conftrace_
← Machine Learning
Machine Learning ›

Learning Paradigms

2,054 papers (7 classified directly here)

Subtopics

Meta-Learning (973) Federated Learning (894) Curriculum Learning (202)

Papers per year

1
6
3
175
315
'05
'10
'15
'20
'25

Papers (including subtopics)

Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured Learning NIPS 2023
SimFBO: Towards Simple, Flexible and Communication-efficient Federated Bilevel Learning NIPS 2023
H-nobs: Achieving Certified Fairness and Robustness in Distributed Learning on Heterogeneous Datasets NIPS 2023
Revisiting Logistic-softmax Likelihood in Bayesian Meta-Learning for Few-Shot Classification NIPS 2023
Aggregating Capacity in FL through Successive Layer Training for Computationally-Constrained Devices NIPS 2023
Meta-Learning Adversarial Bandit Algorithms NIPS 2023
Mobilizing Personalized Federated Learning in Infrastructure-Less and Heterogeneous Environments via Random Walk Stochastic ADMM NIPS 2023
Online Control for Meta-optimization NIPS 2023
A Computation and Communication Efficient Method for Distributed Nonconvex Problems in the Partial Participation Setting NIPS 2023
Robust and Actively Secure Serverless Collaborative Learning NIPS 2023
Federated Multi-Objective Learning NIPS 2023
L2T-DLN: Learning to Teach with Dynamic Loss Network NIPS 2023
Parameterizing Non-Parametric Meta-Reinforcement Learning Tasks via Subtask Decomposition NIPS 2023
DeepACO: Neural-enhanced Ant Systems for Combinatorial Optimization NIPS 2023
Hyperbolic Graph Neural Networks at Scale: A Meta Learning Approach NIPS 2023
PTADisc: A Cross-Course Dataset Supporting Personalized Learning in Cold-Start Scenarios NIPS 2023
Learning to Group Auxiliary Datasets for Molecule NIPS 2023
ProtoDiff: Learning to Learn Prototypical Networks by Task-Guided Diffusion NIPS 2023
Hierarchical Gaussian Mixture based Task Generative Model for Robust Meta-Learning NIPS 2023
Global Update Tracking: A Decentralized Learning Algorithm for Heterogeneous Data NIPS 2023
Curriculum Learning for Graph Neural Networks: Which Edges Should We Learn First NIPS 2023
Optimistic Meta-Gradients NIPS 2023
Train Hard, Fight Easy: Robust Meta Reinforcement Learning NIPS 2023
Learning Functional Transduction NIPS 2023
Mnemosyne: Learning to Train Transformers with Transformers NIPS 2023