no code implementations • 10 Apr 2024 • Saeid Tizpaz-Niari, Sriram Sankaranarayanan
On the set of larger machine learning training algorithms and deep neural network inference, we show the feasibility and usefulness of EVT models to accurately predict WCCTs, their expected return periods, and their likelihood.
no code implementations • 15 May 2023 • Guillaume O. Berger, Sriram Sankaranarayanan
Next, we present a top-down algorithm that considers subsets of the overall data set in a systematic manner, trying to fit an affine function for each subset using linear regression.
no code implementations • 24 May 2022 • Emily Jensen, Maya Luster, Hansol Yoon, Brandon Pitts, Sriram Sankaranarayanan
In this paper, we use the concept of artificial risk fields to predict how human operators control a vehicle in response to upcoming road situations.
no code implementations • 28 Sep 2021 • Keyvan Majd, Siyu Zhou, Heni Ben Amor, Georgios Fainekos, Sriram Sankaranarayanan
In this paper, we propose a framework to repair a pre-trained feed-forward neural network (NN) to satisfy a set of properties.
no code implementations • 30 Jul 2021 • Eric Goubault, Sébastien Palumby, Sylvie Putot, Louis Rustenholz, Sriram Sankaranarayanan
This paper studies the problem of range analysis for feedforward neural networks, which is a basic primitive for applications such as robustness of neural networks, compliance to specifications and reachability analysis of neural-network feedback systems.
no code implementations • NeurIPS 2020 • Sriram Sankaranarayanan, Yi Chou, Eric Goubault, Sylvie Putot
In this paper, we propose polynomial forms to represent distributions of state variables over time for discrete-time stochastic dynamical systems.
no code implementations • 18 Jan 2020 • Shakiba Yaghoubi, Georgios Fainekos, Sriram Sankaranarayanan
Control Barrier Functions (CBF) have been recently utilized in the design of provably safe feedback control laws for nonlinear systems.
1 code implementation • 17 Dec 2019 • Yoshua Bengio, Emma Frejinger, Andrea Lodi, Rahul Patel, Sriram Sankaranarayanan
We propose a novel approach using supervised learning to obtain near-optimal primal solutions for two-stage stochastic integer programming (2SIP) problems with constraints in the first and second stages.
1 code implementation • 14 Oct 2019 • Margarida Carvalho, Gabriele Dragotto, Felipe Feijoo, Andrea Lodi, Sriram Sankaranarayanan
This article introduces a class of $Nash$ games among $Stackelberg$ players ($NASPs$), namely, a class of simultaneous non-cooperative games where the players solve sequential Stackelberg games.
Computer Science and Game Theory Optimization and Control
no code implementations • 23 Jul 2019 • Saeid Tizpaz-Niari, Pavol Cerny, Sriram Sankaranarayanan, Ashutosh Trivedi
As demonstrated in our experiments, both of these tasks are feasible in practice --- making the approach a significant improvement over the state-of-the-art side channel detectors and quantifiers.
no code implementations • 23 Feb 2017 • Saeid Tizpaz-Niari, Pavol Cerny, Bor-Yuh Evan Chang, Sriram Sankaranarayanan, Ashutosh Trivedi
What properties about the internals of a program explain the possible differences in its overall running time for different inputs?