activity
20142022
most citedIncorrect by Construction: Fine Tuning Neural Networks for Guaranteed Performance on Finite Sets of Examples

2 citations · 5 across the 4 of their papers we have counts for

collaborators

5 papers

cs.LO2022★ 1 cited

Envelopes and Waves: Safe Multivehicle Collision Avoidance for Horizontal Non-deterministic Turns

Yanni Kouskoulas, T. J. Machado, Daniel Genin +3

We present an approach to analyzing the safety of asynchronous, independent, non-deterministic, turn-to-bearing horizontal maneuvers for two vehicles. Future turn rates, final bear…

cs.LG2020★ 2 cited

Incorrect by Construction: Fine Tuning Neural Networks for Guaranteed Performance on Finite Sets of Examples

Ivan Papusha, Rosa Wu, Joshua Brulé +3

There is great interest in using formal methods to guarantee the reliability of deep neural networks. However, these techniques may also be used to implant carefully selected input…

math.OC2018★ 1 cited

Affine Multiplexing Networks: System Analysis, Learning, and Computation

Ivan Papusha, Ufuk Topcu, Steven Carr +1

We introduce a novel architecture and computational framework for formal, automated analysis of systems with a broad set of nonlinearities in the feedback loop, such as neural netw…

cs.LO2017★ 1 cited

Sequential Convex Programming for the Efficient Verification of Parametric MDPs

Murat Cubuktepe, Nils Jansen, Sebastian Junges +4

Multi-objective verification problems of parametric Markov decision processes under optimality criteria can be naturally expressed as nonlinear programs. We observe that many of th…

math.OC2014

Domain Decomposition for Stochastic Optimal Control

Matanya B. Horowitz, Ivan Papusha, Joel W. Burdick

This work proposes a method for solving linear stochastic optimal control (SOC) problems using sum of squares and semidefinite programming. Previous work had used polynomial optimi…