Review of "The Analysis of Feedback Systems" by Jan C. Willems. - Mathukumalli Vidyasagar (IEEE Trans. Syst. Man Cybern., 1972)

Review of "Nonlinear System Theory-A Functional Analysis Approach" by Jack M. Holtzman. - Mathukumalli Vidyasagar (IEEE Trans. Syst. Man Cybern., 1972)

New algorithms for constrained minimax optimization. - S. R. K. Dutta; Mathukumalli Vidyasagar (Math. Program., 1977)

Review of "Qualitative Analysis of Large Scale Dynamical Systems" by Anthony N. Michel and Richard K. Miller. - Mathukumalli Vidyasagar (IEEE Trans. Syst. Man Cybern., 1980)

Algebraic and topological aspects of the regulator problem for lumped linear systems. - Bruce A. Francis; M. Vidyasagar (Autom., 1983)

Maximal lyapunov functions and domains of attraction for autonomous nonlinear systems. - Anthony Vannelli; M. Vidyasagar (Autom., 1985)

Reliable stabilization using a multi-controller configuration. - M. Vidyasagar; N. Viswanadham (Autom., 1985)

Nonlinear systems: Stability analysis. - Jake K. Aggarwal; Mathukumalli Vidyasagar (IEEE Trans. Syst. Man Cybern., 1985)

Control of a flexible robot arm using the stable factorization approach. - M. Vidyasagar; Y. Xiong (ICRA, 1986)

Infinite gain margin problem in multivariable feedback systems. - H. Maeda; M. Vidyasagar (Autom., 1986)

Robust controllers for uncertain linear multivariable systems. - M. Vidyasagar; H. Kimura (Autom., 1986)

New directions of research in nonlinear system theory. - Mathukumalli Vidyasagar (Proc. IEEE, 1986)

Some qualitative results on the collision-free joint space of a planar n-DOF linkage. - Y. C. Chen; M. Vidyasagar (ICRA, 1987)

Control of a flexible robot arm with bounded input: Optimum step responses. - I. Y. Shung; M. Vidyasagar (ICRA, 1987)

Control of a flexible beam for optimum step response. - David Wang 001; M. Vidyasagar (ICRA, 1987)

Parametric conditions for stability of reduced-order linear time-varying control systems. - C. C. H. Ma; M. Vidyasagar (Autom., 1987)

Some results on simultaneous stabilization with multiple domains of stability. - M. Vidyasagar (Autom., 1987)

Robust linear compensator design for nonlinear robotic control. - Mark W. Spong; Mathukumalli Vidyasagar (IEEE J. Robotics Autom., 1987)

Optimal trajectory planning for planar n-link revolute manipulators in the presence of obstacles. - Yao-Chon Chen; Mathukumalli Vidyasagar (ICRA, 1988)

Control of a single-link flexible beam using a Hankel-norm-based reduced order model. - H. Krishnan; Mathukumalli Vidyasagar (ICRA, 1988)

Modelling of a 5-bar-linkage manipulator with one flexible link. - David Wang 001; Mathukumalli Vidyasagar (ICRA, 1988)

Passive control of a single flexible link. - David Wang 001; M. Vidyasagar (ICRA, 1990)

A new path planning algorithm for moving a point object amidst unknown obstacles in a plane. - A. Sankaranarayanan; M. Vidyasagar (ICRA, 1990)

Improved neural networks for analog to digital conversion. - M. Vidyasagar (IJCNN, 1990)

An analysis of the flows of neural networks with linear interconnections. - M. Vidyasagar (IJCNN, 1990)

Optimal control of robotic manipulators in the presence of obstacles. - Yao-Chon Chen; Mathukumalli Vidyasagar (J. Field Robotics, 1990)

Observer-controller stabilization of a class of manipulators with a single flexible link. - David Wang 001; Mathukumalli Vidyasagar (ICRA, 1991)

Passivity of flexible beam transfer functions with modified outputs. - Hemanshu Roy Pota; M. Vidyasagar (ICRA, 1991)

Path planning for moving a point object amidst unknown obstacles in a plane: the universal lower bound on the worst path lengths and a classification of algorithms. - A. Sankaranarayanan; M. Vidyasagar (ICRA, 1991)

New techniques for H2 optimal control of a flexible beam. - David Vinke; M. Vidyasagar (ICRA, 1991)

Transfer Functions for a Single Flexible Link. - David Wang 001; Mathukumalli Vidyasagar (Int. J. Robotics Res., 1991)

A new parallelogram linkage configuration for gravity compensation using torsional springs. - Ajay Gopalswamy; Pramod Gupta; M. Vidyasagar (ICRA, 1992)

Modeling a class of multilink manipulators with the last link flexible. - David Wang 001; Mathukumalli Vidyasagar (IEEE Trans. Robotics Autom., 1992)

Convergence of higher-order neural networks with modified updating. - M. Vidyasagar (ICNN, 1993)

Location and stability of the high-gain equilibria of nonlinear neural networks. - Mathukumalli Vidyasagar (IEEE Trans. Neural Networks, 1993)

Minimum-seeking properties of analog neural networks with multilinear objective functions. - Mathukumalli Vidyasagar (IEEE Trans. Autom. Control., 1995)

A Brief History of the Graph Topology*. - M. Vidyasagar (Eur. J. Control, 1996)

Learning decision rules for pattern classification under a family of probability measures. - Sanjeev R. Kulkarni; M. Vidyasagar (IEEE Trans. Inf. Theory, 1997)

Synthesis of fault-tolerant feedforward neural networks using minimax optimization. - Dipti Deodhare; M. Vidyasagar; S. Sathiya Keerthi (IEEE Trans. Neural Networks, 1998)

Some contributions to fixed-distribution learning theory. - M. Vidyasagar; Sanjeev R. Kulkarni (IEEE Trans. Autom. Control., 2000)

System identification: a learning theory approach. - Mathukumalli Vidyasagar; Rajeeva Laxman Karandikar (CDC, 2001)

Randomized algorithms for robust controller synthesis using statistical learning theory. - M. Vidyasagar (Autom., 2001)

Probabilistic solutions to some NP-hard matrix problems. - M. Vidyasagar; Vincent D. Blondel (Autom., 2001)

Randomized Algorithms for Robust Controller Synthesis Using Statistical Learning Theory: A Tutorial Overview. - Mathukumalli Vidyasagar (Eur. J. Control, 2001)

Learning with prior information. - Marco C. Campi; Mathukumalli Vidyasagar (IEEE Trans. Autom. Control., 2001)

Some challenges in computational biology. - M. Vidyasagar (ECC, 2003)

Synthesis of hidden Markov models based on finite sample paths and applications to computational biology. - M. Vidyasagar (ECC, 2003)

Convergence of Empirical Means with Alpha-Mixing Input Sequences, and an Application to PAC Learning. - M. Vidyasagar (CDC/ECC, 2005)

The Realization Problem for Hidden Markov Models: The Complete Realization Problem. - M. Vidyasagar (CDC/ECC, 2005)

The 4M (Mixed Memory Markov Model) Algorithm for Finding Genes from Prokaryote Genomes. - M. Vidyasagar (CDC, 2006)

Bimodal Projection-based Features for Pattern Classification. - Dipti Deodhare; M. Vidyasagar; M. Narasimha Murty (IJCNN, 2006)

Predicting protein-protein interactions in E. coli using machine learning methods. - Kshama Goyal; M. Vidyasagar (CDC, 2007)

Bounds on the kullback-leibler divergence rate between hidden markov models. - M. Vidyasagar (CDC, 2007)

A Unified Approach to Encoding and Classification Using Bimodal Projection-Based Features. - Dipti Deodhare; M. Vidyasagar; M. Narasimha Murty (ICCTA, 2007)

Guest Editorial - Special Issue on Systems Biology. - Mustafa Khammash; Claire J. Tomlin; M. Vidyasagar (IEEE Trans. Autom. Control., 2008)

The 4M (Mixed Memory Markov Model) Algorithm for Finding Genes in Prokaryotic Genomes. - Mathukumalli Vidyasagar; Sharmila S. Mande; Ch. V. Siva K. Reddy; V. V. Raja Rao (IEEE Trans. Autom. Control., 2008)

An elementary derivation of the large deviation rate function for finite state Markov chains. - Mathukumalli Vidyasagar (CDC, 2009)

Learning, System Identification, and Complexity. - M. Vidyasagar (Encyclopedia of Complexity and Systems Science, 2009)

Kullback-Leibler divergence rate between probability distributions on sets of different cardinalities. - Mathukumalli Vidyasagar (CDC, 2010)

Reduced-order modeling of Markov and hidden Markov processes via aggregation. - Mathukumalli Vidyasagar (CDC, 2010)

A recursive learning algorithm for model reduction of Hidden Markov Models. - Kun Deng; Prashant G. Mehta; Sean P. Meyn; Mathukumalli Vidyasagar (CDC/ECC, 2011)

A metric between probability distributions on finite sets of different cardinalities. - M. Vidyasagar (CDC/ECC, 2011)

Optimal order reduction of probability distributions by maximizing mutual information. - M. Vidyasagar (CDC/ECC, 2011)

Compact sets in the graph topology and applications to approximation of system design. - Yutaka Yamamoto; Mathukumalli Vidyasagar (CDC/ECC, 2011)

Metrics Between Probability Distributions on Finite Sets of Different Cardinalities by Maximizing Mutual Information (MMI) - Mathukumalli Vidyasagar (CoRR, 2011)

Probabilistic Methods in Cancer Biology. - Mathukumalli Vidyasagar (Eur. J. Control, 2011)

The complete realization problem for hidden Markov models: a survey and some new results. - M. Vidyasagar (Math. Control. Signals Syst., 2011)

Control System Synthesis: A Factorization Approach, Part I - Mathukumalli Vidyasagar (Control System Synthesis Part I, 2011)

Control System Synthesis: A Factorization Approach, Part II - Mathukumalli Vidyasagar (Control System Synthesis Part II, 2011)

A novel application of mixing coefficients for reverse-engineering gene interaction networks. - Nitin K. Singh; Mehmet Eren Ahsen; Shiva Mankala; M. Vidyasagar; Michael A. White (Allerton Conference, 2012)

A new feature selection algorithm for two-class classification problems and application to endometrial cancer. - Mehmet Eren Ahsen; Nitin K. Singh; Todd P. Boren; M. Vidyasagar; Michael A. White (CDC, 2012)

Convergence and compactness of families of proper plants in the graph topology. - Mathukumalli Vidyasagar; Yutaka Yamamoto (CDC, 2012)

Inferring weighted and directed gene interaction networks from gene expression data using the phi-mixing coefficient. - Nitin K. Singh; Mehmet Eren Ahsen; Shiva Mankala; M. Vidyasagar; Michael A. White (GENSiPS, 2012)

A Metric Between Probability Distributions on Finite Sets of Different Cardinalities and Applications to Order Reduction. - Mathukumalli Vidyasagar (IEEE Trans. Autom. Control., 2012)

Computational Cancer Biology - An Interaction Network Approach - Mathukumalli Vidyasagar (Springer, 2012)

On the computation of mixing coefficients between discrete-valued random variables. - Mehmet Eren Ahsen; M. Vidyasagar (ASCC, 2013)

Near-ideal behavior of a modified Elastic Net algorithm. - Mathukumalli Vidyasagar (CDC, 2013)

A sparse Bayesian learning based approach to Inferring Gene Regulatory Networks. - Nitin K. Singh; Aishwarya Sundaresan; Mathukumalli Vidyasagar (GlobalSIP, 2013)

Near-ideal behavior of compressed sensing algorithms. - Mehmet Eren Ahsen; Mathukumalli Vidyasagar (CDC, 2014)

Near-ideal behavior of some compressed sensing algorithms. - Mehmet Eren Ahsen; Mathukumalli Vidyasagar (ECC, 2014)

Inferring Gene Regulatory Networks with Sparse Bayesian Learning and phi-mixing coefficient. - Nitin K. Singh; M. Vidyasagar (ECC, 2014)

Machine Learning Methods in the Computational Biology of Cancer. - Mathukumalli Vidyasagar (CoRR, 2014)

Mixing Coefficients Between Discrete and Real Random Variables: Computation and Properties. - Mehmet Eren Ahsen; Mathukumalli Vidyasagar (IEEE Trans. Autom. Control., 2014)

A PAC learning approach to one-bit compressed sensing. - Mehmet Eren Ahsen; Mathukumalli Vidyasagar (ACC, 2015)

Optimized prediction of extreme treatment outcomes in ovarian cancer. - Burook Misganaw; Mehmet Eren Ahsen; Nitin K. Singh; Keith A. Baggerly; Anna Unruh; Michael A. White; Mathukumalli Vidyasagar (CDC, 2015)

Learning Theory. - Mathukumalli Vidyasagar (Encyclopedia of Systems and Control, 2015)

A transfer learning approach for integrating biological data across platforms. - Hema Kumari Achanta; Burook Misganaw; M. Vidyasagar (ACC, 2016)

A robust platform-independent gene signature for single-sample breast cancer subtyping. - Burook Misganaw; Mathukumalli Vidyasagar (ACC, 2016)

Sparse Feature Selection for Classification and Prediction of Metastasis in Endometrial Cancer: Extended Abstract. - Mehmet Eren Ahsen; Todd P. Boren; Nitin K. Singh; Burook Misganaw; Jayanthi S. Lea; David S. Miller; Michael A. White; Mathukumalli Vidyasagar (BCB, 2016)

Modelling drug response and resistance in cancer: Opportunities and challenges. - Niharika Challapalli; Mehmet Eren Ahsen; Mathukumalli Vidyasagar (CDC, 2016)

A tutorial introduction to compressed sensing. - M. Vidyasagar (CDC, 2016)

Continuous Hands-off Control by CLOT Norm Minimization. - Niharika Challapalli; Masaaki Nagahara; Mathukumalli Vidyasagar (CoRR, 2016)

bLARS: An Algorithm to Infer Gene Regulatory Networks. - Nitin K. Singh; Mathukumalli Vidyasagar (IEEE ACM Trans. Comput. Biol. Bioinform., 2016)

A fast single-pass algorithm for compressive sensing based on binary measurement matrices. - Mahsa Lotfi; Mathukumalli Vidyasagar (Allerton, 2017)

A multi-view ℓ1-norm SVM algorithm for data integration in biological applications. - Hema Kumari Achanta; Burook Misganaw; Mathukumalli Vidyasagar (ACC, 2017)

Integrating biological data across multiple platforms using importance-weighted transfer learning and applications to breast cancer data sets. - Hema Kumari Achanta; Burook Misganaw; M. Vidyasagar (CCTA, 2017)

Prediction of Time to Tumor Recurrence in Ovarian Cancer: Comparison of Three Sparse Regression Methods. - Mahsa Lotfi; Burook Misganaw; Mathukumalli Vidyasagar (ISBRA, 2017)

Machine learning methods in computational cancer biology. - Mathukumalli Vidyasagar (Annu. Rev. Control., 2017)

CLOT Norm Minimization for Continuous Hands-off Control. - Niharika Challapalli; Masaaki Nagahara; Mathukumalli Vidyasagar (CoRR, 2017)

Two New Approaches to Compressed Sensing Exhibiting Both Robust Sparse Recovery and the Grouping Effect. - Mehmet Eren Ahsen; Niharika Challapalli; Mathukumalli Vidyasagar (J. Mach. Learn. Res., 2017)

Array LDPC Code-based Compressive Sensing. - Mahsa Lotfi; Mathukumalli Vidyasagar (Allerton, 2018)

Inferring Genome-Wide Interaction Networks Using the Phi-Mixing Coefficient, and Applications to Lung and Breast Cancer. - Nitin K. Singh; Mehmet Eren Ahsen; Niharika Challapalli; Hyun-Seok Kim; Michael A. White; Mathukumalli Vidyasagar (IEEE Trans. Mol. Biol. Multi Scale Commun., 2018)

A Fast Noniterative Algorithm for Compressive Sensing Using Binary Measurement Matrices. - Mahsa Lotfi; Mathukumalli Vidyasagar (IEEE Trans. Signal Process., 2018)

Construction of High-Degree Ramanujan Graphs With Applications to Matrix Completion. - Shantanu Prasad Burnwal; Mathukumalli Vidyasagar (ACC, 2019)

Completion of Rectangular Matrices Using Asymmetric Ramanujan Graphs. - Shantanu Prasad Burnwal; Mathukumalli Vidyasagar (CDC, 2019)

Deterministic Construction of Bipolar Matrices For Compressed Sensing. - Shashank Ranjan; Mathukumalli Vidyasagar (CDC, 2019)

Deterministic Completion of Rectangular Matrices Using Asymmetric Ramanujan Graphs. - Shantanu Prasad Burnwal; Mathukumalli Vidyasagar (CoRR, 2019)

Deterministic Completion of Rectangular Matrices Using Ramanujan Bigraphs - II: Explicit Constructions and Phase Transitions. - Shantanu Prasad Burnwal; Mathukumalli Vidyasagar; Kaneenika Sinha (CoRR, 2019)

Editorial to the Special Issue of L-CSS on Control and Network Theory for Biological Systems. - Murat Arcak; Franco Blanchini; M. Vidyasagar (IEEE Control. Syst. Lett., 2019)

An Approach to One-Bit Compressed Sensing Based on Probably Approximately Correct Learning Theory. - Mehmet Eren Ahsen; Mathukumalli Vidyasagar (J. Mach. Learn. Res., 2019)

Tight Performance Bounds for Compressed Sensing With Conventional and Group Sparsity. - Shashank Ranjan; Mathukumalli Vidyasagar (IEEE Trans. Signal Process., 2019)

Exact Completion of Rectangular Matrices Using Ramanujan Bigraphs. - Shantanu Prasad Burnwal; Mathukumalli Vidyasagar (ACC, 2020)

For Matrix Recovery, Rank Restricted Isometry Property and Robust Uniform Boundedness Property Imply Rank Robust Null Space Property. - Shashank Ranjan; Mathukumalli Vidyasagar (ACC, 2020)

Recent Advances in Reinforcement Learning. - M. Vidyasagar (ACC, 2020)

Deterministic Completion of Rectangular Matrices With Measurement Noise Using Unbalanced Ramanujan Bigraphs. - Shantanu Prasad Burnwal; Mathukumalli Vidyasagar (CDC, 2020)

Modelling a pandemic with asymptomatic patients, impact of lockdown and herd immunity, with applications to SARS-CoV-2. - Santosh Ansumali; Shaurya Kaushal; Aloke Kumar; Meher K. Prakash; M. Vidyasagar (Annu. Rev. Control., 2020)

CLOT norm minimization for continuous hands-off control. - Masaaki Nagahara; Debasish Chatterjee; Niharika Challapalli; Mathukumalli Vidyasagar (Autom., 2020)

Deterministic Completion of Rectangular Matrices Using Asymmetric Ramanujan Graphs: Exact and Stable Recovery. - Shantanu Prasad Burnwal; Mathukumalli Vidyasagar (IEEE Trans. Signal Process., 2020)

Compressed Sensing Using Binary Matrices of Nearly Optimal Dimensions. - Mahsa Lotfi; Mathukumalli Vidyasagar (IEEE Trans. Signal Process., 2020)

SUTRA: An Approach to Modelling Pandemics with Undetected (Asymptomatic) Patients, and Applications to COVID-19. - Manindra Agrawal; Madhuri Kanitkar; Mathukumalli Vidyasagar (CDC, 2021)

Convergence of Batch Asynchronous Stochastic Approximation With Applications to Reinforcement Learning. - Rajeeva Laxman Karandikar; M. Vidyasagar (CoRR, 2021)

A New Converse Lyapunov Theorem for Global Exponential Stability and Applications to Stochastic Approximation. - M. Vidyasagar (CDC, 2022)

Estimating large causal polytree skeletons from small samples. - Sourav Chatterjee; Mathukumalli Vidyasagar (CoRR, 2022)

Modified Error Bounds for Matrix Completion and Application to RL. - Shantanu Prasad Burnwal; Mathukumalli Vidyasagar (IEEE Control. Syst. Lett., 2022)

A prospective evaluation of breast thermography enhanced by a novel machine learning technique for screening breast abnormalities in a general population of women presenting to a secondary care hospital. - Richa Bansal; Sathiakar Collison; Lakshmi Krishnan; Bharat Aggarwal; Mathukumalli Vidyasagar; Siva Teja Kakileti; Geetha Manjunath (Frontiers Artif. Intell., 2022)

A Tutorial Introduction to Reinforcement Learning. - Mathukumalli Vidyasagar (CoRR, 2023)

Convergence of stochastic approximation via martingale and converse Lyapunov methods. - M. Vidyasagar (Math. Control. Signals Syst., 2023)

Constant step-size stochastic approximation with delayed updates. - Aditya Mahajan; Silviu-Iulian Niculescu; Mathukumalli Vidyasagar (CDC, 2024)

A vector almost-supermartingale convergence theorem and its applications. - Aditya Mahajan; Silviu-Iulian Niculescu; Mathukumalli Vidyasagar (CDC, 2024)

Convergence Rates for Stochastic Approximation: Biased Noise with Unbounded Variance, and Applications. - Rajeeva Laxman Karandikar; Mathukumalli Vidyasagar (J. Optim. Theory Appl., 2024)

Revisiting Stochastic Approximation and Stochastic Gradient Descent. - Rajeeva Laxman Karandikar; Bhamidi Visweswara Rao; Mathukumalli Vidyasagar (CoRR, 2025)

Convergence of Momentum-Based Optimization Algorithms with Time-Varying Parameters. - Mathukumalli Vidyasagar (CoRR, 2025)