We provide the first polynomial-time convergence guarantees for the probabilistic flow ODE
implementation (together with a corrector step) of score-based generative modeling. Our …

The ubiquitous missing values cause the multivariate time series data to be partially
observed, destroying the integrity of time series and hindering the effective time series data …

Effective imputation is a crucial preprocessing step for time series analysis. Despite the
development of numerous deep learning algorithms for time series imputation, the …

It is a crucial challenge to reconstruct population dynamics using unlabeled samples from
distributions at coarse time intervals. Recent approaches such as flow-based models or …

As software systems grow increasingly intricate, Artificial Intelligence for IT Operations
(AIOps) methods have been widely used in software system failure management to ensure …

Diffusion models have become the go-to method for large-scale generative models in real-
world applications. These applications often involve data distributions confined within …

Foundation models, especially LLMs, are profoundly transforming deep learning. Instead of
training many task-specific models, we can adapt a single pretrained model to many tasks …

Although pre-trained transformers and reprogrammed text-based LLMs have shown strong
performance on time series tasks, the best-performing architectures vary widely across …

Probabilistic time series imputation has been widely applied in real-world scenarios due to
its ability to estimate uncertainty of imputation results. Meanwhile, denoising diffusion …

Missing values are prevalent in multivariate time series, compromising the integrity of
analyses and degrading the performance of downstream tasks. Consequently, research has …