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

December 3, 2024

The effect of priors on Learning with Restricted Boltzmann Machines
Gianluca Manzan, Daniele Tantari
LeArning Abstract Generative Model Mixed Effect Restricted Boltzmann Machine SAM Prior Prior Model
Leveraging Ensemble-Based Semi-Supervised Learning for Illicit Account Detection in Ethereum DeFi Transactions
Shabnam Fazliani, Mohammad Mowlavi Sorond, Arsalan Masoudifard
LeArning Abstract Data Detection Self Training Outlier Detection Malicious User
Learn More by Using Less: Distributed Learning with Energy-Constrained Devices
Roberto Pereira, Cristian J. Vaca-Rubio, Luis Blanco
LeArning Abstract Federated Learning Smart Device Sustainable AI
Learning from Concealed Labels
Zhongnian Li, Meng Wei, Peng Ying, Tongfeng Sun, Xinzheng Xu
LeArning Abstract Sensitive Data Multi Class Classification Class Classifier Private Label
The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis
Kee Siong Ng, Samuel Yang-Zhao, Timothy Cadogan-Cowper
LeArning Abstract Timely Survey Multi Agent Reinforcement Learning Critical Synthesis Reinforcement Learning Agent Hidden CoST Multi Agent Environment Multi Agent Setting Utility Maximization

December 2, 2024

Explore Reinforced: Equilibrium Approximation with Reinforcement Learning
Ryan Yu, Mateusz Nowak, Qintong Xie, Michelle Yilin Feng, Peter Chin
LeArning Abstract Positive Reinforcement Modern Reinforcement Learning Equilibrium Computation Coarse Correlated Equilibrium Equilibrium Approximators
FERERO: A Flexible Framework for Preference-Guided Multi-Objective Learning
Lisha Chen, AFM Saif, Yanning Shen, Tianyi Chen
LeArning Abstract Multi Objective Pareto Optimal Flexible Framework Preference Based Optimization
Gen-SIS: Generative Self-augmentation Improves Self-supervised Learning
Varun Belagali, Srikar Yellapragada, Alexandros Graikos, Saarthak Kapse, Zilinghan Li, Tarak Nath Nandi, Ravi K Madduri, Prateek Prasanna, Joel Saltz, Dimitris Samaras
LeArning Abstract Self Supervised Generative Diffusion Model Generative Blending Augmentation
Effectiveness of L2 Regularization in Privacy-Preserving Machine Learning
Nikolaos Chandrinos (1), Iliana Loi (2), Panagiotis Zachos (2), Ioannis Symeonidis (1), Aristotelis Spiliotis (1), Maria Panou (1), Konstantinos Moustakas (2) ((1) Human Factors and Vehicle Technology, Hellenic Institute of Transport, Centre for Research and Technology Hellas, Thermi, Greece, (2) Wire Communications and Information Technology Laboratory, Dept. of Electrical and Computer Engineering, University of Patras, Patras, Greece)
LeArning Abstract Differential Privacy Membership Inference Attack Inference Attack L2 Regularization
Towards Cross-Lingual Audio Abuse Detection in Low-Resource Settings with Few-Shot Learning
Aditya Narayan Sankaran, Reza Farahbaksh, Noel Crespi
LeArning Abstract Low Resource Language Low Resource Abuse Detection Abusive Language
RL2: Reinforce Large Language Model to Assist Safe Reinforcement Learning for Energy Management of Active Distribution Networks
Xu Yang, Chenhui Lin, Haotian Liu, Wenchuan Wu
Large Language Model LeArning Abstract Deep Reinforcement Learning Positive Reinforcement Power System Energy Management Penalty Method Active Distribution
Integrating Decision-Making Into Differentiable Optimization Guided Learning for End-to-End Planning of Autonomous Vehicles
Wenru Liu, Yongkang Song, Chengzhen Meng, Zhiyu Huang, Haochen Liu, Chen Lv, Jun Ma
LeArning Abstract Autonomous Vehicle Optimization Purpose Trajectory Planning Temporal Trajectory Waymo Open Motion Dataset Differentiable Optimization
HumekaFL: Automated Detection of Neonatal Asphyxia Using Federated Learning
Pamely Zantou, Blessed Guda, Bereket Retta, Gladys Inabeza, Carlee Joe-Wong, Assane Gueye
LeArning Abstract Automatic Detection Federated Prompt Cooperation Sensitive Patient Federated Machine Learning Neonatal Outcome

December 1, 2024

Learning to Forget using Hypernetworks
Jose Miguel Lara Rangel, Stefan Schoepf, Jack Foster, David Krueger, Usman Anwar
LeArning Abstract Machine Unlearning Poisoning Attack Attention Hypernetworks Unlearned Model
Learning on Less: Constraining Pre-trained Model Learning for Generalizable Diffusion-Generated Image Detection
Yingjian Chen, Lei Zhang, Yakun Niu, Lei Tan, Pei Chen
LeArning Abstract Diffusion Model Strong Generalization Pre Trained Model Pre Training Model Diffusion Generated Image Image Detection
Towards Unified Molecule-Enhanced Pathology Image Representation Learning via Integrating Spatial Transcriptomics
Minghao Han, Dingkang Yang, Jiabei Cheng, Xukun Zhang, Linhao Qu, Zizhi Chen, Lihua Zhang
LeArning Abstract Computational Pathology Unified Alignment Spatial Transcriptomics
MambaNUT: Nighttime UAV Tracking via Mamba and Adaptive Curriculum Learning
You Wu, Xiangyang Yang, Xucheng Wang, Hengzhou Ye, Dan Zeng, Shuiwang Li
LeArning Abstract Mamba in Mamba Nighttime Datasets Nighttime Unmanned Aerial Vehicle Adaptive Curriculum Tracking Framework

November 30, 2024

Rethinking Generalizability and Discriminability of Self-Supervised Learning from Evolutionary Game Theory Perspective
Jiangmeng Li, Zehua Zang, Qirui Ji, Chuxiong Sun, Wenwen Qiang, Junge Zhang, Changwen Zheng, Fuchun Sun, Hui Xiong
LeArning Abstract Self Supervised Learning Stronger Generalizability Self Supervised Method Discriminative Task Evolutionary Game Theory
Video-3D LLM: Learning Position-Aware Video Representation for 3D Scene Understanding
Duo Zheng, Shijia Huang, Liwei Wang
LeArning Abstract Multimodal Large Language Model Human Understanding 3D Scene Video Representation 3D Scene Understanding Video LLM
Probabilistic Prediction of Ship Maneuvering Motion using Ensemble Learning with Feedforward Neural Networks
Kouki Wakita, Youhei Akimoto, Atsuo Maki
Neural Network LeArning Abstract Diverse Ensemble Parameter Identification Autonomous Surface Vessel Dynamic Maneuver Ship Motion Ship Dataset

LeArning Abstract

Papers

The effect of priors on Learning with Restricted Boltzmann Machines

Leveraging Ensemble-Based Semi-Supervised Learning for Illicit Account Detection in Ethereum DeFi Transactions

Learn More by Using Less: Distributed Learning with Energy-Constrained Devices

Learning from Concealed Labels

The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis

Explore Reinforced: Equilibrium Approximation with Reinforcement Learning

FERERO: A Flexible Framework for Preference-Guided Multi-Objective Learning

Gen-SIS: Generative Self-augmentation Improves Self-supervised Learning

Effectiveness of L2 Regularization in Privacy-Preserving Machine Learning

Towards Cross-Lingual Audio Abuse Detection in Low-Resource Settings with Few-Shot Learning

RL2: Reinforce Large Language Model to Assist Safe Reinforcement Learning for Energy Management of Active Distribution Networks

Integrating Decision-Making Into Differentiable Optimization Guided Learning for End-to-End Planning of Autonomous Vehicles

HumekaFL: Automated Detection of Neonatal Asphyxia Using Federated Learning

Learning to Forget using Hypernetworks

Learning on Less: Constraining Pre-trained Model Learning for Generalizable Diffusion-Generated Image Detection

Towards Unified Molecule-Enhanced Pathology Image Representation Learning via Integrating Spatial Transcriptomics

MambaNUT: Nighttime UAV Tracking via Mamba and Adaptive Curriculum Learning

Rethinking Generalizability and Discriminability of Self-Supervised Learning from Evolutionary Game Theory Perspective

Video-3D LLM: Learning Position-Aware Video Representation for 3D Scene Understanding

Probabilistic Prediction of Ship Maneuvering Motion using Ensemble Learning with Feedforward Neural Networks