Machine Learning

MRConv: Reparameterized multi-resolution convolutions efficiently model long sequences, improving performance across various data modalities.

KATE: A new scale-invariant AdaGrad variant achieves state-of-the-art convergence without square roots, outperforming AdaGrad and matching/exceeding Adam’s performance.

Relational Concept Bottleneck Models (R-CBMs) merge interpretable CBMs with powerful GNNs for high-performing, explainable relational deep learning.

Neural Episodic Control’s differentiable dictionary is shown to be a Universal Hopfield Network, enabling improved performance and a novel evaluation criterion.

This paper introduces a novel framework for classification with rejection by learning density ratios between data and idealized distributions, improving model robustness and accuracy.

Reinforcement learning with complex objectives made easy: This paper introduces an optimality-preserving translation to reduce problems with Linear Temporal Logic (LTL) objectives to standard average …

Provably efficient RL algorithms are designed to utilize immediate reward or transition information, significantly improving reward collection in unknown environments.

Boosting RL data efficiency for continuous control, this paper advocates Euclidean data augmentation using limb-based state features, significantly improving performance across various tasks.

Safe reinforcement learning is achieved via RL-AR, an algorithm that combines a safe policy with an RL policy using a focus module, ensuring safety during training while achieving competitive performa…

Reinforcement learning refines existing macro placements, enhancing chip design by improving power, performance, and area (PPA) metrics and integrating the often-overlooked metric of regularity.

Reinforcement Learning guides a novel semi-supervised learning method, improving model performance by adaptively balancing labeled and unlabeled data.

Reinforced Cross-Domain Knowledge Distillation (RCD-KD) dynamically selects target samples for efficient knowledge transfer from a complex teacher model to a compact student model, achieving superior …

RegQ: A novel regularized Q-learning algorithm ensures convergence with linear function approximation, solving a long-standing instability problem in reinforcement learning.

A novel regularized conditional diffusion model enables effective multi-task preference alignment in sequential decision-making by learning unified preference representations and maximizing mutual inf…

RAMDA: a new algorithm ensures efficient training of structured neural networks by achieving optimal structure and outstanding predictive performance.

REDUCR, a novel data downsampling method, significantly improves worst-class test accuracy in imbalanced datasets by using class priority reweighting, surpassing state-of-the-art methods by ~15%.

Memoroids and Tape-Based Batching revolutionize recurrent RL, enabling efficient processing of long sequences and improving sample efficiency by eliminating segmentation.

Reciprocators: AI agents that learn to cooperate by reciprocating influence, achieving prosocial outcomes in complex scenarios.

Numerous machine learning algorithms are unified under the novel paradigm of reciprocal learning, proven to converge at linear rates under specific conditions, enhancing sample efficiency.

REBEL, a novel reinforcement learning algorithm, simplifies policy optimization by regressing relative rewards, achieving strong performance in language and image generation tasks with increased effic…