers, encompasses a broad range of research focused on improving the efficiency, robustness, and adaptability of machine learning models across diverse applications. Current efforts concentrate on developing novel self-supervised learning techniques, particularly for structured data like tabular formats, and on leveraging low-rank adaptations for efficient fine-tuning of large language and other foundation models. These advancements are significant because they address key challenges in data efficiency, computational cost, and the generalization capabilities of machine learning systems, impacting fields ranging from personalized medicine to autonomous robotics.
2549papers
Papers - Page 37
November 2, 2024
November 1, 2024
Learning in Markov Games with Adaptive Adversaries: Policy Regret, Fundamental Barriers, and Efficient Algorithms
Learning to Look Around: Enhancing Teleoperation and Learning with a Human-like Actuated Neck
Enhancing Adaptive Mixed-Criticality Scheduling with Deep Reinforcement Learning
Active Preference-based Learning for Multi-dimensional Personalization
CLIP-RT: Learning Language-Conditioned Robotic Policies from Natural Language Supervision
Adapting While Learning: Grounding LLMs for Scientific Problems with Intelligent Tool Usage Adaptation
Learning to Rank Salient Content for Query-focused Summarization
C2A: Client-Customized Adaptation for Parameter-Efficient Federated Learning
October 31, 2024
Space for Improvement: Navigating the Design Space for Federated Learning in Satellite Constellations
Compositional Automata Embeddings for Goal-Conditioned Reinforcement Learning
Progressive Safeguards for Safe and Model-Agnostic Reinforcement Learning
Interactive proofs for verifying (quantum) learning and testing
A Non-Monolithic Policy Approach of Offline-to-Online Reinforcement Learning
VecCity: A Taxonomy-guided Library for Map Entity Representation Learning
RA-PbRL: Provably Efficient Risk-Aware Preference-Based Reinforcement Learning
LEAF: Learning and Evaluation Augmented by Fact-Checking to Improve Factualness in Large Language Models