Machine Learning

Neuromap leverages graph neural networks to optimize the map equation for community detection, achieving competitive performance and automatically determining the optimal number of clusters.

A new, surprisingly simple boosting algorithm achieves provably optimal sample complexity and outperforms existing algorithms on large datasets.

This paper presents computationally efficient transfer reinforcement learning algorithms that remove the dependence on state/action space sizes while achieving minimax optimality.

Deep RL policy learning is improved by identifying and boosting key parameter update directions using a novel temporal SVD analysis, leading to more efficient and effective learning.

I-OBS, a novel family of sparse recovery algorithms leveraging second-order information, achieves faster convergence rates for sparse DNNs, validated by large-scale experiments.

ESCAIP, a novel neural network architecture, dramatically boosts the speed and accuracy of atomic simulations by leveraging attention mechanisms, enabling efficient large-scale modeling across diverse…

Researchers extended implicit bias theory to multiclass classification using a novel framework, proving that gradient descent prefers simple solutions even with complex alternatives.

Lowering a neural network’s geometric complexity during pre-training enhances neural collapse and improves transfer learning, especially in few-shot scenarios.

Researchers developed a framework for analyzing stochastic adaptive learning rate algorithms, providing exact risk and learning rate curves, revealing the importance of data covariance and uncovering …

New ‘Feature Speed Formula’ predicts & controls deep learning’s hierarchical feature learning by linking hyperparameter tuning to the angle between feature updates and backward pass.

Offline model-based RL methods fail as dynamics models improve; this paper reveals the ’edge-of-reach’ problem causing this and introduces RAVL, a simple solution ensuring robust performance.

ReBorn revitalizes multi-agent reinforcement learning by tackling dormant neurons, boosting network expressivity and learning efficiency.

Researchers found that superior OOD detection performance comes at the cost of reduced generalization. Their novel Decoupled Uncertainty Learning (DUL) algorithm harmonizes OOD detection and generali…

Data balancing in foundation models surprisingly reduces variance, improving model training and performance.

TFGDA: Leveraging graph topology and feature alignment for superior semi-supervised domain adaptation.

Test-time adaptation strengthens the linear correlation between in- and out-of-distribution accuracy, enabling precise OOD performance prediction and hyperparameter optimization without labeled OOD da…

Ada-ReAlign: a novel algorithm for continual test-time adaptation that leverages non-stationary representation learning to effectively align unlabeled data streams with source data, enhancing model ad…

Prioritize crucial decisions in deep RL policy testing with a novel model-based method for rigorous state importance ranking, enabling efficient safety and performance verification.

Artificial Dopamine (AD) algorithm achieves comparable performance to backpropagation methods in complex RL tasks by using only synchronously distributed per-layer TD errors, demonstrating the suffici…

AdaGauss tackles task-recency bias in exemplar-free class incremental learning by adapting class covariances and introducing an anti-collapse loss, achieving state-of-the-art results.