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.

Papers

November 4, 2024

Learning from Convolution-based Unlearnable Datastes
Dohyun Kim, Pedro Sandoval-Segura
LeArning Abstract Unlearnable Datasets Convolution Based Unlearnable

November 3, 2024

Analysis of regularized federated learning
Langming Liu, Dingxuan Zhou
LeArning Abstract General Analysis Federated Learning Stochastic Gradient Descent Convex Set
Meta-Exploiting Frequency Prior for Cross-Domain Few-Shot Learning
Fei Zhou, Peng Wang, Lei Zhang, Zhenghua Chen, Wei Wei, Chen Ding, Guosheng Lin, Yanning Zhang
LeArning Abstract Shot Learning Meta Learning Cross Domain Image to Image Frequency Prior

November 2, 2024

Interacting Large Language Model Agents. Interpretable Models and Social Learning
Adit Jain, Vikram Krishnamurthy
Large Language Model LeArning Abstract Interpretable Model Social Learning Agent Model Bounded Rational Bayesian Agent
Covariance-based Space Regularization for Few-shot Class Incremental Learning
Yijie Hu, Guanyu Yang, Zhaorui Tan, Xiaowei Huang, Kaizhu Huang, Qiu-Feng Wang
LeArning Abstract Discriminative Representation Shot Training Class Distribution Covariance Neural Network Spatially Varying Regularization
LEARNER: Learning Granular Labels from Coarse Labels using Contrastive Learning
Gautam Gare, Jana Armouti, Nikhil Madaan, Rohan Panda, Tom Fox, Laura Hutchins, Amita Krishnan, Ricardo Rodriguez, Bennett DeBoisblanc, Deva Ramanan, John Galeotti
Deep Learning LeArning Abstract Fine Grained Contrastive Example B Learner Label Granularity Coarse Label

November 1, 2024

Learning in Markov Games with Adaptive Adversaries: Policy Regret, Fundamental Barriers, and Efficient Algorithms
Thanh Nguyen-Tang, Raman Arora
LeArning Abstract Markov Game Sample Efficient Efficient Algorithm Adaptive Adversary Monotone Adversary Counterfactual Regret
Learning to Look Around: Enhancing Teleoperation and Learning with a Human-like Actuated Neck
Bipasha Sen, Michelle Wang, Nandini Thakur, Aditya Agarwal, Pulkit Agrawal
LeArning Abstract Imitation Learning Robot Teleoperation Head Motion Teleoperation Task
Enhancing Adaptive Mixed-Criticality Scheduling with Deep Reinforcement Learning
Bruno Mendes (1), Pedro F. Souto (1 and 2), Pedro C. Diniz (2) ((1) Department of Informatics Engineering (DEI) Faculty of Engineering of the University of Porto (FEUP) (2) CISTER Research Centre)
LeArning Abstract Deep Reinforcement Real Time Scheduling Adaptive Scheduling
Active Preference-based Learning for Multi-dimensional Personalization
Minhyeon Oh, Seungjoon Lee, Jungseul Ok
Large Language Model LeArning Abstract Preference Feedback Human Preference Preference Learning Generating User Activity Scalable Personalization Binary Feedback
CLIP-RT: Learning Language-Conditioned Robotic Policies from Natural Language Supervision
Gi-Cheon Kang, Junghyun Kim, Kyuhwan Shim, Jun Ki Lee, Byoung-Tak Zhang
LeArning Abstract Robot Learning Pre Trained Vision Language Model Robot Policy Language Supervision Visual Naturalness Robot Data CLIP TD Outperforms Contrastive Imitation
Adapting While Learning: Grounding LLMs for Scientific Problems with Intelligent Tool Usage Adaptation
Bohan Lyu, Yadi Cao, Duncan Watson-Parris, Leon Bergen, Taylor Berg-Kirkpatrick, Rose Yu
Large Language Model LeArning Abstract Domain Knowledge Task Complexity
Learning to Rank Salient Content for Query-focused Summarization
Sajad Sotudeh, Nazli Goharian
LeArning Abstract Structured Summary Learning to Rank Query Focused Salient Event Graph Generation
C2A: Client-Customized Adaptation for Parameter-Efficient Federated Learning
Yeachan Kim, Junho Kim, Wing-Lam Mok, Jun-Hyung Park, SangKeun Lee
LeArning Abstract Pre Trained Language Model Parameter Efficient Fine Tuning Adaptation Concern Attention Hypernetworks Non Graph Hypernetwork Baseline Personalized Adaptation

October 31, 2024

Space for Improvement: Navigating the Design Space for Federated Learning in Satellite Constellations
Grace Kim, Luca Powell, Filip Svoboda, Nicholas Lane
Deep Learning LeArning Abstract Deep Space Federated Prompt Cooperation Design Space Federated Self Supervised Learning Unmanned Spacecraft Satellite Constellation
Compositional Automata Embeddings for Goal-Conditioned Reinforcement Learning
Beyazit Yalcinkaya, Niklas Lauffer, Marcell Vazquez-Chanlatte, Sanjit A. Seshia
LeArning Abstract Goal Conditioned Reinforcement Learning Goal Representation Temporal Goal Deterministic Finite Automaton
Progressive Safeguards for Safe and Model-Agnostic Reinforcement Learning
Nabil Omi, Hosein Hasanbeig, Hiteshi Sharma, Sriram K. Rajamani, Siddhartha Sen
LeArning Abstract Meta Learning Safe Reinforcement Learning Finite State Machine Agnostic Meta learnIng Progressive Alignment Agnostic Reinforcement Learning
Interactive proofs for verifying (quantum) learning and testing
Matthias C. Caro, Jens Eisert, Marcel Hinsche, Marios Ioannou, Alexander Nietner, Ryan Sweke
LeArning Abstract Quantum Physic Verification Task Level Test State of the Art Verifier Quantum Memory
A Non-Monolithic Policy Approach of Offline-to-Online Reinforcement Learning
JaeYoon Kim, Junyu Xuan, Christy Liang, Farookh Hussain
LeArning Abstract Offline Policy Exploration Method Offline to Online Reinforcement Learning Dexterous Policy Evolving Policy
VecCity: A Taxonomy-guided Library for Map Entity Representation Learning
Wentao Zhang, Jingyuan Wang, Yifan Yang, Leong Hou U
LeArning Abstract Taxonomy Construction Entity Mapping Geo Entity

LeArning Abstract

Papers

Learning from Convolution-based Unlearnable Datastes

Analysis of regularized federated learning

Meta-Exploiting Frequency Prior for Cross-Domain Few-Shot Learning

Interacting Large Language Model Agents. Interpretable Models and Social Learning

Covariance-based Space Regularization for Few-shot Class Incremental Learning

LEARNER: Learning Granular Labels from Coarse Labels using Contrastive Learning

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

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