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

October 31, 2024

RA-RLHF: Provably Efficient Risk-Aware Reinforcement Learning Human Feedback
Yujie Zhao, Jose Efraim Aguilar Escamill, Weyl Lu, Huazheng Wang
Reinforcement Learning LeArning Abstract Risk Sensitive Positive Reinforcement Preference Based Reinforcement Learning Quantile Temporal Difference Risk Sensitive Algorithm
LEAF: Learning and Evaluation Augmented by Fact-Checking to Improve Factualness in Large Language Models
Hieu Tran, Junda Wang, Yujan Ting, Weijing Huang, Terrence Chen
Large Language Model LeArning Abstract Fact Checking Natural Language Processing Task Factual Claim Front End

October 30, 2024

Kernel-Based Function Approximation for Average Reward Reinforcement Learning: An Optimist No-Regret Algorithm
Sattar Vakili, Julia Olkhovskaya
Reinforcement Learning LeArning Abstract Kernel Ridge Regression Regret Guarantee Kernel Approximation Average Reward Reinforcement Learning
Return Augmented Decision Transformer for Off-Dynamics Reinforcement Learning
Ruhan Wang, Yu Yang, Zhishuai Liu, Dongruo Zhou, Pan Xu
LeArning Abstract Policy Learning Decision Transformer
Learning and Transferring Sparse Contextual Bigrams with Linear Transformers
Yunwei Ren, Zixuan Wang, Jason D. Lee
LeArning Abstract Transformer Megatron Decepticons Layer Transformer Linear Ordered Data Natural Language Model Context Bigram
eDOC: Explainable Decoding Out-of-domain Cell Types with Evidential Learning
Chaochen Wu, Meiyun Zuo, Lei Xie
LeArning Abstract Single Cell RNA scRNA Seq Cell Annotation Evidential Reasoning scRNA Seq Data
Learning for Deformable Linear Object Insertion Leveraging Flexibility Estimation from Visual Cues
Mingen Li, Changhyun Choi
LeArning Abstract Ground Truth Estimation Task Visual Cue Deformable Linear Object Two Stage Approach Flexible Model
On the Optimality of Dilated Entropy and Lower Bounds for Online Learning in Extensive-Form Games
Zhiyuan Fan, Christian Kroer, Gabriele Farina
LeArning Abstract Video Game Lower Bound Near Optimality Stable Entropy Distance Function Extensive Form Game Symmetric Strategy
Keypoint Abstraction using Large Models for Object-Relative Imitation Learning
Xiaolin Fang, Bo-Ruei Huang, Jiayuan Mao, Jasmine Shone, Joshua B. Tenenbaum, Tomás Lozano-Pérez, Leslie Pack Kaelbling
LeArning Abstract Large Model Pre Trained Vision Language Model Action Prediction Conditioned Policy Robot Imitation
Planning and Learning in Risk-Aware Restless Multi-Arm Bandit Problem
Nima Akbarzadeh, Erick Delage, Yossiri Adulyasak
LeArning Abstract Markov Decision Process Task Planning Risk Sensitive Restless Multi Armed Bandit
Explainable Behavior Cloning: Teaching Large Language Model Agents through Learning by Demonstration
Yanchu Guan, Dong Wang, Yan Wang, Haiqing Wang, Renen Sun, Chenyi Zhuang, Jinjie Gu, Zhixuan Chu
Large Language Model LeArning Abstract Behavior Cloning BEHAVIOR Explanation Textual Demonstration Autonomous Operation
DOA-Aware Audio-Visual Self-Supervised Learning for Sound Event Localization and Detection
Yoto Fujita, Yoshiaki Bando, Keisuke Imoto, Masaki Onishi, Kazuyoshi Yoshii
LeArning Abstract Data Detection Localization Focus Sound Event Unlabeled Video
SoftCTRL: Soft conservative KL-control of Transformer Reinforcement Learning for Autonomous Driving
Minh Tri Huynh, Duc Dung Nguyen
LeArning Abstract Autonomous Driving Imitation Learning Motion Planning Urban Autonomous Driving Imitation Ability
Calibrating Practical Privacy Risks for Differentially Private Machine Learning
Yuechun Gu, Keke Chen
LeArning Abstract Differential Privacy Privacy Risk Privacy Preservation Privacy Budget Differential Analysis Privacy Preserving Computation Privacy Compliant Feature

October 29, 2024

Learning and Unlearning of Fabricated Knowledge in Language Models
Chen Sun, Nolan Andrew Miller, Andrey Zhmoginov, Max Vladymyrov, Mark Sandler
Large Language Model Language Model LeArning Abstract Harmful Unlearning Language Model Hallucination
BF-Meta: Secure Blockchain-enhanced Privacy-preserving Federated Learning for Metaverse
Wenbo Liu, Handi Chen, Edith C.H. Ngai
LeArning Abstract Federated Learning Federated Prompt Cooperation Ubiquitous Semantic Metaverse Secure Approach Meta Level Future METAVerse
Comment on Is Complexity an Illusion?
Gabriel Simmons
LeArning Abstract Strong Generalization Scientific Inference Complexity Matter Multi Class Classification Online Comment Optical Illusion
Revisiting Multi-Granularity Representation via Group Contrastive Learning for Unsupervised Vehicle Re-identification
Zhigang Chang, Shibao Zheng
LeArning Abstract Multi Granularity Vehicle Re Identification Contrastive Learning Module

October 28, 2024

SeriesGAN: Time Series Generation via Adversarial and Autoregressive Learning
MohammadReza EskandariNasab, Shah Muhammad Hamdi, Soukaina Filali Boubrahimi
LeArning Abstract Adversarial Attack Supervised Autoencoder GAN Model Autoregressive Model Adversarial Text Time Series Generation
A Unified Solution to Diverse Heterogeneities in One-shot Federated Learning
Jun Bai, Yiliao Song, Di Wu, Atul Sajjanhar, Yong Xiang, Wei Zhou, Xiaohui Tao, Yan Li
LeArning Abstract Unified Framework Data Heterogeneity Model Heterogeneity One Shot Federated Learning

LeArning Abstract

Papers

RA-RLHF: Provably Efficient Risk-Aware Reinforcement Learning Human Feedback

LEAF: Learning and Evaluation Augmented by Fact-Checking to Improve Factualness in Large Language Models

Kernel-Based Function Approximation for Average Reward Reinforcement Learning: An Optimist No-Regret Algorithm

Return Augmented Decision Transformer for Off-Dynamics Reinforcement Learning

Learning and Transferring Sparse Contextual Bigrams with Linear Transformers

eDOC: Explainable Decoding Out-of-domain Cell Types with Evidential Learning

Learning for Deformable Linear Object Insertion Leveraging Flexibility Estimation from Visual Cues

On the Optimality of Dilated Entropy and Lower Bounds for Online Learning in Extensive-Form Games

Keypoint Abstraction using Large Models for Object-Relative Imitation Learning

Planning and Learning in Risk-Aware Restless Multi-Arm Bandit Problem

Explainable Behavior Cloning: Teaching Large Language Model Agents through Learning by Demonstration

DOA-Aware Audio-Visual Self-Supervised Learning for Sound Event Localization and Detection

SoftCTRL: Soft conservative KL-control of Transformer Reinforcement Learning for Autonomous Driving

Calibrating Practical Privacy Risks for Differentially Private Machine Learning

Learning and Unlearning of Fabricated Knowledge in Language Models

BF-Meta: Secure Blockchain-enhanced Privacy-preserving Federated Learning for Metaverse

Comment on Is Complexity an Illusion?

Revisiting Multi-Granularity Representation via Group Contrastive Learning for Unsupervised Vehicle Re-identification

SeriesGAN: Time Series Generation via Adversarial and Autoregressive Learning

A Unified Solution to Diverse Heterogeneities in One-shot Federated Learning