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Found 11 papers, 2 papers with code
Worst-Case Convergence Time of ML Algorithms via Extreme Value Theory
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.
Template-Based Piecewise Affine Regression
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.
Mathematical Models of Human Drivers Using Artificial Risk Fields
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.
Local Repair of Neural Networks Using Optimization
In this paper, we propose a framework to repair a pre-trained feed-forward neural network (NN) to satisfy a set of properties.
Static analysis of ReLU neural networks with tropical polyhedra
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.
Reasoning about Uncertainties in Discrete-Time Dynamical Systems using Polynomial Forms.
In this paper, we propose polynomial forms to represent distributions of state variables over time for discrete-time stochastic dynamical systems.
Training Neural Network Controllers Using Control Barrier Functions in the Presence of Disturbances
Control Barrier Functions (CBF) have been recently utilized in the design of provably safe feedback control laws for nonlinear systems.
A learning-based algorithm to quickly compute good primal solutions for Stochastic Integer Programs
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.
When Nash Meets Stackelberg
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
Efficient Detection and Quantification of Timing Leaks with Neural Networks
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.
Discriminating Traces with Time
What properties about the internals of a program explain the possible differences in its overall running time for different inputs?
