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Found 13 papers, 6 papers with code
The Price of Linear Time: Error Analysis of Structured Kernel Interpolation
Structured Kernel Interpolation (SKI) (Wilson et al. 2015) helps scale Gaussian Processes (GPs) by approximating the kernel matrix via interpolation at inducing points, achieving linear computational complexity.
PyPulse: A Python Library for Biosignal Imputation
We introduce PyPulse, a Python package for imputation of biosignals in both clinical and wearable sensor settings.
RoME: A Robust Mixed-Effects Bandit Algorithm for Optimizing Mobile Health Interventions
Mobile health leverages personalized and contextually tailored interventions optimized through bandit and reinforcement learning algorithms.
REBAR: Retrieval-Based Reconstruction for Time-series Contrastive Learning
The success of self-supervised contrastive learning hinges on identifying positive data pairs, such that when they are pushed together in embedding space, the space encodes useful information for subsequent downstream tasks.
KrADagrad: Kronecker Approximation-Domination Gradient Preconditioned Stochastic Optimization
Second order stochastic optimizers allow parameter update step size and direction to adapt to loss curvature, but have traditionally required too much memory and compute for deep learning.
SKI to go Faster: Accelerating Toeplitz Neural Networks via Asymmetric Kernels
For the low rank component, we replace the RPE MLP with linear interpolation and use asymmetric Structured Kernel Interpolation (SKI) (Wilson et.
PulseImpute: A Novel Benchmark Task for Pulsative Physiological Signal Imputation
The promise of Mobile Health (mHealth) is the ability to use wearable sensors to monitor participant physiology at high frequencies during daily life to enable temporally-precise health interventions.
Transformers for prompt-level EMA non-response prediction
The transformer model achieves a non-response prediction AUC of 0. 77 and is significantly better than classical ML and LSTM-based deep learning models.
Kernel Deformed Exponential Families for Sparse Continuous Attention
Theoretically, we show new existence results for both kernel exponential and deformed exponential families, and that the deformed case has similar approximation capabilities to kernel exponential families.
Efficient Learning and Decoding of the Continuous-Time Hidden Markov Model for Disease Progression Modeling
We solve the first challenge by reformulating the estimation problem as an equivalent discrete time-inhomogeneous hidden Markov model.
A Robust Functional EM Algorithm for Incomplete Panel Count Data
Panel count data describes aggregated counts of recurrent events observed at discrete time points.
iSurvive: An Interpretable, Event-time Prediction Model for mHealth
We present a parameter learning method for GLM emissions and survival model fitting, and present promising results on both synthetic data and an mHealth drug use dataset.
Automatic Variational ABC
Approximate Bayesian Computation (ABC) is a framework for performing likelihood-free posterior inference for simulation models.
