WhozThat? evolving an ecosystem for context-aware mobile social networks. - Aaron Beach; Mike Gartrell; Sirisha Akkala; Jack Elston; John Kelley; Keisuke Nishimoto; Baishakhi Ray; Sergei Razgulin; Karthik Sundaresan; Bonnie Surendar; Michael Terada; Richard Han 001 (IEEE Netw., 2008)

Solutions to Security and Privacy Issues in Mobile Social Networking. - Aaron Beach; Mike Gartrell; Richard Han 001 (CSE (4), 2009)

Enhancing group recommendation by incorporating social relationship interactions. - Mike Gartrell; Xinyu Xing 001; Qin Lv; Aaron Beach; Richard Han 001; Shivakant Mishra; Karim Seada (GROUP, 2010)

Social-K: Real-time K-anonymity guarantees for social network applications. - Aaron Beach; Mike Gartrell; Richard Han 001 (PerCom Workshops, 2010)

q-Anon: Rethinking Anonymity for Social Networks. - Aaron Beach; Mike Gartrell; Richard Han 001 (SocialCom/PASSAT, 2010)

Fusing mobile, sensor, and social data to fully enable context-aware computing. - Aaron Beach; Mike Gartrell; Xinyu Xing 001; Richard Han 001; Qin Lv; Shivakant Mishra; Karim Seada (HotMobile, 2010)

q-Anon: practical anonymity for social networks. - Aaron Beach; Mike Gartrell; Richard Han 001 (Int. J. Soc. Comput. Cyber Phys. Syst., 2012)

AnchorMF: towards effective event context identification. - Hansu Gu; Mike Gartrell; Liang Zhang; Qin Lv; Dirk Grunwald (CIKM, 2013)

A large-scale exploration of group viewing patterns. - Allison June-Barlow Chaney; Mike Gartrell; Jake M. Hofman; John Guiver; Noam Koenigstein; Pushmeet Kohli; Ulrich Paquet (TVX, 2014)

Supporting Healthy Grocery Shopping via Mobile Augmented Reality. - Junho Ahn; James Williamson; Mike Gartrell; Richard Han 001; Qin Lv; Shivakant Mishra (ACM Trans. Multim. Comput. Commun. Appl., 2015)

Bayesian Low-Rank Determinantal Point Processes. - Mike Gartrell; Ulrich Paquet; Noam Koenigstein (RecSys, 2016)

Low-Rank Factorization of Determinantal Point Processes for Recommendation. - Mike Gartrell; Ulrich Paquet; Noam Koenigstein (CoRR, 2016)

The Bayesian Low-Rank Determinantal Point Process Mixture Model. - Mike Gartrell; Ulrich Paquet; Noam Koenigstein (CoRR, 2016)

Group-based Latent Dirichlet Allocation (Group-LDA): Effective audience detection for books in online social media. - Peng Zhang 060; Hansu Gu; Mike Gartrell; Tun Lu; Dayi Yang; Xianghua Ding; Ning Gu (Knowl. Based Syst., 2016)

Low-Rank Factorization of Determinantal Point Processes. - Mike Gartrell; Ulrich Paquet; Noam Koenigstein (AAAI, 2017)

Understanding Group Event Scheduling via the OutWithFriendz Mobile Application. - Shuo Zhang 009; Khaled Alanezi; Mike Gartrell; Richard Han 001; Qin Lv; Shivakant Mishra (Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 2017)

Learning Determinantal Point Processes by Sampling Inferred Negatives. - Zelda Mariet; Mike Gartrell; Suvrit Sra (CoRR, 2018)

Adversarial Training of Word2Vec for Basket Completion. - Ugo Tanielian; Mike Gartrell; Flavian Vasile (CoRR, 2018)

Basket Completion with Multi-task Determinantal Point Processes. - Romain Warlop; Jérémie Mary; Mike Gartrell (CoRR, 2018)

Deep Determinantal Point Processes. - Mike Gartrell; Elvis Dohmatob (CoRR, 2018)

Learning Determinantal Point Processes by Corrective Negative Sampling. - Zelda Mariet; Mike Gartrell; Suvrit Sra (AISTATS, 2019)

Tensorized Determinantal Point Processes for Recommendation. - Romain Warlop; Jérémie Mary; Mike Gartrell (KDD, 2019)

Learning Nonsymmetric Determinantal Point Processes. - Mike Gartrell; Victor-Emmanuel Brunel; Elvis Dohmatob; Syrine Krichene (NeurIPS, 2019)

Embedding models for recommendation under contextual constraints. - Syrine Krichene; Mike Gartrell; Clément Calauzènes (CoRR, 2019)

GEVR: An Event Venue Recommendation System for Groups of Mobile Users. - Jason Shuo Zhang; Mike Gartrell; Richard Han 001; Qin Lv; Shivakant Mishra (Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 2019)

Wasserstein Learning of Determinantal Point Processes. - Lucas Anquetil; Mike Gartrell; Alain Rakotomamonjy; Ugo Tanielian; Clément Calauzènes (CoRR, 2020)

Scalable Learning and MAP Inference for Nonsymmetric Determinantal Point Processes. - Mike Gartrell; Insu Han; Elvis Dohmatob; Jennifer Gillenwater; Victor-Emmanuel Brunel (ICLR, 2021)

Bayesian Causal Inference for Real World Interactive Systems. - Nicolas Chopin; Mike Gartrell; Dawen Liang; Alberto Lumbreras; David Rohde; Yixin Wang 002 (KDD, 2021)

Combining Reward and Rank Signals for Slate Recommendation. - Imad Aouali; Sergey Ivanov; Mike Gartrell; David Rohde; Flavian Vasile; Victor Zaytsev; Diego Legrand (CoRR, 2021)

Scalable Sampling for Nonsymmetric Determinantal Point Processes. - Insu Han; Mike Gartrell; Jennifer Gillenwater; Elvis Dohmatob; Amin Karbasi (ICLR, 2022)

Scalable MCMC Sampling for Nonsymmetric Determinantal Point Processes. - Insu Han; Mike Gartrell; Elvis Dohmatob; Amin Karbasi (ICML, 2022)

Learning from Multiple Sources for Data-to-Text and Text-to-Data. - Song Duong; Alberto Lumbreras; Mike Gartrell; Patrick Gallinari (AISTATS, 2023)

Unifying GANs and Score-Based Diffusion as Generative Particle Models. - Jean-Yves Franceschi; Mike Gartrell; Ludovic Dos Santos; Thibaut Issenhuth; Emmanuel de Bézenac; Mickaël Chen; Alain Rakotomamonjy (NeurIPS, 2023)

Evaluating the Generalization Property of Prefix-based Methods for Data-to-text Generation. - Clarine Vongpaseut; Alberto Lumbreras; Mike Gartrell; Patrick Gallinari (CORIA-TALN (2), 2023)

Differentially Private Gradient Flow based on the Sliced Wasserstein Distance for Non-Parametric Generative Modeling. - Ilana Sebag; Muni Sreenivas Pydi; Jean-Yves Franceschi; Alain Rakotomamonjy; Mike Gartrell; Jamal Atif; Alexandre Allauzen (CoRR, 2023)

Differentially Private Gradient Flow based on the Sliced Wasserstein Distance. - Ilana Sebag; Muni Sreenivas Pydi; Jean-Yves Franceschi; Alain Rakotomamonjy; Mike Gartrell; Jamal Atif; Alexandre Allauzen (Trans. Mach. Learn. Res., 2025)