Machine Learning

TTCT translates natural language constraints into effective training signals for safe reinforcement learning, enabling agents to learn safer policies with lower violation rates and zero-shot transfer …

Channel Clustering Module (CCM) boosts time series forecasting accuracy by intelligently grouping similar channels, improving model performance and generalization.

STONE, a novel multi-view subspace clustering framework, enhances scalability and accuracy by introducing an anchor dictionary learning mechanism and triple information enhancement.

Learn unbiased molecular dynamics from limited biased data using a novel infinitesimal generator approach; accurately estimating eigenfunctions and eigenvalues even with suboptimal biasing.

Freya PAGE achieves optimal time complexity for large-scale nonconvex finite-sum optimization using asynchronous and heterogeneous computations, overcoming limitations of prior methods.

FGTI: a novel frequency-aware model significantly improves multivariate time series imputation by focusing on the often-overlooked residual term, leveraging high-frequency information to enhance accur…

Frequency Adaptive Normalization (FAN) significantly boosts non-stationary time series forecasting accuracy by using Fourier transforms to identify and model dynamic trends and seasonal patterns, achi…

FedEgoists: A novel FL collaboration formation strategy mitigating free-riders & conflicts in cross-silo business settings, ensuring optimal coalition formation for improved model performance.

First oracle-free, computationally tractable algorithms for provably convergent multivariate distributional RL are introduced, achieving convergence rates matching scalar settings and offering insight…

Zero-shot learning achieves accurate Markov jump process inference across diverse datasets, eliminating the need for extensive model retraining.

NsCE framework tackles key OCL challenges: model ignorance (learning effective features in limited time) and myopia (overly simplified features). NsCE integrates non-sparse maximum separation regulari…

FOOGD: A novel federated learning framework that simultaneously tackles out-of-distribution generalization and detection by estimating probability density for reliable global distribution guidance.

SMG (Separated Models for Generalization) enhances visual RL generalization by disentangling task-relevant and irrelevant visual features via cooperative reconstruction, achieving state-of-the-art per…

FlexSBDD, a novel deep generative model, accurately predicts flexible protein-ligand complex structures, generating high-affinity drug molecules while overcoming the limitations of rigid protein model…

FlexPlanner: Deep reinforcement learning solves flexible 3D floorplanning, improving wirelength and alignment significantly.

Brain’s flexible mapping of abstract domains is achieved via self-supervised extraction and projection of generalized velocity signals by grid cells, enabling efficient map generation.

Bayesian best-arm identification algorithm achieves near-optimal sample complexity by incorporating an early-stopping criterion.

Two novel first-order algorithms, FOSL and MemCS, efficiently solve multi-block minimax bilevel optimization problems, significantly improving performance in deep AUC maximization and robust meta-lear…

Meta-RL agents often fail to explore effectively in environments where optimal behavior requires sacrificing immediate rewards for greater future gains. First-Explore, a novel method, tackles this by…

Fine-tune personalization in federated learning to beat adversarial clients; collaboration level depends on data heterogeneity and adversary fraction.