Machine Learning

II-COS: a novel online sample selection method effectively controls individual and interactive constraints in real-time via predictive inference, improving efficiency and addressing various practical …

Recurrent Trace Units (RTUs) significantly enhance real-time recurrent learning in reinforcement learning, outperforming other methods with less computation.

RankUp: Revolutionizing semi-supervised regression by cleverly adapting classification techniques for superior performance!

Provably efficient randomized exploration in cooperative MARL is achieved via a novel unified algorithm framework, CoopTS, using Thompson Sampling with PHE and LMC exploration strategies.

First provably efficient randomized RL algorithms using multinomial logistic function approximation are introduced, achieving superior performance and constant-time computational cost.

This paper presents randomized algorithms with PAC bounds for solving inverse reinforcement learning problems in continuous state and action spaces, offering robust theoretical guarantees and practica…

Random pixel projections outperform complex online continual learning methods for image classification, challenging assumptions about representation learning.

RandNet-Parareal: A novel time-parallel PDE solver using Random Neural Networks achieves speed gains up to x125, dramatically improving scalability for large-scale simulations.

RAGRAPH, a novel retrieval-augmented graph learning framework, boosts GNN generalization by integrating external graph data, significantly outperforming state-of-the-art methods.

RA-PbRL introduces a provably efficient algorithm for risk-aware preference-based reinforcement learning, addressing the limitations of existing risk-neutral methods in applications demanding heighten…

Quantum VAE with spherical latent variable learning enables efficient, one-shot 3D molecule generation, outperforming classic and other quantum methods.

Quasi-Bayesian Vine (QB-Vine) efficiently models high-dimensional densities by recursively updating 1D marginal predictives and a vine copula, significantly outperforming state-of-the-art methods.

Quantum Deep Equilibrium Models (QDEQs) achieve higher QML performance with shallower circuits by using a DEQ training paradigm, improving near-term quantum computation efficiency.

Accelerated Lie group optimization achieved via a novel momentum algorithm (Lie NAG-SC) with proven convergence rates, surpassing existing methods in efficiency.

QGFN boosts Generative Flow Networks (GFNs) by cleverly combining their sampling policy with an action-value estimate, creating controllable and efficient generation of high-reward samples.

Offline RL struggles with OOD action overestimation. QDQ tackles this by penalizing uncertain Q-values using a consistency model, enhancing offline RL performance.

PUREGEN uses generative model dynamics to purify poisoned training data, providing a universal, effective, and efficient train-time defense against various data poisoning attacks.

PURE: A novel method uses Graph ODE to adapt spatio-temporal forecasting models to various fluid dynamics scenarios, improving model adaptation to unseen parameters and long-term predictions.

Pure message passing in graph neural networks can accurately estimate common neighbor heuristics for superior link prediction.

DASH: a novel pruning framework leverages domain knowledge to improve the interpretability and sparsity of neural network models for gene regulatory dynamics, outperforming existing methods.