Machine Learning

Federated Learning over Connected Modes (FLOCO) accelerates global training and improves local accuracy in heterogeneous data settings by leveraging mode connectivity for collaborative model personali…

FedGCDR, a novel federated graph learning framework, tackles cross-domain recommendation challenges by securely leveraging positive knowledge from multiple sources while mitigating negative transfer a…

FEDORA, a novel algorithm, enables high-quality policy learning in federated offline reinforcement learning by leveraging the collective wisdom of diverse client datasets without data sharing.

Federated Behavioural Planes visualize client behavior in federated learning, enabling robust aggregation and enhanced security against malicious clients.

FedAvP enhances federated learning’s privacy by sharing only augmentation policies, improving performance in diverse settings.

Revolutionizing graph analysis, this paper introduces a novel framework for efficiently solving graph diffusion equations, achieving up to a hundred-fold speed improvement and enabling faster graph ne…

Risk control boosts early-exit neural networks’ speed and safety by ensuring accurate predictions before exiting early, achieving substantial computational savings across diverse tasks.

TRAC: a parameter-free optimizer conquering lifelong RL’s plasticity loss!

Accelerate iterative hard thresholding (IHT) up to 73x by safely pruning unnecessary gradient computations without accuracy loss.

Fast Graph Sharpness-Aware Minimization (FGSAM) accelerates few-shot node classification by cleverly combining GNNs and MLPs for efficient, high-performing training.

FasMe: a novel meta-learning approach delivers fast and sample-efficient precision matrix estimation, surpassing existing methods in accuracy and speed for small sample datasets.

Nash bargaining resolves hypergradient conflicts in fairness-aware meta-learning, boosting model performance and fairness.

Fair Kernel K-Means (FKKM) framework ensures fair data partitioning by integrating a novel fairness regularization term into the kernel k-means algorithm, extending this to multiple kernel settings fo…

FACT, a novel federated learning mechanism, eliminates free-riding and incentivizes truthful agent behavior by introducing a penalty system and a competitive environment, boosting model performance si…

Quantum computing drastically reduces communication needs for distributed machine learning, enabling faster and more private AI.

XFADS: a novel low-rank structured VAE framework for large-scale nonlinear Gaussian state-space modeling, achieving high predictive accuracy and scalability.

Single-layer GANs learn data subspaces more effectively using multi-feature discriminators, enabling faster training and better feature representation than conventional methods.

CE2: A new goal-directed exploration algorithm for efficient reinforcement learning in unknown environments, prioritizing accessible frontier goals via latent state clustering.

Uni-Mol2, a groundbreaking 1.1B parameter molecular pretraining model, reveals power-law scaling in molecular representation learning, achieving significant performance improvements on downstream task…

Boost GNN graph classification accuracy by enforcing consistency in learned representations across layers using a novel loss function!