1 paper accepted to AISTATS

Our paper on convergence of gradient descent on overparametrized linear network [1] has been accepted to International Conference on Artificial Intelligence and Statistics!

[1] Z. Xu, H. Min, S. Tarmoun, E. Mallada, and R. Vidal, “Linear Convergence of Gradient Descent For Overparametrized Finite Width Two-Layer Linear Networks with General Initialization,” in International Conference on Artificial Intelligence and Statistics (AISTATS), 2023.
[Bibtex] [Download PDF]
@inproceedings{xmtmv2023aistats,
  author = {Xu, Ziqing and Min, Hancheng and Tarmoun, Salma and Mallada, Enrique and Vidal, Rene},
  booktitle = {International Conference on Artificial Intelligence and Statistics (AISTATS)},
  month = {1},
  pubstate = {accepted},
  title = {Linear Convergence of Gradient Descent For Overparametrized Finite Width Two-Layer Linear Networks with General Initialization},
  url = {https://mallada.ece.jhu.edu/pubs/2023-AISTATS-XMTMV.pdf},
  year = {2023}
}

2 papers accepted to ACC

Our paper on spectral clustering for weakly-connected network systems [1] and our paper on frequency shaping control [2] have been accepted to American Control Conference 2023!

[1] H. Min and E. Mallada, “Spectral clustering and model reduction for weakly-connected coherent network systems,” in American Control Conference (ACC), 2023, pp. 1-7.
[Bibtex] [Abstract] [Download PDF]

We propose a novel model-reduction methodology for large-scale dynamic networks with tightly-connected components. First, the coherent groups are identified by a spectral clustering algorithm on the graph Laplacian matrix that models the network feedback. Then, a reduced network is built, where each node represents the aggregate dynamics of each coherent group, and the reduced network captures the dynamic coupling between the groups. Our approach is theoretically justified under a random graph setting. Finally, numerical experiments align with and validate our theoretical findings.

@inproceedings{mm2023acc,
  abstract = {We propose a novel model-reduction methodology
for large-scale dynamic networks with tightly-connected components.
First, the coherent groups are identified by a spectral
clustering algorithm on the graph Laplacian matrix that models
the network feedback. Then, a reduced network is built, where
each node represents the aggregate dynamics of each coherent
group, and the reduced network captures the dynamic coupling
between the groups. Our approach is theoretically justified
under a random graph setting. Finally, numerical experiments
align with and validate our theoretical findings.},
  author = {Min, Hancheng and Mallada, Enrique},
  booktitle = {American Control Conference (ACC)},
  grants = {CAREER-1752362, CPS-2136324},
  month = {1},
  pages = {1-7},
  pubstate = {accepted, submitted Sep 2022},
  title = {Spectral clustering and model reduction for weakly-connected coherent network systems},
  url = {https://mallada.ece.jhu.edu/pubs/2023-ACC-MM.pdf},
  year = {2023}
}
[2] B. K. Poolla, Y. Lin, A. Bernstein, E. Mallada, and D. Groß, “Frequency shaping control for weakly-coupled grid-forming IBRs,” in American Control Conference (ACC), 2023.
[Bibtex] [Abstract] [Download PDF]

We introduce a novel framework to approximate the aggregate frequency dynamics of coherent synchronous generators. By leveraging recent results on dynamics concentration of tightly connected networks, we develop a hierarchy of reduced order models –based on frequency weighted balanced truncation– that accurately approximate the aggregate system response. Our results outperform existing aggregation techniques and can be shown to monotonically improve the approximation as the hierarchy order increases.

@inproceedings{plbmg2023acc,
  abstract = {We introduce a novel framework to approximate the aggregate frequency dynamics of coherent synchronous generators. By leveraging recent results on dynamics concentration of tightly connected networks, we develop a hierarchy of reduced order models --based on frequency weighted balanced truncation-- that accurately approximate the aggregate system response. Our results outperform existing aggregation techniques and can be shown to monotonically improve the approximation as the hierarchy order increases.},
  author = {Poolla, Bala Kameshwar and Lin, Yashen and Bernstein, Andrey and Mallada, Enrique and Groß, Dominic},
  booktitle = {American Control Conference (ACC)},
  grants = {CAREER-1752362, CPS-2136324},
  month = {1},
  record = {accepted, submitted Sep 2022.},
  title = {Frequency shaping control for weakly-coupled grid-forming IBRs},
  url = {https://mallada.ece.jhu.edu/pubs/2023-ACC-PLBMG.pdf},
  year = {2023}
}

1 paper accepted to TAC

Our paper on safe reinforcement learning with almost sure constraints [1] has been accepted to Transactions on Automatic Control Special Issue on Learning for Control!

[1] A. Castellano, H. Min, J. Bazerque, and E. Mallada, “Learning to Act Safely with Limited Exposure and Almost Sure Certainty,” IEEE Transactions on Automatic Control, pp. 1-16, 2023.
[Bibtex] [Abstract] [Download PDF]

This paper aims to put forward the concept that learning to take safe actions in unknown environments, even with probability one guarantees, can be achieved without the need for an unbounded number of exploratory trials, provided that one is willing to navigate trade-offs between optimality, level of exposure to unsafe events, and the maximum detection time of unsafe actions. We illustrate this concept in two complementary settings. We first focus on the canonical multi-armed bandit problem and seek to study the intrinsic trade-offs of learning safety in the presence of uncertainty. Under mild assumptions on sufficient exploration, we provide an algorithm that provably detects all unsafe machines in an (expected) finite number of rounds. The analysis also unveils a trade-off between the number of rounds needed to secure the environment and the probability of discarding safe machines. We then consider the problem of finding optimal policies for a Markov Decision Process (MDP) with almost sure constraints. We show that the (action) value function satisfies a barrier-based decomposition which allows for the identification of feasible policies independently of the reward process. Using this decomposition, we develop a Barrier-learning algorithm, that identifies such unsafe state-action pairs in a finite expected number of steps. Our analysis further highlights a trade-off between the time lag for the underlying MDP necessary to detect unsafe actions, and the level of exposure to unsafe events. Simulations corroborate our theoretical findings, further illustrating the aforementioned trade-offs, and suggesting that safety constraints can further speed up the learning process.

@article{cmbm2023tac,
  abstract = {This paper aims to put forward the concept that learning to take safe actions in unknown environments, even with probability one guarantees, can be achieved without the need for an unbounded number of exploratory trials, provided that one is willing to navigate trade-offs between optimality, level of exposure to unsafe events, and the maximum detection time of unsafe actions. We illustrate this concept in two complementary settings. We first focus on the canonical multi-armed bandit problem and seek to study the intrinsic trade-offs of learning safety in the presence of uncertainty.  Under mild assumptions on sufficient exploration, we provide an algorithm that provably detects all unsafe machines in an (expected) finite number of rounds. The analysis also unveils a trade-off between the number of rounds needed to secure the environment and the probability of discarding safe machines.  We then consider the problem of finding optimal policies for a Markov Decision Process (MDP) with almost sure constraints. 
We show that the (action) value function satisfies a barrier-based decomposition which allows for the identification of feasible policies independently of the reward process. Using this decomposition, we develop a Barrier-learning algorithm, that identifies such unsafe state-action pairs in a finite expected number of steps. Our analysis further highlights a trade-off between the time lag for the underlying MDP necessary to detect unsafe actions, and the level of exposure to unsafe events. Simulations corroborate our theoretical findings, further illustrating the aforementioned trade-offs, and suggesting that safety constraints can further speed up the learning process.},
  author = {Castellano, Agustin and Min, Hancheng and Bazerque, Juan and Mallada, Enrique},
  grants = {CAREER-1752362, TRIPODS-1934979, CPS-2136324},
  journal = {IEEE Transactions on Automatic Control},
  month = {1},
  pages = {1-16},
  pubstate = {accepted, revised Oct 2022, submitted May 2021},
  title = {Learning to Act Safely with Limited Exposure and Almost Sure Certainty},
  url = {https://mallada.ece.jhu.edu/pubs/2023-TAC-CMBM.pdf},
  year = {2023}
}

1 paper accepted to LCSS

Our paper on frequency shaping control for grid-forming IBRs [1] has been accepted to Control Systems Letters!

[1] [doi] B. K. Poolla, Y. Lin, A. Bernstein, E. Mallada, and D. Groß, “Frequency shaping control for weakly-coupled grid-forming IBRs,” IEEE Control Systems Letters (L-CSS), pp. 937-942, 2022.
[Bibtex] [Abstract] [Download PDF]

We introduce a novel framework to approximate the aggregate frequency dynamics of coherent synchronous generators. By leveraging recent results on dynamics concentration of tightly connected networks, we develop a hierarchy of reduced order models –based on frequency weighted balanced truncation– that accurately approximate the aggregate system response. Our results outperform existing aggregation techniques and can be shown to monotonically improve the approximation as the hierarchy order increases.

@article{plbmg2023lcss,
  abstract = {We introduce a novel framework to approximate the aggregate frequency dynamics of coherent synchronous generators. By leveraging recent results on dynamics concentration of tightly connected networks, we develop a hierarchy of reduced order models --based on frequency weighted balanced truncation-- that accurately approximate the aggregate system response. Our results outperform existing aggregation techniques and can be shown to monotonically improve the approximation as the hierarchy order increases.},
  author = {Poolla, Bala Kameshwar and Lin, Yashen and Bernstein, Andrey and Mallada, Enrique and Groß, Dominic},
  doi = {10.1109/LCSYS.2022.3228855},
  grants = {CAREER-1752362, CPS-2136324},
  journal = {IEEE Control Systems Letters (L-CSS)},
  month = {12},
  pages = {937-942},
  record = {accepted Nov 2022, submitted Sep 2022.},
  title = {Frequency shaping control for weakly-coupled grid-forming IBRs},
  url = {https://mallada.ece.jhu.edu/pubs/2022-LCSS-PLBMG.pdf},
  year = {2022}
}

1 paper accepted to ACC

Our paper on closed-form Minkowski sum approximations [1] has been accepted to the American Control Conference!

[1] J. Guthrie, M. Kobilarov, and E. Mallada, “Closed-Form Minkowski Sum Approximations for Efficient Optimization-Based Collision Avoidance,” in American Control Conference (ACC), 2022, pp. 1-7.
[Bibtex] [Abstract] [Download PDF]

Motion planning methods for autonomous systems based on nonlinear programming offer great flexibility in incorporating various dynamics, objectives, and constraints. One limitation of such tools is the difficulty of efficiently representing obstacle avoidance conditions for non-trivial shapes. For example, it is possible to define collision avoidance constraints suitable for nonlinear programming solvers in the canonical setting of a circular robot navigating around $M$ convex polytopes over $N$ time steps. However, it requires introducing $(2+L)MN$ additional constraints and $LMN$ additional variables, with $L$ being the number of halfplanes per polytope, leading to larger nonlinear programs with slower and less reliable solving time. In this paper, we overcome this issue by building closed-form representations of the collision avoidance conditions by outer-approximating the Minkowski sum conditions for collision. Our solution requires only $MN$ constraints (and no additional variables), leading to a smaller nonlinear program. On motion planning problems for an autonomous car and quadcopter in cluttered environments, we achieve speedups of 4.0x and 10x respectively with significantly less variance in solve times and negligible impact on performance arising from the use of outer approximations.

@inproceedings{gkm2022acc,
  abstract = {Motion planning methods for autonomous systems based on nonlinear programming offer great flexibility in incorporating various dynamics, objectives, and constraints. One limitation of such tools is the difficulty of efficiently representing obstacle avoidance conditions for non-trivial shapes. For example, it is possible to define collision avoidance constraints suitable for nonlinear programming solvers in the canonical setting of a circular robot navigating around $M$ convex polytopes over $N$ time steps. However, it requires introducing $(2+L)MN$ additional constraints and $LMN$ additional variables, with $L$ being the number of halfplanes per polytope, leading to larger nonlinear programs with slower and less reliable solving time. In this paper, we overcome this issue by building closed-form representations of the collision avoidance conditions by outer-approximating the Minkowski sum conditions for collision. Our solution requires only $MN$ constraints (and no additional variables), leading to a smaller nonlinear program. On motion planning problems for an autonomous car and quadcopter in cluttered environments, we achieve speedups of 4.0x and 10x respectively with significantly less variance in solve times and negligible impact on performance arising from the use of outer approximations.},
  author = {Guthrie, James and Kobilarov, Marin and Mallada, Enrique},
  booktitle = {American Control Conference (ACC)},
  grants = {CAREER-1752362, CPS-2136324, TRIPODS-1934979},
  month = {6},
  pages = {1-7},
  pubstate = {presented},
  record = {accepted Feb. 2022, submitted Oct. 2021},
  title = {Closed-Form Minkowski Sum Approximations for Efficient Optimization-Based Collision Avoidance},
  url = {https://mallada.ece.jhu.edu/pubs/2022-ACC-GKM.pdf},
  year = {2022}
}

1 paper accepted to PSCC

Our paper on market mechanism for truthful bidding with energy storage [1] have been accepted to The Power Systems Computation Conference!

[1] R. K. Bansal, P. You, D. F. Gayme, and E. Mallada, “A Market Mechanism for Truthful Bidding with Energy Storage,” in Power Systems Computation Conference (PSCC), 2022, pp. 1-9.
[Bibtex] [Abstract] [Download PDF]

This paper proposes a market mechanism for multi-interval electricity markets with generator and storage participants. Drawing ideas from supply function bidding, we introduce a novel bid structure for storage participation that allows storage units to communicate their cost to the market using energy cycling functions that map prices to cycle depths. The resulting market-clearing process–implemented via convex programming–yields corresponding schedules and payments based on traditional energy prices for power supply and per-cycle prices for storage utilization. We illustrate the benefits of our solution by comparing the competitive equilibrium of the resulting mechanism to that of an alternative solution that uses prosumer-based bids. Our solution shows several advantages over the prosumer-based approach. It does not require a priori price estimation. It also incentivizes participants to reveal their truthful costs, thus leading to an efficient, competitive equilibrium. Numerical experiments using New York Independent System Operator (NYISO) data validate our findings.

@inproceedings{bygm2022pscc,
  abstract = {This paper proposes a market mechanism for multi-interval electricity markets with generator and storage participants. Drawing ideas from supply function bidding, we introduce a novel bid structure for storage participation that allows storage units to communicate their cost to the market using energy cycling functions that map prices to cycle depths. The resulting market-clearing process--implemented via convex programming--yields corresponding schedules and payments based on traditional energy prices for power supply and per-cycle prices for storage utilization. We illustrate the benefits of our solution by comparing the competitive equilibrium of the resulting mechanism to that of an alternative solution that uses prosumer-based bids. Our solution shows several advantages over the prosumer-based approach. It does not require a priori price estimation. It also incentivizes participants to reveal their truthful costs, thus leading to an efficient, competitive equilibrium. Numerical experiments using New York Independent System Operator (NYISO) data validate our findings.},
  author = {Bansal, Rajni Kant and You, Pengcheng and Gayme, Dennice F. and Mallada, Enrique},
  booktitle = {Power Systems Computation Conference (PSCC)},
  grants = {CAREER-1752362, TRIPODS-1934979, CPS-2136324},
  month = {7},
  pages = {1-9},
  record = {accepted Feb. 2022, submitted Oct. 2021},
  title = {A Market Mechanism for Truthful Bidding with Energy Storage},
  url = {https://mallada.ece.jhu.edu/pubs/2022-PSCC-BYGM.pdf},
  year = {2022}
}

1 paper accepted to SICON

Our paper on delays and saturation in contact tracing for disease control [1] have been accepted to SIAM Journal of Control and Optimization!

[1] Unknown bibtex entry with key [pfpypm2021sicon]
[Bibtex]

Rajni passes his GBO

Rajni Kant Bansal, ME Ph.D. student from our lab, passed his Graduate Board Oral Examination! Congratulations!