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 to Assist Humans without Inferring Rewards
Vivek Myers, Evan Ellis, Sergey Levine, Benjamin Eysenbach, Anca Dragan
Reinforcement Learning LeArning Abstract Real Human Inverse Reinforcement Learning Novel Predecessor and Successor Reward Inference
Information plane and compression-gnostic feedback in quantum machine learning
Nathan Haboury, Mo Kordzanganeh, Alexey Melnikov, Pavel Sekatski
LeArning Abstract Loss Function Gradient Descent Variational Quantum Circuit Learning Algorithm Quantum Machine Quantum Learning
SpecRaGE: Robust and Generalizable Multi-view Spectral Representation Learning
Amitai Yacobi, Ofir Lindenbaum, Uri Shaham
LeArning Abstract Multi View Multi View Learning Multi View Representation Learning
Collaborative Cognitive Diagnosis with Disentangled Representation Learning for Learner Modeling
Weibo Gao, Qi Liu, Linan Yue, Fangzhou Yao, Hao Wang, Yin Gu, Zheng Zhang
LeArning Abstract Disentanglement Capability Disentangled Representation Mental State Learner Model Cognitive Diagnosis
Against Multifaceted Graph Heterogeneity via Asymmetric Federated Prompt Learning
Zhuoning Guo, Ruiqian Han, Hao Liu
LeArning Abstract Federated Graph Learning Heterogeneous Graph Learning Federated Prompt Transferable Graph Neural
Fast Semi-supervised Learning on Large Graphs: An Improved Green-function Method
Feiping Nie, Yitao Song, Wei Chang, Rong Wang, Xuelong Li
LeArning Abstract Semi Supervised Large Relevance Improvement Sparse Graph Large Graph
Transferable Sequential Recommendation via Vector Quantized Meta Learning
Zhenrui Yue, Huimin Zeng, Yang Zhang, Julian McAuley, Dong Wang
LeArning Abstract Sequential Recommendation Recommendation Performance Large Scale Recommendation
Automatic Structured Pruning for Efficient Architecture in Federated Learning
Thai Vu Nguyen, Long Bao Le, Anderson Avila
LeArning Abstract Federated Prompt Cooperation Pruning Method Structured Pruning Efficient Architecture
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 Class Distribution Shot Training 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 Abstract

Papers

Learning to Assist Humans without Inferring Rewards

Information plane and compression-gnostic feedback in quantum machine learning

SpecRaGE: Robust and Generalizable Multi-view Spectral Representation Learning

Collaborative Cognitive Diagnosis with Disentangled Representation Learning for Learner Modeling

Against Multifaceted Graph Heterogeneity via Asymmetric Federated Prompt Learning

Fast Semi-supervised Learning on Large Graphs: An Improved Green-function Method

Transferable Sequential Recommendation via Vector Quantized Meta Learning

Automatic Structured Pruning for Efficient Architecture in Federated Learning

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