Test-time regression: a unifying framework for designing sequence models with associative memory. - Wang, Ke Alexander; Shi, Jiaxin; Fox, Emily B. (CoRR, 2025)

Automated Statistical Model Discovery with Language Models. - Li, Michael Y.; Fox, Emily B.; Goodman, Noah D. (2024)

Hybrid2 Neural ODE Causal Modeling and an Application to Glycemic Response. - Zou, Bob Junyi; Levine, Matthew E.; Zaharieva, Dessi P.; Johari, Ramesh; Fox, Emily B. (2024)

Hybrid Square Neural ODE Causal Modeling. - Zou, Bob Junyi; Levine, Matthew E.; Zaharieva, Dessi P.; Johari, Ramesh; Fox, Emily B. (CoRR, 2024)

Automated Statistical Model Discovery with Language Models. - Li, Michael Y.; Fox, Emily B.; Goodman, Noah D. (CoRR, 2024)

How to Build the Virtual Cell with Artificial Intelligence: Priorities and Opportunities. - Bunne, Charlotte; Roohani, Yusuf H.; Rosen, Yanay; Gupta, Ankit; Zhang, Xikun; Roed, Marcel; Alexandrov, Theo; AlQuraishi, Mohammed; Brennan, Patricia; Burkhardt, Daniel B.; Califano, Andrea; Cool, Jonah; et al. (CoRR, 2024)

CriticAL: Critic Automation with Language Models. - Li, Michael Y.; Vajipey, Vivek; Goodman, Noah D.; Fox, Emily B. (CoRR, 2024)

Learning Explainable Treatment Policies with Clinician-Informed Representations: A Practical Approach. - Ferstad, Johannes O.; Fox, Emily B.; Scheinker, David; Johari, Ramesh (CoRR, 2024)

Sequence Modeling with Multiresolution Convolutional Memory. - Shi, Jiaxin; Wang, Ke Alexander; Fox, Emily B. (2023)

Interpretable Mechanistic Representations for Meal-level Glycemic Control in the Wild. - Wang, Ke Alexander; Fox, Emily B. (2023)

Learning Absorption Rates in Glucose-Insulin Dynamics from Meal Covariates. - Wang, Ke Alexander; Levine, Matthew E.; Shi, Jiaxin; Fox, Emily B. (CoRR, 2023)

Sequence Modeling with Multiresolution Convolutional Memory. - Shi, Jiaxin; Wang, Ke Alexander; Fox, Emily B. (CoRR, 2023)

Interpretable Mechanistic Representations for Meal-level Glycemic Control in the Wild. - Wang, Ke Alexander; Fox, Emily B. (CoRR, 2023)

Neural Granger Causality. - Tank, Alex; Covert, Ian; Foti, Nicholas J.; Shojaie, Ali; Fox, Emily B. (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

Improving Reproducibility in Machine Learning Research(A Report from the NeurIPS 2019 Reproducibility Program). - Pineau, Joelle; Vincent-Lamarre, Philippe; Sinha, Koustuv; Larivière, Vincent; Beygelzimer, Alina; d'Alché-Buc, Florence; Fox, Emily B.; Larochelle, Hugo (J. Mach. Learn. Res., 2021)

It's complicated: characterizing the time-varying relationship between cell phone mobility and COVID-19 spread in the US. - Jewell, Sean; Futoma, Joseph; Hannah, Lauren; Miller, Andrew C.; Foti, Nicholas J.; Fox, Emily B. (npj Digit. Medicine, 2021)

The Convex Mixture Distribution: Granger Causality for Categorical Time Series. - Tank, Alex; Li, Xiudi; Fox, Emily B.; Shojaie, Ali (SIAM J. Math. Data Sci., 2021)

Model-based metrics: Sample-efficient estimates of predictive model subpopulation performance. - Miller, Andrew C.; Gatys, Leon A.; Futoma, Joseph; Fox, Emily B. (2021)

Breiman's two cultures: You don't have to choose sides. - Miller, Andrew C.; Foti, Nicholas J.; Fox, Emily B. (CoRR, 2021)

Model-based metrics: Sample-efficient estimates of predictive model subpopulation performance. - Miller, Andrew C.; Gatys, Leon A.; Futoma, Joseph; Fox, Emily B. (CoRR, 2021)

Granger Causality: A Review and Recent Advances. - Shojaie, Ali; Fox, Emily B. (CoRR, 2021)

Learning Insulin-Glucose Dynamics in the Wild. - Miller, Andrew C.; Foti, Nicholas J.; Fox, Emily B. (2020)

Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program). - Pineau, Joelle; Vincent-Lamarre, Philippe; Sinha, Koustuv; Larivière, Vincent; Beygelzimer, Alina; d'Alché-Buc, Florence; Fox, Emily B.; Larochelle, Hugo (CoRR, 2020)

Learning Insulin-Glucose Dynamics in the Wild. - Miller, Andrew C.; Foti, Nicholas J.; Fox, Emily B. (CoRR, 2020)

Representing and Denoising Wearable ECG Recordings. - Chan, Jeffrey; Miller, Andrew C.; Fox, Emily B. (CoRR, 2020)

Irreversible samplers from jump and continuous Markov processes. - Ma, Yi-An; Fox, Emily B.; Chen, Tianqi; Wu, Lei (Stat. Comput., 2019)

Control variates for stochastic gradient MCMC. - Baker, Jack; Fearnhead, Paul; Fox, Emily B.; Nemeth, Christopher (Stat. Comput., 2019)

Stochastic Gradient MCMC for State Space Models. - Aicher, Christopher; Ma, Yi-An; Foti, Nicholas J.; Fox, Emily B. (SIAM J. Math. Data Sci., 2019)

A Simple Adaptive Tracker with Reminiscences. - Xie, Christopher; Fox, Emily B.; Harchaoui, Zaïd (2019)

Adaptively Truncating Backpropagation Through Time to Control Gradient Bias. - Aicher, Christopher; Foti, Nicholas J.; Fox, Emily B. (2019)

Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada. (2019)

Stochastic Gradient MCMC for Nonlinear State Space Models. - Aicher, Christopher; Putcha, Srshti; Nemeth, Christopher; Fearnhead, Paul; Fox, Emily B. (CoRR, 2019)

Adaptively Truncating Backpropagation Through Time to Control Gradient Bias. - Aicher, Christopher; Foti, Nicholas J.; Fox, Emily B. (CoRR, 2019)

Modeling patterns of smartphone usage and their relationship to cognitive health. - Rauber, Jonas; Fox, Emily B.; Gatys, Leon A. (CoRR, 2019)

oi-VAE: Output Interpretable VAEs for Nonlinear Group Factor Analysis. - Ainsworth, Samuel K.; Foti, Nicholas J.; Lee, Adrian K. C.; Fox, Emily B. (2018)

Large-Scale Stochastic Sampling from the Probability Simplex. - Baker, Jack; Fearnhead, Paul; Fox, Emily B.; Nemeth, Christopher (2018)

Interpretable VAEs for nonlinear group factor analysis. - Ainsworth, Samuel K.; Foti, Nicholas J.; Lee, Adrian K. C.; Fox, Emily B. (CoRR, 2018)

Large-Scale Stochastic Sampling from the Probability Simplex. - Baker, Jack; Fearnhead, Paul; Fox, Emily B.; Nemeth, Christopher (CoRR, 2018)

Disentangled VAE Representations for Multi-Aspect and Missing Data. - Ainsworth, Samuel K.; Foti, Nicholas J.; Fox, Emily B. (CoRR, 2018)

Approximate Collapsed Gibbs Clustering with Expectation Propagation. - Aicher, Christopher; Fox, Emily B. (CoRR, 2018)

Stochastic Gradient MCMC for State Space Models. - Aicher, Christopher; Ma, Yi-An; Foti, Nicholas J.; Fox, Emily B. (CoRR, 2018)

Stochastic Gradient MCMC Methods for Hidden Markov Models. - Ma, Yi-An; Foti, Nicholas J.; Fox, Emily B. (2017)

Control Variates for Stochastic Gradient MCMC. - Baker, Jack; Fearnhead, Paul; Fox, Emily B.; Nemeth, Christopher (CoRR, 2017)

A Unified Framework for Long Range and Cold Start Forecasting of Seasonal Profiles in Time Series. - Xie, Christopher; Tank, Alex; Greaves-Tunnell, Alec; Fox, Emily B. (CoRR, 2017)

Bayesian nonparametric covariance regression. - Fox, Emily B.; Dunson, David B. (J. Mach. Learn. Res., 2015)

Guest Editors' Introduction to the Special Issue on Bayesian Nonparametrics. - Adams, Ryan P.; Fox, Emily B.; Sudderth, Erik B.; Teh, Yee Whye (IEEE Trans. Pattern Anal. Mach. Intell., 2015)

Streaming Variational Inference for Bayesian Nonparametric Mixture Models. - Tank, Alex; Foti, Nicholas J.; Fox, Emily B. (2015)

A Complete Recipe for Stochastic Gradient MCMC. - Ma, Yi-An; Chen, Tianqi; Fox, Emily B. (2015)

Bayesian Structure Learning for Stationary Time Series. - Tank, Alex; Foti, Nicholas J.; Fox, Emily B. (2015)

Modeling the complex dynamics and changing correlations of epileptic events. - Wulsin, Drausin F.; Fox, Emily B.; Litt, Brian (Artif. Intell., 2014)

Learning the Parameters of Determinantal Point Process Kernels. - Affandi, Raja Hafiz; Fox, Emily B.; Adams, Ryan P.; Taskar, Benjamin (2014)

Stochastic Gradient Hamiltonian Monte Carlo. - Chen, Tianqi; Fox, Emily B.; Guestrin, Carlos (2014)

Expectation-Maximization for Learning Determinantal Point Processes. - Gillenwater, Jennifer; Kulesza, Alex; Fox, Emily B.; Taskar, Benjamin (2014)

Stochastic variational inference for hidden Markov models. - Foti, Nicholas J.; Xu, Jason; Laird, Dillon; Fox, Emily B. (2014)

Mixed Membership Models for Time Series. - Fox, Emily B.; Jordan, Michael I. (2014)

Bayesian nonparametric models of sparse and exchangeable random graphs. - Caron, Francois; Fox, Emily B. (CoRR, 2014)

Stochastic Gradient Hamiltonian Monte Carlo. - Chen, Tianqi; Fox, Emily B.; Guestrin, Carlos (CoRR, 2014)

Learning the Parameters of Determinantal Point Process Kernels. - Affandi, Raja Hafiz; Fox, Emily B.; Adams, Ryan P.; Taskar, Ben (CoRR, 2014)

Expectation-Maximization for Learning Determinantal Point Processes. - Gillenwater, Jennifer; Kulesza, Alex; Fox, Emily B.; Taskar, Ben (CoRR, 2014)

Nystrom Approximation for Large-Scale Determinantal Processes. - Affandi, Raja Hafiz; Kulesza, Alex; Fox, Emily B.; Taskar, Ben (2013)

Parsing epileptic events using a Markov switching process model for correlated time series. - Wulsin, Drausin; Fox, Emily B.; Litt, Brian (2013)

Representing documents through their readers. - El-Arini, Khalid; Xu, Min; Fox, Emily B.; Guestrin, Carlos (2013)

Approximate Inference in Continuous Determinantal Processes. - Affandi, Raja Hafiz; Fox, Emily B.; Taskar, Ben (2013)

A Bayesian Approach for Predicting the Popularity of Tweets - Zaman, Tauhid; Fox, Emily B.; Bradlow, Eric T. (CoRR, 2013)

Mixed Membership Models for Time Series. - Fox, Emily B.; Jordan, Michael I. (CoRR, 2013)

Approximate Inference in Continuous Determinantal Point Processes. - Affandi, Raja Hafiz; Fox, Emily B.; Taskar, Ben (CoRR, 2013)

Multiresolution Gaussian Processes. - Fox, Emily B.; Dunson, David B. (2012)

Effective Split-Merge Monte Carlo Methods for Nonparametric Models of Sequential Data. - Hughes, Michael C.; Fox, Emily B.; Sudderth, Erik B. (2012)

Markov Determinantal Point Processes. - Affandi, Raja Hafiz; Kulesza, Alex; Fox, Emily B. (2012)

Hierarchical Latent Dictionaries for Models of Brain Activation. - Fyshe, Alona; Fox, Emily B.; Dunson, David B.; Mitchell, Tom M. (2012)

Concept Modeling with Superwords - El-Arini, Khalid; Fox, Emily B.; Guestrin, Carlos (CoRR, 2012)

Markov Determinantal Point Processes - Affandi, Raja Hafiz; Kulesza, Alex; Fox, Emily B. (CoRR, 2012)

Bayesian Nonparametric Inference of Switching Dynamic Linear Models. - Fox, Emily B.; Sudderth, Erik B.; Jordan, Michael I.; Willsky, Alan S. (IEEE Trans. Signal Process., 2011)

Bayesian Nonparametric Methods for Learning Markov Switching Processes. - Fox, Emily B.; Sudderth, Erik B.; Jordan, Michael I.; Willsky, Alan S. (IEEE Signal Process. Mag., 2010)

Bayesian nonparametric learning of complex dynamical phenomena. - Fox, Emily B. (2009)

Sharing Features among Dynamical Systems with Beta Processes. - Fox, Emily B.; Sudderth, Erik B.; Jordan, Michael I.; Willsky, Alan S. (2009)

An HDP-HMM for systems with state persistence. - Fox, Emily B.; Sudderth, Erik B.; Jordan, Michael I.; Willsky, Alan S. (2008)

Nonparametric Bayesian Learning of Switching Linear Dynamical Systems. - Fox, Emily B.; Sudderth, Erik B.; Jordan, Michael I.; Willsky, Alan S. (2008)

Detection and Localization of Material Releases With Sparse Sensor Configurations. - Fox, Emily B.; Fisher, John W., III; Willsky, Alan S. (IEEE Trans. Signal Process., 2007)

Hierarchical Dirichlet processes for tracking maneuvering targets. - Fox, Emily B.; Sudderth, Erik B.; Willsky, Alan S. (2007)

Detection and Localization of Material Releases with Sparse Sensor Configurations. - Fox, Emily B.; Williams, Jason L.; Fisher, John W., III; Willsky, Alan S. (2006)