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Found 6 papers, 0 papers with code
LaTiM: Longitudinal representation learning in continuous-time models to predict disease progression
This work proposes a novel framework for analyzing disease progression using time-aware neural ordinary differential equations (NODE).
L-MAE: Longitudinal masked auto-encoder with time and severity-aware encoding for diabetic retinopathy progression prediction
Our results demonstrated the relevancy of both time-aware position embedding and masking strategies based on disease progression knowledge.
Composable Core-sets for Determinant Maximization: A Simple Near-Optimal Algorithm
In this work, first we provide a theoretical approximation guarantee of $O(C^{k^2})$ for the Greedy algorithm in the context of composable core-sets; Further, we propose to use a Local Search based algorithm that while being still practical, achieves a nearly optimal approximation bound of $O(k)^{2k}$; Finally, we implement all three algorithms and show the effectiveness of our proposed algorithm on standard data sets.
A Polynomial Time MCMC Method for Sampling from Continuous DPPs
We study the Gibbs sampling algorithm for continuous determinantal point processes.
Composable Core-sets for Determinant Maximization Problems via Spectral Spanners
We show that for many objective functions one can use a spectral spanner, independent of the underlying functions, as a core-set and obtain almost optimal composable core-sets.
Monte Carlo Markov Chain Algorithms for Sampling Strongly Rayleigh Distributions and Determinantal Point Processes
Strongly Rayleigh distributions are natural generalizations of product and determinantal probability distributions and satisfy strongest form of negative dependence properties.
