Point Processes
135 papers with code • 0 benchmarks • 2 datasets
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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.
Optimized Algorithms to Sample Determinantal Point Processes
The standard sampling algorithm is separated in three phases: 1/~eigendecomposition of $\mathbf{L}$, 2/~an eigenvector sampling phase where $\mathbf{L}$'s eigenvectors are sampled independently via a Bernoulli variable parametrized by their associated eigenvalue, 3/~a Gram-Schmidt-type orthogonalisation procedure of the sampled eigenvectors.
Determinantal Point Processes for Coresets
We apply our results to both the k-means and the linear regression problems, and give extensive empirical evidence that the small additional computational cost of DPP sampling comes with superior performance over its iid counterpart.
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.
Deep Random Splines for Point Process Intensity Estimation of Neural Population Data
cunningham-lab/drs
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NeurIPS 2019
Gaussian processes are the leading class of distributions on random functions, but they suffer from well known issues including difficulty scaling and inflexibility with respect to certain shape constraints (such as nonnegativity).
Neural Jump Stochastic Differential Equations
000Justin000/torchdiffeq
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NeurIPS 2019
Many time series are effectively generated by a combination of deterministic continuous flows along with discrete jumps sparked by stochastic events.
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 $\mathbf{L}$.
Effective Diversity in Population Based Reinforcement Learning
Exploration is a key problem in reinforcement learning, since agents can only learn from data they acquire in the environment.
Quantifying the Effects of Contact Tracing, Testing, and Containment Measures in the Presence of Infection Hotspots
Multiple lines of evidence strongly suggest that infection hotspots, where a single individual infects many others, play a key role in the transmission dynamics of COVID-19.
Using the Epps effect to detect discrete processes
The Epps effect is key phenomenology relating to high frequency correlation dynamics in financial markets.
