Approximating discrete probability distributions with causal dependence trees. - Christopher J. Quinn; Todd P. Coleman; Negar Kiyavash (ISITA, 2010)

A minimal approach to causal inference on topologies with bounded indegree. - Christopher J. Quinn; Negar Kiyavash; Todd P. Coleman (CDC/ECC, 2011)

Equiangular tight frame fingerprinting codes. - Dustin G. Mixon; Christopher J. Quinn; Negar Kiyavash; Matthew Fickus (ICASSP, 2011)

Equivalence between minimal generative model graphs and directed information graphs. - Christopher J. Quinn; Negar Kiyavash; Todd P. Coleman (ISIT, 2011)

Causal Dependence Tree Approximations of Joint Distributions for Multiple Random Processes - Christopher J. Quinn; Todd P. Coleman; Negar Kiyavash (CoRR, 2011)

Estimating the directed information to infer causal relationships in ensemble neural spike train recordings. - Christopher J. Quinn; Todd P. Coleman; Negar Kiyavash; Nicholas G. Hatsopoulos (J. Comput. Neurosci., 2011)

Robust directed tree approximations for networks of stochastic processes. - Christopher J. Quinn; Jalal Etesami; Negar Kiyavash; Todd P. Coleman (ISIT, 2013)

Optimal bounded-degree approximations of joint distributions of networks of stochastic processes. - Christopher J. Quinn; Ali Pinar; Negar Kiyavash (ISIT, 2013)

Fingerprinting With Equiangular Tight Frames. - Dustin G. Mixon; Christopher J. Quinn; Negar Kiyavash; Matthew Fickus (IEEE Trans. Inf. Theory, 2013)

Efficient Methods to Compute Optimal Tree Approximations of Directed Information Graphs. - Christopher J. Quinn; Negar Kiyavash; Todd P. Coleman (IEEE Trans. Signal Process., 2013)

Dynamic and Succinct Statistical Analysis of Neuroscience Data. - Sanggyun Kim; Christopher J. Quinn; Negar Kiyavash; Todd P. Coleman (Proc. IEEE, 2014)

Identification and approximation of the structure of networks of stochastic processes - Christopher John Quinn (2014)

Bounded Degree Approximations of Stochastic Networks. - Christopher J. Quinn; Ali Pinar; Negar Kiyavash (CoRR, 2015)

Directed Information Graphs. - Christopher J. Quinn; Negar Kiyavash; Todd P. Coleman (IEEE Trans. Inf. Theory, 2015)

On sparse approximations for time-series networks. - Christopher J. Quinn (Allerton, 2016)

Sparse approximations of directed information graphs. - Christopher J. Quinn; Ali Pinar; Jing Gao 004; Lu Su 001 (ISIT, 2016)

Crowdsourcing High Quality Labels with a Tight Budget. - Qi Li 012; Fenglong Ma; Jing Gao 004; Lu Su 001; Christopher J. Quinn (WSDM, 2016)

Bounded-Degree Connected Approximations of Stochastic Networks. - Christopher J. Quinn; Ali Pinar; Negar Kiyavash (IEEE Trans. Mol. Biol. Multi Scale Commun., 2017)

A Measure of Synergy, Redundancy, and Unique Information using Information Geometry. - Xueyan Niu; Christopher J. Quinn (ISIT, 2019)

Modeling Piece-Wise Stationary Time Series. - Daoping Wu; Suhas Gundimeda; Shaoshuai Mou; Christopher J. Quinn (ICASSP, 2020)

Synergy and Redundancy Duality Between Gaussian Multiple Access and Broadcast Channels. - Xueyan Niu; Christopher J. Quinn (ISITA, 2020)

DART: aDaptive Accept RejecT for non-linear top-K subset identification. - Mridul Agarwal; Vaneet Aggarwal; Christopher J. Quinn; Abhishek K. Umrawal (CoRR, 2020)

DART: Adaptive Accept Reject Algorithm for Non-Linear Combinatorial Bandits. - Mridul Agarwal; Vaneet Aggarwal; Abhishek Kumar Umrawal; Christopher J. Quinn (AAAI, 2021)

Stochastic Top-K Subset Bandits with Linear Space and Non-Linear Feedback. - Mridul Agarwal; Vaneet Aggarwal; Christopher J. Quinn; Abhishek K. Umrawal (ALT, 2021)

Information Flow in Markov Chains. - Xueyan Niu; Christopher J. Quinn (CDC, 2021)

Stochastic Top K-Subset Bandits with Linear Space and Non-Linear Feedback with Applications to Social Influence Maximization. - Mridul Agarwal; Vaneet Aggarwal; Abhishek K. Umrawal; Christopher J. Quinn (Trans. Data Sci., 2021)

An explore-then-commit algorithm for submodular maximization under full-bandit feedback. - Guanyu Nie; Mridul Agarwal; Abhishek Kumar Umrawal; Vaneet Aggarwal; Christopher John Quinn (UAI, 2022)

Randomized Greedy Learning for Non-monotone Stochastic Submodular Maximization Under Full-bandit Feedback. - Fares Fourati; Vaneet Aggarwal; Christopher J. Quinn; Mohamed-Slim Alouini (AISTATS, 2023)

Fractional Budget Allocation for Influence Maximization. - Abhishek K. Umrawal; Vaneet Aggarwal; Christopher J. Quinn (CDC, 2023)

A Framework for Adapting Offline Algorithms to Solve Combinatorial Multi-Armed Bandit Problems with Bandit Feedback. - Guanyu Nie; Yididiya Y. Nadew; Yanhui Zhu; Vaneet Aggarwal; Christopher John Quinn (ICML, 2023)

A Unified Approach for Maximizing Continuous DR-submodular Functions. - Mohammad Pedramfar; Christopher J. Quinn; Vaneet Aggarwal (NeurIPS, 2023)

Size-constrained k-submodular maximization in near-linear time. - Guanyu Nie; Yanhui Zhu; Yididiya Y. Nadew; Samik Basu 001; A. Pavan 001; Christopher John Quinn (UAI, 2023)

Interpreting Pretrained Source-code Models using Neuron Redundancy Analyses. - Arushi Sharma; Zefu Hu; Christopher J. Quinn; Ali Jannesari (CoRR, 2023)

A Community-Aware Framework for Social Influence Maximization. - Abhishek K. Umrawal; Christopher J. Quinn; Vaneet Aggarwal (IEEE Trans. Emerg. Top. Comput. Intell., 2023)

Combinatorial Stochastic-Greedy Bandit. - Fares Fourati; Christopher John Quinn; Mohamed-Slim Alouini; Vaneet Aggarwal (AAAI, 2024)

Unsupervised Change Point Detection in Multivariate Time Series. - Daoping Wu; Suhas Gundimeda; Shaoshuai Mou; Christopher J. Quinn (AISTATS, 2024)

Unified Projection-Free Algorithms for Adversarial DR-Submodular Optimization. - Mohammad Pedramfar; Yididiya Y. Nadew; Christopher John Quinn; Vaneet Aggarwal (ICLR, 2024)

Conditionally-Conjugate Gaussian Process Factor Analysis for Spike Count Data via Data Augmentation. - Yididiya Y. Nadew; Xuhui Fan 001; Christopher John Quinn (ICML, 2024)

Redundancy and Concept Analysis for Code-trained Language Models. - Arushi Sharma; Zefu Hu; Christopher J. Quinn; Ali Jannesari (ASE Workshops, 2024)

Gradient Methods for Online DR-Submodular Maximization with Stochastic Long-Term Constraints. - Guanyu Nie; Vaneet Aggarwal; Christopher J. Quinn (NeurIPS, 2024)

Federated Neural Nonparametric Point Processes. - Hui Chen 026; Hengyu Liu 001; Yaqiong Li; Xuhui Fan 001; Zhilin Zhao 001; Feng Zhou 011; Christopher John Quinn; Longbing Cao (CoRR, 2024)

Learning Coupled Subspaces for Multi-Condition Spike Data. - Yididiya Y. Nadew; Xuhui Fan 001; Christopher J. Quinn (CoRR, 2024)

Stochastic k-Submodular Bandits with Full Bandit Feedback. - Guanyu Nie; Vaneet Aggarwal; Christopher John Quinn (AAMAS, 2025)

Bi-Criteria Optimization for Combinatorial Bandits: Sublinear Regret and Constraint Violation under Bandit Feedback. - Vaneet Aggarwal; Shweta Jain 002; Subham Pokhriyal; Christopher John Quinn (CoRR, 2025)