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Found 5 papers, 3 papers with code
Short-term Temporal Dependency Detection under Heterogeneous Event Dynamic with Hawkes Processes
Then we proposed a robust and computationally-efficient method modified from MLE that does not rely on the prior estimation of the heterogeneous intensity and is thus applicable in a data-limited regime (e. g., few-shot, no repeated observations).
Provably Convergent Schrödinger Bridge with Applications to Probabilistic Time Series Imputation
We show that optimizing the transport cost improves the performance and the proposed algorithm achieves the state-of-the-art result in healthcare and environmental data while exhibiting the advantage of exploring both temporal and feature patterns in probabilistic time series imputation.
Do price trajectory data increase the efficiency of market impact estimation?
Market impact is an important problem faced by large institutional investor and active market participant.
Scalable Safety-Critical Policy Evaluation with Accelerated Rare Event Sampling
Evaluating rare but high-stakes events is one of the main challenges in obtaining reliable reinforcement learning policies, especially in large or infinite state/action spaces where limited scalability dictates a prohibitively large number of testing iterations.
Robust Importance Weighting for Covariate Shift
In many learning problems, the training and testing data follow different distributions and a particularly common situation is the \textit{covariate shift}.
