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Greatest papers with code
Learning Neural Event Functions for Ordinary Differential Equations
ICLR 2021
• rtqichen/torchdiffeq
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The existing Neural ODE formulation relies on an explicit knowledge of the termination time.
Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations
• interpretml/DiCE
• 
Post-hoc explanations of machine learning models are crucial for people to understand and act on algorithmic predictions.
Tick: a Python library for statistical learning, with a particular emphasis on time-dependent modelling
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.
Neural Jump Stochastic Differential Equations
Many time series are effectively generated by a combination of deterministic continuous flows along with discrete jumps sparked by stochastic events.
On two ways to use determinantal point processes for Monte Carlo integration
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.
Exact sampling of determinantal point processes with sublinear time preprocessing
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 $\mathbf{L}$.
DPPy: Sampling DPPs with Python
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.
Zonotope hit-and-run for efficient sampling from projection DPPs
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 for machine learning
Determinantal point processes (DPPs) are elegant probabilistic models of repulsion that arise in quantum physics and random matrix theory.
PoPPy: A Point Process Toolbox Based on PyTorch
• HongtengXu/PoPPy
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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?