Emily Beth Fox
Stanford University
Conference paper
Publication Types:
Sort by year:
Hybrid^2 Neural ODE Causal Modeling
Conference paper
To appear in International Conference on Machine Learning (ICML)
Publication year: 2024
Automated Statistical Model Discovery with Language Models
Conference paper
To appear in International Conference on Machine Learning (ICML)
Publication year: 2024
Sequence Modeling with Multiresolution Convolutional Memory
Interpretable Mechanistic Representations for Meal-level Glycemic Control in the Wild
Model-based metrics: Sample-efficient estimates of predictive model subpopulation performance
Learning Insulin-Glucose Dynamics in the Wild
Adaptively Truncating Backpropagation Through Time to Control Gradient Bias
A Simple Adaptive Tracker with Reminiscences
Conference paper
Proc. IEEE International Conference on Robotics and Automation
Publication year: 2019
oi-VAE: Output Interpretable VAEs for Nonlinear Group Factor Analysis
Large-Scale Stochastic Sampling from the Probability Simplex
Conference paper
Advances in Neural Information Processing Systems 31 (NeurIPS 2018)
Publication year: 2018
Stochastic Gradient MCMC Methods for Hidden Markov Models
A Complete Recipe for Stochastic Gradient MCMC
Conference paper
Advances in Neural Information Processing Systems 28 (NIPS 2015)
Publication year: 2016
Streaming Variational Inference for Bayesian Nonparametric Mixture Models
Conference paper
International Conference on Artificial Intelligence and Statistics (AISTATS)
Publication year: 2015
Stochastic Variational Inference for Hidden Markov Models
Conference paper
Advances in Neural Information Processing Systems 27 (NIPS 2014)
Publication year: 2015
Expectation-Maximization for Learning Determinantal Point Processes
Conference paper
Advances in Neural Information Processing Systems 27 (NIPS 2014)
Publication year: 2015
Bayesian Structure Learning for Stationary Time Series
Stochastic Gradient Hamiltonian Monte Carlo
Learning the Parameters of Determinantal Point Process Kernels
Approximate Inference in Continuous Determinant Point Processes
Conference paper
Advances in Neural Information Processing Systems 26 (NIPS 2013)
Publication year: 2014
Representing Documents Through Their Readers
Parsing Epileptic Events Using a Markov Switching Process Model for Correlated Time Series
Nystrom Approximation for Large-Scale Determinantal Processes
Conference paper
International Conference on Artificial Intelligence and Statistics (AISTATS)
Publication year: 2013
Multiresolution Gaussian Processes
Conference paper
Advances in Neural Information Processing Systems 25 (NIPS 2012)
Publication year: 2013
Effective Split-Merge Monte Carlo Methods for Nonparametric Models of Sequence Data
Conference paper
Advances in Neural Information Processing Systems 25 (NIPS 2012)
Publication year: 2013
Markov Determinantal Point Processes
Hierarchical Latent Dictionaries for Models of Brain Activation
Conference paper
International Conference on Artificial Intelligence and Statistics (AISTATS)
Publication year: 2012
Sharing Features among Dynamical Systems with Beta Processes
Conference paper
Advances in Neural Information Processing Systems 22 (NIPS 2009)
Publication year: 2010
Nonparametric Bayesian Learning of Switching Linear Dynamical Systems
Conference paper
Advances in Neural Information Processing Systems 21 (NIPS 2008)
Publication year: 2009
Nonparametric Bayesian Identification of Jump Systems with Sparse Dependencies
An HDP-HMM for Systems with State Persistence
Tracking a Non-cooperative Maneuvering Target using Hierarchical Dirichlet Processes
Hierarchical Dirichlet Processes for Tracking Maneuvering Targets
Nonparametric Bayesian Methods for Large Scale Multi-Target Tracking
Detection and Localization of Material Releases with Sparse Sensor Configurations
Conference paper
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
Publication year: 2006