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Found 9 papers, 3 papers with code
Linking Across Data Granularity: Fitting Multivariate Hawkes Processes to Partially Interval-Censored Data
The multivariate Hawkes process (MHP) is widely used for analyzing data streams that interact with each other, where events generate new events within their own dimension (via self-excitation) or across different dimensions (via cross-excitation).
Fair Densities via Boosting the Sufficient Statistics of Exponential Families
Furthermore, when the weak learners are specified to be decision trees, the sufficient statistics of the learned distribution can be examined to provide clues on sources of (un)fairness.
UNIPoint: Universally Approximating Point Processes Intensities
The proof connects the well known Stone-Weierstrass Theorem for function approximation, the uniform density of non-negative continuous functions using a transfer functions, the formulation of the parameters of a piece-wise continuous functions as a dynamic system, and a recurrent neural network implementation for capturing the dynamics.
Interval-censored Hawkes processes
We propose the multi-impulse exogenous function - for when the exogenous events are observed as event time - and the latent homogeneous Poisson process exogenous function - for when the exogenous events are presented as interval-censored volumes.
On the Variance of the Fisher Information for Deep Learning
In the realm of deep learning, the Fisher information matrix (FIM) gives novel insights and useful tools to characterize the loss landscape, perform second-order optimization, and build geometric learning theories.
Fair Wrapping for Black-box Predictions
We introduce a new family of techniques to post-process ("wrap") a black-box classifier in order to reduce its bias.
Sampled Transformer for Point Sets
The sparse transformer can reduce the computational complexity of the self-attention layers to $O(n)$, whilst still being a universal approximator of continuous sequence-to-sequence functions.
Tempered Calculus for ML: Application to Hyperbolic Model Embedding
Most mathematical distortions used in ML are fundamentally integral in nature: $f$-divergences, Bregman divergences, (regularized) optimal transport distances, integral probability metrics, geodesic distances, etc.
Tradeoffs of Diagonal Fisher Information Matrix Estimators
The Fisher information matrix characterizes the local geometry in the parameter space of neural networks.
