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ORCIDAndrew G. Lamperski
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- affiliation: University of Minnesota, Minneapolis, Department of Electrical and Computer Engineering
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2020 – today
- 2024
[c39]Logan Anderson, Ryan James Caverly, Andrew G. Lamperski:
Statistical Bounds on Identified QSR Dissipative Properties from Input-Output Data. ACC 2024: 1380-1385- [i21]Andrew G. Lamperski, Tyler Lekang:
Approximation with Random Shallow ReLU Networks with Applications to Model Reference Adaptive Control. CoRR abs/2403.17142 (2024) - 2023
- [j16]Debojyoti Biswas
, Andrew G. Lamperski, Yu Yang, Kathleen Hoffman, John Guckenheimer, Eric S. Fortune, Noah J. Cowan:
Mode switching in organisms for solving explore-versus-exploit problems. Nat. Mac. Intell. 5(11): 1285-1296 (2023) - [j15]Venkat Ram Subramanian, Deepjyoti Deka, Saurav Talukdar, Andrew G. Lamperski, Murti V. Salapaka:
Topology Learning in Radial Dynamical Systems With Unreliable Data. IEEE Trans. Control. Netw. Syst. 10(4): 2010-2021 (2023) - [j14]Andrew G. Lamperski:
Nonasymptotic Pointwise and Worst-Case Bounds for Classical Spectrum Estimators. IEEE Trans. Signal Process. 71: 4273-4287 (2023) - [c38]Logan Anderson, Ryan James Caverly, Andrew G. Lamperski:
Gain-Scheduled QSR-Dissipative Systems: An Input-Output Approach. ACC 2023: 2417-2423 - [i20]Andrew G. Lamperski:
Non-Asymptotic Pointwise and Worst-Case Bounds for Classical Spectrum Estimators. CoRR abs/2303.11908 (2023) - [i19]Tyler Lekang, Andrew G. Lamperski:
Function Approximation with Randomly Initialized Neural Networks for Approximate Model Reference Adaptive Control. CoRR abs/2303.16251 (2023) - 2022
- [j13]Bolei Di, Andrew G. Lamperski:
Newton's Method, Bellman Recursion and Differential Dynamic Programming for Unconstrained Nonlinear Dynamic Games. Dyn. Games Appl. 12(2): 394-442 (2022) - [j12]Venkat Ram Subramanian, Andrew G. Lamperski, Murti V. Salapaka:
Effects of Data Corruption on Network Identification Using Directed Information. IEEE Trans. Autom. Control. 67(6): 2771-2783 (2022) - [c37]Tyler Lekang, Andrew G. Lamperski:
Sufficient Conditions for Persistency of Excitation with Step and ReLU Activation Functions. CDC 2022: 2025-2030 - [c36]Andrew G. Lamperski:
Neural Network Independence Properties with Applications to Adaptive Control. CDC 2022: 3365-3370 - [c35]Yuping Zheng, Andrew G. Lamperski:
Constrained Langevin Algorithms with L-mixing External Random Variables. NeurIPS 2022 - [i18]Yuping Zheng, Andrew G. Lamperski:
Constrained Langevin Algorithms with L-mixing External Random Variables. CoRR abs/2205.14192 (2022) - 2021
- [j11]Venkat Ram Subramanian, Andrew G. Lamperski, Murti V. Salapaka:
Network Structure Identification From Corrupt Data Streams. IEEE Trans. Autom. Control. 66(11): 5314-5325 (2021) - [c34]Bolei Di, Andrew G. Lamperski:
Confidence Bounds on Identification of Linear Systems with Multiplicative Noise. ACC 2021: 2212-2217 - [c33]Tyler Lekang, Andrew G. Lamperski:
Wasserstein Contraction Bounds on Closed Convex Domains with Applications to Stochastic Adaptive Control. CDC 2021: 366-371 - [c32]Andrew G. Lamperski:
Projected Stochastic Gradient Langevin Algorithms for Constrained Sampling and Non-Convex Learning. COLT 2021: 2891-2937 - 2020
- [j10]Khem Raj Ghusinga, Andrew G. Lamperski, Abhyudai Singh:
Moment analysis of stochastic hybrid systems using semidefinite programming. Autom. 112 (2020) - [c31]Jianjun Yuan, Andrew G. Lamperski:
Trading-Off Static and Dynamic Regret in Online Least-Squares and Beyond. AAAI 2020: 6712-6719 - [c30]Chaitanya Awasthi, Andrew G. Lamperski:
Inverse Differential Games With Mixed Inequality Constraints. ACC 2020: 2182-2187 - [c29]Bolei Di, Andrew G. Lamperski:
Local First-Order Algorithms for Constrained Nonlinear Dynamic Games. ACC 2020: 5358-5363 - [c28]Andrew G. Lamperski:
Computing Stabilizing Linear Controllers via Policy Iteration. CDC 2020: 1902-1907 - [c27]Bruce Lee, Andrew G. Lamperski:
Non-asymptotic Closed-Loop System Identification using Autoregressive Processes and Hankel Model Reduction. CDC 2020: 3419-3424 - [i17]Bolei Di, Andrew G. Lamperski:
First-Order Algorithms for Constrained Nonlinear Dynamic Games. CoRR abs/2001.01826 (2020) - [i16]Venkat Ram Subramanian, Andrew G. Lamperski, Murti V. Salapaka:
Causal Structure Identification from Corrupt Data-Streams. CoRR abs/2006.04683 (2020) - [i15]Venkat Ram Subramanian, Andrew G. Lamperski, Murti V. Salapaka:
Network Structure Identification from Corrupt Data Streams. CoRR abs/2006.04690 (2020) - [i14]Venkat Ram Subramanian, Deepjyoti Deka, Saurav Talukdar, Andy Lamperski, Murti V. Salapaka:
Topology Learning in Radial Dynamical Systems with Unreliable Data. CoRR abs/2009.13458 (2020) - [i13]Andrew G. Lamperski:
Projected Stochastic Gradient Langevin Algorithms for Constrained Sampling and Non-Convex Learning. CoRR abs/2012.12137 (2020)
2010 – 2019
- 2019
- [j9]Andrew G. Lamperski, Khem Raj Ghusinga, Abhyudai Singh:
Analysis and Control of Stochastic Systems Using Semidefinite Programming Over Moments. IEEE Trans. Autom. Control. 64(4): 1726-1731 (2019) - [c26]Bolei Di, Andrew G. Lamperski:
Newton's Method and Differential Dynamic Programming for Unconstrained Nonlinear Dynamic Games. CDC 2019: 4073-4078 - [c25]Venkat Ram Subramanian, Andrew G. Lamperski, Murti V. Salapaka:
Corruption Detection in Networks of Bi-directional Dynamical Systems. CDC 2019: 4545-4550 - [c24]Jianjun Yuan, Andrew G. Lamperski:
Online Adaptive Principal Component Analysis and Its extensions. ICML 2019: 7213-7221 - [i12]Jianjun Yuan, Andrew G. Lamperski:
Online Adaptive Principal Component Analysis and Its extensions. CoRR abs/1901.07687 (2019) - [i11]Tyler Lekang, Andrew G. Lamperski:
Simple Algorithms for Dueling Bandits. CoRR abs/1906.07611 (2019) - [i10]Bolei Di, Andrew G. Lamperski:
Newton's Method and Differential Dynamic Programming for Unconstrained Nonlinear Dynamic Games. CoRR abs/1906.09097 (2019) - [i9]Bruce Lee, Andrew G. Lamperski:
Non-asymptotic Closed-Loop System Identification using Autoregressive Processes and Hankel Model Reduction. CoRR abs/1909.02192 (2019) - [i8]Jianjun Yuan, Andrew G. Lamperski:
Trading-Off Static and Dynamic Regret in Online Least-Squares and Beyond. CoRR abs/1909.03118 (2019) - 2018
- [c23]Jianjun Yuan, Andrew G. Lamperski:
A Random Algorithm for Semidefinite Programming Problems. ACC 2018: 1382-1387 - [c22]Venkat Ram Subramanian, Andrew G. Lamperski, Murti V. Salapaka:
Inferring Directed Graphs for Networks from Corrupt Data-Streams. CDC 2018: 4493-4498 - [c21]Khem Raj Ghusinga, Andrew G. Lamperski, Abhyudai Singh:
Estimating stationary characteristic functions of stochastic systems via semidefinite programming. ECC 2018: 2720-2725 - [c20]Jianjun Yuan, Andrew G. Lamperski:
Online convex optimization for cumulative constraints. NeurIPS 2018: 6140-6149 - [i7]Jianjun Yuan, Andrew G. Lamperski:
Online Convex Optimization for Cumulative Constraints. CoRR abs/1802.06472 (2018) - 2017
- [j8]Vivek Nagaraj, Andrew G. Lamperski, Theoden I. Netoff:
Seizure Control in a Computational Model Using a Reinforcement Learning Stimulation Paradigm. Int. J. Neural Syst. 27(7): 1750012:1-1750012:13 (2017) - [c19]Jianjun Yuan, Andrew G. Lamperski:
Online control basis selection by a regularized actor critic algorithm. ACC 2017: 4448-4453 - [c18]Venkat Ram Subramanian, Andrew G. Lamperski, Murti V. Salapaka:
Network topology identification from corrupt data streams. CDC 2017: 1695-1700 - [c17]Khem Raj Ghusinga, Mohammad Soltani, Andrew G. Lamperski, Sairaj V. Dhople, Abhyudai Singh:
Approximate moment dynamics for polynomial and trigonometric stochastic systems. CDC 2017: 1864-1869 - [c16]Andrew G. Lamperski, Sairaj V. Dhople:
A semidefinite programming method for moment approximation in stochastic differential algebraic systems. CDC 2017: 2455-2460 - 2016
- [j7]Andrew G. Lamperski, Noah J. Cowan:
Optimal Control With Noisy Time. IEEE Trans. Autom. Control. 61(2): 319-333 (2016) - [j6]Tichakorn Wongpiromsarn, Ufuk Topcu, Andrew G. Lamperski:
Automata Theory Meets Barrier Certificates: Temporal Logic Verification of Nonlinear Systems. IEEE Trans. Autom. Control. 61(11): 3344-3355 (2016) - [c15]Andrew G. Lamperski, Bolei Di, Tyler Lekang, Jinsun Liu, Ran Tian:
(Un)ambiguous optimal control. ACC 2016: 1111-1116 - [c14]Andrew G. Lamperski:
Natural gradients for state and output feedback control. CDC 2016: 1984-1989 - [c13]Andrew G. Lamperski, Khem Raj Ghusinga, Abhyudai Singh:
Stochastic optimal control using semidefinite programming for moment dynamics. CDC 2016: 1990-1995 - 2015
- [j5]Andrew G. Lamperski, Laurent Lessard:
Optimal decentralized state-feedback control with sparsity and delays. Autom. 58: 143-151 (2015) - [j4]Andrew G. Lamperski, John C. Doyle:
The ℋ2 Control Problem for Quadratically Invariant Systems With Delays. IEEE Trans. Autom. Control. 60(7): 1945-1950 (2015) - 2014
- [c12]Andrew G. Lamperski, Antonis Papachristodoulou:
Stability and consensus for multi-agent systems with Poisson clock noise. CDC 2014: 3023-3028 - [i6]Andrew G. Lamperski, Noah J. Cowan:
Optimal Control with Noisy Time. CoRR abs/1401.0202 (2014) - [i5]Tichakorn Wongpiromsarn, Ufuk Topcu, Andrew G. Lamperski:
Automata Theory Meets Barrier Certificates: Temporal Logic Verification of Nonlinear Systems. CoRR abs/1403.3524 (2014) - [i4]Nikolai Matni, Andrew G. Lamperski, John C. Doyle:
Optimal Two Player LQR State Feedback With Varying Delay. CoRR abs/1403.7790 (2014) - 2013
- [j3]Andrew G. Lamperski, Aaron D. Ames:
Lyapunov Theory for Zeno Stability. IEEE Trans. Autom. Control. 58(1): 100-112 (2013) - [c11]Andrew G. Lamperski, John C. Doyle:
Output feedback ℌ2 model matching for decentralized systems with delays. ACC 2013: 5778-5783 - [c10]Andrew G. Lamperski, Noah J. Cowan:
Time-changed linear quadratic regulators. ECC 2013: 198-203 - [i3]Andrew G. Lamperski, Laurent Lessard:
Optimal Decentralized State-Feedback Control with Sparsity and Delays. CoRR abs/1306.0036 (2013) - [i2]Andrew G. Lamperski, John C. Doyle:
The $H_2$ Control Problem for Decentralized Systems with Delays. CoRR abs/1312.7724 (2013) - 2012
- [j2]Tichakorn Wongpiromsarn, Sayan Mitra, Andrew G. Lamperski, Richard M. Murray:
Verification of Periodically Controlled Hybrid Systems: Application to an Autonomous Vehicle. ACM Trans. Embed. Comput. Syst. 11(S2): 53:1-53:24 (2012) - [c9]Andrew G. Lamperski, John C. Doyle:
Dynamic programming solutions for decentralized state-feedback LQG problems with communication delays. ACC 2012: 6322-6327 - [i1]Andrew G. Lamperski, John C. Doyle:
Output Feedback H_2 Model Matching for Decentralized Systems with Delays. CoRR abs/1209.3600 (2012) - 2011
- [c8]Andrew G. Lamperski, John C. Doyle:
On the structure of state-feedback LQG controllers for distributed systems with communication delays. CDC/ECC 2011: 6901-6906 - 2010
- [c7]Andrew G. Lamperski:
A neuro-inspired method for data rate limited feedback control. CDC 2010: 6463-6468
2000 – 2009
- 2009
- [c6]Andrew G. Lamperski:
Representations and algorithms for finite-state bisimulations of linear discrete-time control systems. CDC 2009: 51-56 - [c5]Tichakorn Wongpiromsarn, Sayan Mitra, Richard M. Murray, Andrew G. Lamperski:
Periodically Controlled Hybrid Systems. HSCC 2009: 396-410 - 2008
- [j1]Jusuk Lee, S. N. Sponberg, Owen Y. Loh, Andrew G. Lamperski, Robert J. Full, Noah J. Cowan:
Templates and Anchors for Antenna-Based Wall Following in Cockroaches and Robots. IEEE Trans. Robotics 24(1): 130-143 (2008) - [c4]Andrew G. Lamperski, Aaron D. Ames:
On the existence of Zeno behavior in hybrid systems with non-isolated Zeno equilibria. CDC 2008: 2776-2781 - [c3]Andrew G. Lamperski, Aaron D. Ames:
Sufficient Conditions for Zeno Behavior in Lagrangian Hybrid Systems. HSCC 2008: 622-625 - 2007
- [c2]Andrew G. Lamperski, Aaron D. Ames:
Lyapunov-Like Conditions for the Existence of Zeno Behavior in Hybrid and Lagrangian Hybrid Systems. CDC 2007: 115-120 - 2005
- [c1]Andrew G. Lamperski, Owen Y. Loh, Brett L. Kutscher, Noah J. Cowan:
Dynamical Wall Following for a Wheeled Robot Using a Passive Tactile Sensor. ICRA 2005: 3838-3843
Coauthor Index
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