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Found 8 papers, 5 papers with code
Unsupervised multimodal modeling of cognitive and brain health trajectories for early dementia prediction
In contrast to supervised classification approaches that require labeled data, we propose an unsupervised multimodal trajectory modeling (MTM) approach based on a mixture of state space models that captures changes in longitudinal data (i. e., trajectories) and stratifies individuals without using clinical diagnosis for model training.
Neuroevolutionary representations for learning heterogeneous treatment effects
In contrast to previous approaches that encourage the distribution of representations to be treatment-invariant, we leverage a genetic algorithm that optimizes over representations useful for predicting the outcome to select those less useful for predicting the treatment.
Deep Low-Density Separation for Semi-Supervised Classification
Given a small set of labeled data and a large set of unlabeled data, semi-supervised learning (SSL) attempts to leverage the location of the unlabeled datapoints in order to create a better classifier than could be obtained from supervised methods applied to the labeled training set alone.
Neuroevolutionary Feature Representations for Causal Inference
Within the field of causal inference, we consider the problem of estimating heterogeneous treatment effects from data.
Discriminative Bayesian filtering lends momentum to the stochastic Newton method for minimizing log-convex functions
To minimize the average of a set of log-convex functions, the stochastic Newton method iteratively updates its estimate using subsampled versions of the full objective's gradient and Hessian.
The Discriminative Kalman Filter for Bayesian Filtering with Nonlinear and Nongaussian Observation Models
Extensions to the Kalman filter, including the extended and unscented Kalman filters, incorporate linearizations for models where the observation model p(observation∣state) is nonlinear.
A Discriminative Approach to Bayesian Filtering with Applications to Human Neural Decoding
Given a stationary state-space model that relates a sequence of hidden states and corresponding measurements or observations, Bayesian filtering provides a principled statistical framework for inferring the posterior distribution of the current state given all measurements up to the present time.
The discriminative Kalman filter for nonlinear and non-Gaussian sequential Bayesian filtering
The Kalman filter (KF) is used in a variety of applications for computing the posterior distribution of latent states in a state space model.
