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 11, 2024

Learning Multi-Agent Collaborative Manipulation for Long-Horizon Quadrupedal Pushing
Chuye Hong, Yuming Feng, Yaru Niu, Shiqi Liu, Yuxiang Yang, Wenhao Yu, Tingnan Zhang, Jie Tan, Ding Zhao
LeArning Abstract Long Term Quadruped Robot Quadrupedal Locomotion Locomotion Policy Collaborative Manipulation Task Driven Manipulation
Multi-Objective Algorithms for Learning Open-Ended Robotic Problems
Martin Robert, Simon Brodeur, Francois Ferland
LeArning Abstract Multi Objective Optimization Quadrupedal Robot Multi Objective Evolutionary Algorithm Quadrupedal Locomotion Robot Locomotion Multi Objective Learning Robotics Problem
Computable Model-Independent Bounds for Adversarial Quantum Machine Learning
Bacui Li, Tansu Alpcan, Chandra Thapa, Udaya Parampalli
LeArning Abstract Adversarial Attack Quantum Computing Upper Bound Geometric QML
Learning from Feedback: Semantic Enhancement for Object SLAM Using Foundation Models
Jungseok Hong, Ran Choi, John J. Leonard
LeArning Abstract Foundation Model Human Feedback Object Level Mapping Semantic Enhancement Semantic Simultaneous Localization

November 10, 2024

Using Diffusion Models as Generative Replay in Continual Federated Learning -- What will Happen?
Yongsheng Mei, Liangqi Yuan, Dong-Jun Han, Kevin S. Chan, Christopher G. Brinton, Tian Lan
LeArning Abstract Diffusion Model Generative Adversarial Network Conditional Diffusion Model Decentralized Learning Continuous Learning Generative Replay Federated Continual Learning
Barriers to Complexity-Theoretic Proofs that Achieving AGI Using Machine Learning is Intractable
Michael Guerzhoy
LeArning Abstract New Machine Inductive Bias Complexity Matter Artificial General Intelligence Machine Learning System Knowledge Barrier Recent Research Complexity Result
SamRobNODDI: Q-Space Sampling-Augmented Continuous Representation Learning for Robust and Generalized NODDI
Taohui Xiao, Jian Cheng, Wenxin Fan, Enqing Dong, Hairong Zheng, Shanshan Wang
LeArning Abstract Robust Version K Space Continuous Representation
A Variance Minimization Approach to Temporal-Difference Learning
Xingguo Chen, Yu Gong, Shangdong Yang, Wenhao Wang
LeArning Abstract Value Based Reinforcement Learning Error Minimizing Noise
Layer-Wise Feature Metric of Semantic-Pixel Matching for Few-Shot Learning
Hao Tang, Junhao Lu, Guoheng Huang, Ming Li, Xuhang Chen, Guo Zhong, Zhengguang Tan, Zinuo Li
LeArning Abstract Pixel Level Image Pair Layer Wise Semantic Embeddings
Activation Map Compression through Tensor Decomposition for Deep Learning
Le-Trung Nguyen, Aël Quélennec, Enzo Tartaglione, Samuel Tardieu, Van-Tam Nguyen
Deep Learning LeArning Abstract Back Propagation Tensor Decomposition Device Inference Tensor Decomposition Method Activation Compression Higher Order Singular Value Decomposition

November 9, 2024

SEEKR: Selective Attention-Guided Knowledge Retention for Continual Learning of Large Language Models
Jinghan He, Haiyun Guo, Kuan Zhu, Zihan Zhao, Ming Tang, Jinqiao Wang
LeArning Abstract Continual LEArning Continual Learning Benchmark Federat\textbf{T}ed Cl\textbf{A}ss Continual Lea\textbf{R}nin\textbf{G Replay Based Continual Learning Knowledge Retention Fine Grained Knowledge
CGLearn: Consistent Gradient-Based Learning for Out-of-Distribution Generalization
Jawad Chowdhury, Gabriel Terejanu
LeArning Abstract Strong Generalization Strong Consistency Causal Structure Distribution Data Causal Direction Invariant Predictor Gradient Consistency

November 8, 2024

November 7, 2024

Learning in Budgeted Auctions with Spacing Objectives
Giannis Fikioris, Robert Kleinberg, Yoav Kolumbus, Raunak Kumar, Yishay Mansour, Éva Tardos
LeArning Abstract Optimal Policy Price Auction Optimal Bidding Strategy Online Bayesian
Learning from Demonstration with Hierarchical Policy Abstractions Toward High-Performance and Courteous Autonomous Racing
Chanyoung Chung, Hyunki Seong, David Hyunchul Shim
LeArning Abstract Multi Agent Trajectory Planning Textual Demonstration Autonomous Racing High Performance Policy Architecture
Boosting the Efficiency of Metaheuristics Through Opposition-Based Learning in Optimum Locating of Control Systems in Tall Buildings
Salar Farahmand-Tabar, Sina Shirgir
LeArning Abstract High Efficiency Metaheuristic Algorithm Residential Building Facility Location Problem Control System Engineering Problem Metaheuristic Optimization Algorithm
Constrained Latent Action Policies for Model-Based Offline Reinforcement Learning
Marvin Alles, Philip Becker-Ehmck, Patrick van der Smagt, Maximilian Karl
Reinforcement Learning LeArning Abstract Offline Reinforcement Learning Model Based Offline Reinforcement Learning Latent Action Latent Action Space
Hypercube Policy Regularization Framework for Offline Reinforcement Learning
Yi Shen, Hanyan Huang
LeArning Abstract Offline Reinforcement Learning Constrained Policy Optimization Policy Regularization

LeArning Abstract

Papers

Learning Multi-Agent Collaborative Manipulation for Long-Horizon Quadrupedal Pushing

Multi-Objective Algorithms for Learning Open-Ended Robotic Problems

Computable Model-Independent Bounds for Adversarial Quantum Machine Learning

Learning from Feedback: Semantic Enhancement for Object SLAM Using Foundation Models

Using Diffusion Models as Generative Replay in Continual Federated Learning -- What will Happen?

Barriers to Complexity-Theoretic Proofs that Achieving AGI Using Machine Learning is Intractable

SamRobNODDI: Q-Space Sampling-Augmented Continuous Representation Learning for Robust and Generalized NODDI

A Variance Minimization Approach to Temporal-Difference Learning

Layer-Wise Feature Metric of Semantic-Pixel Matching for Few-Shot Learning

Activation Map Compression through Tensor Decomposition for Deep Learning

SEEKR: Selective Attention-Guided Knowledge Retention for Continual Learning of Large Language Models

CGLearn: Consistent Gradient-Based Learning for Out-of-Distribution Generalization

Predicting Stroke through Retinal Graphs and Multimodal Self-supervised Learning

Towards Active Flow Control Strategies Through Deep Reinforcement Learning

Bridging the Gap between Learning and Inference for Diffusion-Based Molecule Generation

Learning in Budgeted Auctions with Spacing Objectives

Learning from Demonstration with Hierarchical Policy Abstractions Toward High-Performance and Courteous Autonomous Racing

Boosting the Efficiency of Metaheuristics Through Opposition-Based Learning in Optimum Locating of Control Systems in Tall Buildings

Constrained Latent Action Policies for Model-Based Offline Reinforcement Learning

Hypercube Policy Regularization Framework for Offline Reinforcement Learning