Machine Learning

EGonc, a novel energy-based open-set node classification method, leverages substitute unknowns and energy scores to achieve superior accuracy and robustness in classifying nodes from known classes whi…

Sign-based optimization gets a speed boost! This paper introduces new algorithms that significantly accelerate convergence in distributed optimization by cleverly using variance reduction and enhanced…

Discover efficient neural pathways for reinforcement learning; drastically reducing model size and energy consumption without sacrificing performance.

Recurrent off-policy RL, while robust, suffers from training instability. RESEL, a novel algorithm, solves this by using a context-encoder-specific learning rate, significantly improving stability an…

Boost multi-task reinforcement learning with Cross-Task Policy Guidance (CTPG)! CTPG cleverly uses policies from already mastered tasks to guide the learning of new tasks, significantly improving effi…

FedAWE, a novel federated learning algorithm, efficiently handles intermittent and unpredictable client availability, ensuring fast and unbiased model training.

Provably efficient algorithms for learning with distribution shift are introduced, generalizing and improving prior work by achieving near-optimal error rates and offering universal learners for large…

New attacks foil both supervised and contrastive learning, achieving state-of-the-art unlearnability with less computation.

This paper proposes a novel, statistically efficient offline policy evaluation method robust to environmental shifts and unobserved confounding, providing sharp bounds with theoretical guarantees.

RELA accelerates representation learning by leveraging freely available pre-trained models to generate efficient data, reducing computational costs by up to 50% while maintaining accuracy.

SI2E, a novel RL exploration framework, leverages structural information principles to maximize value-conditional structural entropy, significantly outperforming state-of-the-art baselines in various …

EEGPT: A pretrained transformer model revolutionizes EEG signal representation by using a dual self-supervised learning method, achieving state-of-the-art results across various tasks.

EASI: Evolutionary Adversarial Simulator Identification bridges the reality gap in robotics by using GAN and ES to find optimal simulator parameters, enabling seamless sim-to-real transfer with minima…

DynaMITE-RL: A new meta-RL approach masters environments with evolving latent states by cleverly modeling episode sessions and refining existing meta-RL techniques.

Researchers developed a novel stochastic-process approach to precisely analyze learning dynamics in nonlinear perceptrons, revealing how input noise and learning rules significantly impact learning sp…

Dynamic rescaling boosts GNN training by controlling layer learning speeds and balancing networks, leading to faster training and improved generalization, especially on heterophilic graphs.

Dynamic Neural Regeneration (DNR) enhances deep learning generalization on small datasets using a data-aware dynamic masking scheme inspired by neurogenesis.

Dynamic Model Predictive Shielding (DMPS) ensures provably safe reinforcement learning by dynamically optimizing reinforcement learning objectives while maintaining provable safety, achieving higher r…

Simulation-free flow generates conditional distributions via dynamic conditional optimal transport.

Dual Defense Federated Learning (DDFed) simultaneously boosts privacy and thwarts poisoning attacks in federated learning without altering the existing framework.