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The existing Neural ODE formulation relies on an explicit knowledge of the termination time.

Post-hoc explanations of machine learning models are crucial for people to understand and act on algorithmic predictions.

Tick is a statistical learning library for Python~3, with a particular emphasis on time-dependent models, such as point processes, and tools for generalized linear models and survival analysis.

Many time series are effectively generated by a combination of deterministic continuous flows along with discrete jumps sparked by stochastic events.

In the absence of DPP machinery to derive an efficient sampler and analyze their estimator, the idea of Monte Carlo integration with DPPs was stored in the cellar of numerical integration.

For this purpose, we propose a new algorithm which, given access to $\mathbf{L}$, samples exactly from a determinantal point process while satisfying the following two properties: (1) its preprocessing cost is $n \cdot \text{poly}(k)$, i. e., sublinear in the size of $\mathbf{L}$, and (2) its sampling cost is $\text{poly}(k)$, i. e., independent of the size of $\mathbf{L}$.

Determinantal point processes (DPPs) are specific probability distributions over clouds of points that are used as models and computational tools across physics, probability, statistics, and more recently machine learning.

Previous theoretical results yield a fast mixing time of our chain when targeting a distribution that is close to a projection DPP, but not a DPP in general.

Determinantal point processes (DPPs) are elegant probabilistic models of repulsion that arise in quantum physics and random matrix theory.

In practice, the key points of point process-based sequential data modeling include: 1) How to design intensity functions to describe the mechanism behind observed data?