Scalable Learning

Scalable learning focuses on developing machine learning methods capable of efficiently handling massive datasets and complex tasks, addressing limitations of traditional approaches. Current research emphasizes adapting existing architectures like neural networks (including transformers and graph neural networks) and developing novel algorithms such as masked autoencoders and coreset methods to improve scalability and generalization across diverse data types (e.g., graphs, images, time series). This field is crucial for advancing AI applications in areas like biological research, traffic management, and cybersecurity, where large-scale data analysis is essential for extracting meaningful insights and developing effective solutions.

Papers

July 2, 2024

Core Knowledge Learning Framework for Graph Adaptation and Scalability Learning
Bowen Zhang, Zhichao Huang, Genan Dai, Guangning Xu, Xiaomao Fan, Hu Huang
Graph Classification Graph Domain Adaptation Scalable Learning Knowledge Subgraph

May 9, 2024

Scalable Learning of Segment-Level Traffic Congestion Functions
Shushman Choudhury, Abdul Rahman Kreidieh, Iveel Tsogsuren, Neha Arora, Carolina Osorio, Alexandre Bayen
Traffic Data Scalable Learning Traffic Congestion Macroscopic Traffic

April 16, 2024

Masked Autoencoders for Microscopy are Scalable Learners of Cellular Biology
Oren Kraus, Kian Kenyon-Dean, Saber Saberian, Maryam Fallah, Peter McLean, Jess Leung, Vasudev Sharma, Ayla Khan, Jia Balakrishnan, Safiye Celik, Dominique Beaini, Maciej Sypetkowski, Chi Vicky Cheng, Kristen Morse, Maureen Makes, Ben Mabey, Berton Earnshaw
Supervised Autoencoder Masked Autoencoders Microscopy Image Scalable Learning

March 23, 2024

Scaling Learning based Policy Optimization for Temporal Logic Tasks by Controller Network Dropout
Navid Hashemi, Bardh Hoxha, Danil Prokhorov, Georgios Fainekos, Jyotirmoy Deshmukh
Gradient Descent Policy OpTimization Structured Dropout Continuous Control Signal Temporal Logic Temporal Task Scalable Learning

March 1, 2024

Scalable Learning of Item Response Theory Models
Susanne Frick, Amer Krivošija, Alexander Munteanu
Latent Variable Item Response Theory Scalable Learning

February 12, 2024

Scalable Structure Learning for Sparse Context-Specific Systems
Felix Leopoldo Rios, Alex Markham, Liam Solus
Many Sparse Structure Learning Context Modeling Scalable Learning DAG Learning Contextual Relationship

December 1, 2023

Sequential Modeling Enables Scalable Learning for Large Vision Models
Yutong Bai, Xinyang Geng, Karttikeya Mangalam, Amir Bar, Alan Yuille, Trevor Darrell, Jitendra Malik, Alexei A Efros
Semantic Segmentation Vision Task Visual Data Large Vision Model Sequential Model Scalable Learning

October 29, 2023

Hyperbolic Graph Neural Networks at Scale: A Meta Learning Approach
Nurendra Choudhary, Nikhil Rao, Chandan K. Reddy
Meta Learning Visual Analogue Scale Inductive Bias Hyperbolic Embeddings Meta Gradient Hyperbolic Neural Network Scalable Learning Hyperbolic Graph

September 27, 2023

Masked Autoencoders are Scalable Learners of Cellular Morphology
Oren Kraus, Kian Kenyon-Dean, Saber Saberian, Maryam Fallah, Peter McLean, Jess Leung, Vasudev Sharma, Ayla Khan, Jia Balakrishnan, Safiye Celik, Maciej Sypetkowski, Chi Vicky Cheng, Kristen Morse, Maureen Makes, Ben Mabey, Berton Earnshaw
Supervised Autoencoder Masked Autoencoders Microscopy Image Deep Vision Model Scalable Learning Large Scale Self Supervised Cell Morphology

September 8, 2023

Probabilistic Safety Regions Via Finite Families of Scalable Classifiers
Alberto Carlevaro, Teodoro Alamo, Fabrizio Dabbene, Maurizio Mongelli
Machine Learning Scalable Learning Supervised Classification Safety Specification

September 6, 2023

Scalable Learning of Intrusion Responses through Recursive Decomposition
Kim Hammar, Rolf Stadler
Reinforcement Learning Intrusion Detection Stochastic Game Scalable Learning Fictitious Self Play

June 17, 2023

Linearly-scalable learning of smooth low-dimensional patterns with permutation-aided entropic dimension reduction
Illia Horenko, Lukas Pospisil
Dimension Reduction Scalable Learning Low Dimensional Data Dimensional Data Pattern

May 31, 2023

Scalable Learning of Latent Language Structure With Logical Offline Cycle Consistency
Maxwell Crouse, Ramon Astudillo, Tahira Naseem, Subhajit Chaudhury, Pavan Kapanipathi, Salim Roukos, Alexander Gray
Semantic Parsing Cycle Consistency Neural Semantic Frozen Language Model Scalable Learning Neural Text Generation

May 10, 2023

Towards Scalable Adaptive Learning with Graph Neural Networks and Reinforcement Learning
Jean Vassoyan, Jill-Jênn Vie, Pirmin Lemberger
Reinforcement Learning Graph Neural Network Adaptive Learning Sequential Recommender System Personalized Learning Scalable Learning Student Simulation

February 21, 2023

Scalable Infomin Learning
Yanzhi Chen, Weihao Sun, Yingzhen Li, Adrian Weller
Representation Learning Mutual Information Scalable Learning New Learning

November 4, 2022

Improved Adaptive Algorithm for Scalable Active Learning with Weak Labeler
Yifang Chen, Karthik Sankararaman, Alessandro Lazaric, Matteo Pirotta, Dmytro Karamshuk, Qifan Wang, Karishma Mandyam, Sinong Wang, Han Fang
Active Learning Weak Label Adaptive Algorithm Scalable Learning

October 17, 2022

A Solver-Free Framework for Scalable Learning in Neural ILP Architectures
Yatin Nandwani, Rishabh Ranjan, Mausam, Parag Singla
New Framework Neural Model Scalable Learning Learning Based Solver

July 19, 2022

Active-Learning-as-a-Service: An Automatic and Efficient MLOps System for Data-Centric AI
Yizheng Huang, Huaizheng Zhang, Yuanming Li, Chiew Tong Lau, Yang You
Active Learning Data Centric AI Application MLOps System Scalable Learning

March 7, 2022

Provably Accurate and Scalable Linear Classifiers in Hyperbolic Spaces
Chao Pan, Eli Chien, Puoya Tabaghi, Jianhao Peng, Olgica Milenkovic
Hyperbolic Space Dimensional Embeddings Hyperbolic Neural Network Scalable Learning Hyperbolic Classifier

February 8, 2022

Bandit Sampling for Multiplex Networks
Cenk Baykal, Vamsi K. Potluru, Sameena Shah, Manuela M. Veloso
Link Prediction Node Classification Large Network Scalable Learning Multiplex Network