Publications

Journal and Preprints

  1. Graph-Theoretic Analysis of Belief System Dynamics under Logic Constraints 
    A. Nedić, A. Olshevsky, and C. A. Uribe
    Submitted
  2. A Dual Approach for Optimal Algorithms in Distributed Optimization over Networks
    C. A. Uribe, S. Lee, A. Gasnikov and A. Nedić
    Submitted
  3. Distributed Learning for Cooperative Inference
    A. Nedić, A. Olshevsky, and C. A. Uribe
    preprint
  4. Optimal Distributed Optimization on Slowly Time-Varying Graphs
    A. Rogozin, C.A. Uribe, A. Gasnikov, N. Malkovsky, A. Nedić
    Submitted
  5. On Curvature-aided Incremental Aggregated Gradient Methods
    H.T. Wai, W. Shi, C.A. Uribe, A. Nedić, A. Scaglione
    Submitted
  6. Fast Convergence Rates for Distributed Non-Bayesian Learning
    A. Nedić, A. Olshevsky, and C. A. Uribe
    IEEE Transactions on Automatic Control

Conference Papers

  1. On Increasing Self-Confidence in Non-Bayesian Social Learning over Time-Varying Directed Graphs
    C.A. Uribe, and A. Jadbabaie
    Submitted to ACC 2019.
  2. Achieving Acceleration in Distributed Optimization via Direct Discretization of the Heavy-Ball ODE
    J. Zhang, C.A. Uribe, A. Mokhtari, and A. Jadbabaie
    Submitted to ACC 2019.
  3. Decentralize and Randomize: Faster Algorithm for Wasserstein Barycenters
    P. Dvurechensky, D. Dvinskikh, A. Gasnikov, C.A. Uribe, and A. Nedić
    NIPS 2018 (Spotlight)
  4. A Method for Distributed Transactive Control in Power Systems based on the Projected Consensus Algorithm
    E. Baron-Prada, C.A. Uribe, and E. Mojica-Nava
    NecSys 2018.
  5. Distributed Computation of Wasserstein Barycenters over Networks
    C.A. Uribe, D. Dvinskikh, P. Dvurechensky, A. Gasnikov, and A. Nedić
    IEEE Control and Decision Conference (CDC), 2018.
  6. Geometrically Convergent Distributed Optimization with Uncoordinated Step-Sizes
    A. Nedić, A. Olshevsky, W.Shi, and C. A. Uribe
    American Control Conference (ACC), 2017.
  7. A Tutorial on Distributed (Non-Bayesian) Learning: Problem, Algorithms and Results
    A. Nedić, A. Olshevsky, and C. A. Uribe
    IEEE Control and Decision Conference (CDC), 2016.
  8. Distributed Learning with Infinitely Many Hypotheses
    A. Nedić, A. Olshevsky, and C. A. Uribe
    IEEE Control and Decision Conference (CDC), 2016.
  9. Distributed Gaussian Learning over Time-varying Directed Graphs
    A. Nedić, A. Olshevsky, and C. A. Uribe
    Asilomar Conference on Signals, Systems, and Computers, 2016.
  10. Network Independent Rates in Distributed Learning
    A. Nedić, A. Olshevsky, and C. A. Uribe
    American Control Conference (ACC), 2016. Best Presentation in the Learning and Estimation in Networks Session.
  11. Nonasymptotic Convergence Rates for Cooperative Learning Over Time-Varying Directed Graphs
    A. Nedić, A. Olshevsky, and C. A. Uribe
    American Control Conference (ACC), 2015.
  12. Computing Optimal Control Laws for Finite Stochastic Systems with Non-Classical Information Patterns.
    C. A. Uribe, T. Keviczky, and J. H. van Schuppen.
    American Control Conference (ACC), 2014, Best Presentation in the Decentralized Control Session.
  13. Analysis of Signaling in a Finite Stochastic System Motivated by Decentralized Control.
    C. A. Uribe, and J. H. van Schuppen
    IEEE Control and Decision Conference (CDC), 2013

Talks and Posters

  1. Using Second-order Information To Accelerate Incremental Gradient Methods
    Informs Annual Meeting, 2018.
  2. A Dual Approach for Optimal Algorithms for Distributed Optimization over Networks
    Informs Annual Meeting, 2018
  3. Accelerated Curvature-aided Incremental Aggregated Gradient Method
    23rd International Symposium on Mathematical Programming, ISMP 2018.
  4. Optimal Distributed Optimization on Slowly Time-Varying Graphs,
    23rd International Symposium on Mathematical Programming, ISMP 2018.
  5. Distributed Computation of Wasserstein Barycenters over Networks,
    23rd International Symposium on Mathematical Programming, ISMP 2018.
  6. Optimal Algorithms for Distributed Optimization
    23rd International Symposium on Mathematical Programming, ISMP 2018.
  7. A Dual Approach for Optimal Algorithms for Distributed Optimization over Networks
    CSL Student Conference, 2018. Best Talk Award.
  8. A Dual Approach for Optimal Algorithms for Distributed Optimization over Networks
    7th Midwest Workshop on Control and Game Theory, 2018. Best Poster Award.
  9. A Dual Approach for Optimal Algorithms for Distributed Optimization over Networks
    Midwest ML Symposium, 2018. (Spotlight) Best Poster Award.
  10. Fast algorithms for distributed optimization
    Information Theory and Applications Workshop, ITA 2018.
  11. Distributed Optimization: from consensus to learning (Workshop Organizer)
    IEEE Colombian Conference on Automatic Control, October 2017.
  12. Distributed Learning for Cooperative Inference
    Science Park Informal Probability Meetings, CWI, June 2017
  13. Distributed Learning for Cooperative Inference
    DCSC Lunch Colloquium, Delft University of Technology, June 2017
  14. Distributed Learning for Cooperative Inference
    LCCC Seminar, Lund University, June 2017
  15. Distributed Learning: Parameter Estimation for the Exponential Family
    SIAM Conference on Optimization, 2017.
  16. Distributed Learning for Cooperative Inference
    SAMSI Workshop on the Interface of Statistics and Optimization, WISO 2017.
  17. Distributed Learning for Cooperative Inference
    Duke University, Prof. Zavlanos’ Research Group, 2017.
  18. Non-asymptotic Convergence Rate for Distributed Learning in Graphs
    The fifth International Conference on Continuous Optimization, ICCOPT 2016.
  19. Non-asymptotic Rates in Distributed Learning
    2016 SIAM Annual Meeting (AN16).
  20. Convergence Rates in Distributed Learning: Acceleration, Network Independence and Uniform Social Sampling.
    Stochastic Networks Conference 2016. Outstanding Poster Award.
  21. Fast Rates and Network Independence in Distributed Learning
    5th Midwest Workshop in Control and Game Theory, 2016.
  22. Fast Rates and Network Independence in Distributed Learning
    2016 CSL Student Conference, 2016. Best Talk Award.
  23. Distributed Non-Bayesian Learning: Fast Convergence Rates and Time-Varying Directed Graphs
    IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), 2015. Workshop on Large-Scale Bayesian Data Fusion and Consensus.

Book Chapters

  1. Signaling of Information.
    C. A. Uribe, and J. H. van Schuppen.
    Coordination Control of Distributed Systems, Lecture Notes Series Control and Information Sciences (LNCIS), Springer, 2014.
  2. Unsupervised feature selection based on fuzzy clustering for fault detection of the Tennessee Eastman process.
    C. Bedoya, C. Uribe, and C. Isaza.
    13th Ibero-American Conference on AI (IBERAMIA). Lecture Notes in Computer Science, Springer, 2012.

Others (Undergraduate Research)

  1. Unsupervised feature selection based on fuzzy partition optimization for industrial processes monitoring.
    C. Uribe and C. Isaza.
    IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA), 2011.
  2. Qualitative-fuzzy decision support system for monitoring patients with cardiovascular risk.
    C. Uribe, C. Isaza, and J. Florez-Arango.
    IEEE Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), 2011.
  3. A wrapper approach based on clustering for sensors selection of industrial monitoring systems.
    C. Uribe, C. Isaza, O. Gualdron, C. Duran, and A. Carvajal.
    International Conference on Broadband, Wireless Computing, Communication and Applications (BWCCA), 2010.
  4. Expert knowledge-guided feature selection for data-based industrial process monitoring.
    C. Uribe, and C. Isaza.
    Revista Facultad Ingeniería Universidad de Antioquia N.° 65, 2012.
  5. Fast Variable Selection Based on Stochastic Methods (Simulated Annealing) Coupled to Least Square Support Vector Machines: Application to Multisensor System (In Spanish)
    O. Gualdron, C. Duran, C. Isaza, A. Carvajal, and C. Uribe.
    VIII Congreso Internacional Electrónica y Tecnologías de Avanzada, 2011.
  6. Low Cost Electronic Nose System for Detecting Different Chemicals Pollutants (In Spanish).
    C. Duran, O. Gualdron, C. Isaza, A. Carvajal and C. Uribe.
    Revista Colombiana de Tecnologías de Avanzada, 2011.
  7. Data Acquisition, Analysis and Processing Tool for Multisensory Systems and Mass Spectrometry” (In Spanish).
    C. Duran, O. Gualdron, C. Isaza, A. Carvajal, and C. Uribe.
    7th International Congress of Electrical Electronic and Systems Engineering, (INTERCON)2010.
  8. Integration methodology of face detection and speech recognition.
    C. Uribe, and C. Isaza.
    International Conference on Image Processing, Computer Vision, & Pattern Recognition, (IPCV), 2009.
  9. High School Robotics as a Research Skills developing process. (In Spanish).
    C. Uribe, and O. Carrillo, D. Fernandez.
    Sixth World Congress of Scientific Youth, International Federation of Scientific Societies, 2009.
  10. Algoritmo de Ubicación y Conducta en Entornos Conocidos con Condiciones Iniciales Desconocidas (In Spanish).
    C. Uribe, J. Marín, A. Pedraza, L. Arango, and C. Madrigal.
    CINTEX Journal, Vol. 13. 2008.