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Found 35 papers, 11 papers with code
Automated Statistical Model Discovery with Language Models
We evaluate our method in three common settings in probabilistic modeling: searching within a restricted space of models, searching over an open-ended space, and improving classic models under natural language constraints (e. g., this model should be interpretable to an ecologist).
Hybrid Square Neural ODE Causal Modeling
We encode this information in a causal loss that we combine with the standard predictive loss to arrive at a hybrid loss that biases our learning towards causally valid hybrid models.
Interpretable Mechanistic Representations for Meal-level Glycemic Control in the Wild
Diabetes encompasses a complex landscape of glycemic control that varies widely among individuals.
Sequence Modeling with Multiresolution Convolutional Memory
Popular approaches in the space tradeoff between the memory burden of brute-force enumeration and comparison, as in transformers, the computational burden of complicated sequential dependencies, as in recurrent neural networks, or the parameter burden of convolutional networks with many or large filters.
Learning Absorption Rates in Glucose-Insulin Dynamics from Meal Covariates
In this paper, we propose to learn the effects of macronutrition content from glucose-insulin data and meal covariates.
Granger Causality: A Review and Recent Advances
Introduced more than a half century ago, Granger causality has become a popular tool for analyzing time series data in many application domains, from economics and finance to genomics and neuroscience.
Breiman's two cultures: You don't have to choose sides
And while these categories represent extreme points in model space, modern computational and algorithmic tools enable us to interpolate between these points, producing flexible, interpretable, and scientifically-informed hybrids that can enjoy accurate and robust predictions, and resolve issues with data analysis that Breiman describes, such as the Rashomon effect and Occam's dilemma.
Model-based metrics: Sample-efficient estimates of predictive model subpopulation performance
We propose using an evaluation model $-$ a model that describes the conditional distribution of the predictive model score $-$ to form model-based metric (MBM) estimates.
Representing and Denoising Wearable ECG Recordings
In this work, we develop a statistical model to simulate a structured noise process in ECGs derived from a wearable sensor, design a beat-to-beat representation that is conducive for analyzing variation, and devise a factor analysis-based method to denoise the ECG.
Modeling patterns of smartphone usage and their relationship to cognitive health
The ubiquity of smartphone usage in many people's lives make it a rich source of information about a person's mental and cognitive state.
Adaptively Truncating Backpropagation Through Time to Control Gradient Bias
Truncated backpropagation through time (TBPTT) is a popular method for learning in recurrent neural networks (RNNs) that saves computation and memory at the cost of bias by truncating backpropagation after a fixed number of lags.
Stochastic Gradient MCMC for Nonlinear State Space Models
We evaluate our proposed particle buffered stochastic gradient using stochastic gradient MCMC for inference on both long sequential synthetic and minute-resolution financial returns data, demonstrating the importance of this class of methods.
Stochastic Gradient MCMC for State Space Models
However, inference in SSMs is often computationally prohibitive for long time series.
Approximate Collapsed Gibbs Clustering with Expectation Propagation
We develop a framework for approximating collapsed Gibbs sampling in generative latent variable cluster models.
oi-VAE: Output Interpretable VAEs for Nonlinear Group Factor Analysis
Deep generative models have recently yielded encouraging results in producing subjectively realistic samples of complex data.
Disentangled VAE Representations for Multi-Aspect and Missing Data
Many problems in machine learning and related application areas are fundamentally variants of conditional modeling and sampling across multi-aspect data, either multi-view, multi-modal, or simply multi-group.
Large-Scale Stochastic Sampling from the Probability Simplex
Unfortunately, many popular large-scale Bayesian models, such as network or topic models, require inference on sparse simplex spaces.
An Interpretable and Sparse Neural Network Model for Nonlinear Granger Causality Discovery
A sufficient condition for Granger non-causality in this setting is that all of the outgoing weights of the input data, the past lags of a series, to the first hidden layer are zero.
An Efficient ADMM Algorithm for Structural Break Detection in Multivariate Time Series
We present an efficient alternating direction method of multipliers (ADMM) algorithm for segmenting a multivariate non-stationary time series with structural breaks into stationary regions.
sgmcmc: An R Package for Stochastic Gradient Markov Chain Monte Carlo
To do this, the package uses the software library TensorFlow, which has a variety of statistical distributions and mathematical operations as standard, meaning a wide class of models can be built using this framework.
Control Variates for Stochastic Gradient MCMC
These methods use a noisy estimate of the gradient of the log posterior, which reduces the per iteration computational cost of the algorithm.
Stochastic Gradient MCMC Methods for Hidden Markov Models
Stochastic gradient MCMC (SG-MCMC) algorithms have proven useful in scaling Bayesian inference to large datasets under an assumption of i. i. d data.
A Complete Recipe for Stochastic Gradient MCMC
That is, any continuous Markov process that provides samples from the target distribution can be written in our framework.
Achieving a Hyperlocal Housing Price Index: Overcoming Data Sparsity by Bayesian Dynamical Modeling of Multiple Data Streams
Understanding how housing values evolve over time is important to policy makers, consumers and real estate professionals.
Streaming Variational Inference for Bayesian Nonparametric Mixture Models
In theory, Bayesian nonparametric (BNP) models are well suited to streaming data scenarios due to their ability to adapt model complexity with the observed data.
Stochastic Variational Inference for Hidden Markov Models
Variational inference algorithms have proven successful for Bayesian analysis in large data settings, with recent advances using stochastic variational inference (SVI).
Modeling the Complex Dynamics and Changing Correlations of Epileptic Events
A goal of our work is to parse these complex epileptic events into distinct dynamic regimes.
Learning the Parameters of Determinantal Point Process Kernels
Determinantal point processes (DPPs) are well-suited for modeling repulsion and have proven useful in many applications where diversity is desired.
Stochastic Gradient Hamiltonian Monte Carlo
Hamiltonian Monte Carlo (HMC) sampling methods provide a mechanism for defining distant proposals with high acceptance probabilities in a Metropolis-Hastings framework, enabling more efficient exploration of the state space than standard random-walk proposals.
Sparse graphs using exchangeable random measures
We show that for certain choices of such exchangeable random measures underlying our graph construction, our network process is sparse with power-law degree distribution.
Approximate Inference in Continuous Determinantal Point Processes
Determinantal point processes (DPPs) are random point processes well-suited for modeling repulsion.
Mixed Membership Models for Time Series
Although much of the literature on mixed membership models considers the setting in which exchangeable collections of data are associated with each member of a set of entities, it is equally natural to consider problems in which an entire time series is viewed as an entity and the goal is to characterize the time series in terms of a set of underlying dynamic attributes or "dynamic regimes".
Joint modeling of multiple time series via the beta process with application to motion capture segmentation
We propose a Bayesian nonparametric approach to the problem of jointly modeling multiple related time series.
Bayesian Nonparametric Inference of Switching Linear Dynamical Systems
Many complex dynamical phenomena can be effectively modeled by a system that switches among a set of conditionally linear dynamical modes.
A sticky HDP-HMM with application to speaker diarization
To address this problem, we take a Bayesian nonparametric approach to speaker diarization that builds on the hierarchical Dirichlet process hidden Markov model (HDP-HMM) of Teh et al. [J. Amer.
