46 citations · 115 across the 40 of their papers we have counts for
50 papers
Certified Robustness via Sparse Second-Order Cone Relaxations
Mohammadreza Maleki, Daniel Sadig, Rushendra Sidibomma +3
Certified robustness is critical for deploying neural networks in safety-sensitive settings, but computing exact certificates typically requires solving nonconvex optimization prob…
Minimally Invasive Steering of Language Models
Taha Entesari, Jingyu Zhang, Daniel Khashabi +1
Pre-logit steering adapts a frozen language model to a test-time reward by adding vectors to its final hidden states. Unregularized reward optimization can substantially alter the…
Beyond Average Safety: Chance-Constrained LLM Fine-tuning
Taha Entesari, Mahyar Fazlyab
Fine-tuning large language models on new objectives can improve helpfulness, instruction following, or domain-specific performance, but it can also induce regressions on safety-cri…
Anytime-Feasible Gradient Descent for Constrained Optimization Under Gradient Uncertainty
Sina Sharifi, Jiarui Wang, Mahyar Fazlyab
Constrained optimization is central to many engineering systems in which decisions must satisfy strict safety and operational requirements, especially in real-time settings with li…
Generalized Model Predictive Path Integral Control as Expectation--Maximization
Jiarui Wang, Sina Sharifi, Mahyar Fazlyab
Model Predictive Path Integral (MPPI) control is a powerful sampling-based method for solving stochastic optimal control problems and has enabled real-time control in complex robot…
Hierarchical End-to-End Taylor Bounds for Complete Neural Network Verification
Taha Entesari, Mahyar Fazlyab
Reachability analysis of neural networks, which seeks to compute or bound the set of outputs attainable over a given input domain, is central to certifying safety and robustness in…
Model Predictive Path Integral Control as Preconditioned Gradient Descent
Mahyar Fazlyab, Sina Sharifi, Jiarui Wang
Model Predictive Path Integral (MPPI) control is a widely used sampling-based method for trajectory optimization, yet its convergence properties remain only partially understood. T…
Anytime-Feasible First-Order Optimization via Safe Sequential QCQP
Jiarui Wang, Mahyar Fazlyab
This paper presents the Safe Sequential Quadratically Constrained Quadratic Programming (SS-QCQP) algorithm, a first-order method for smooth inequality-constrained nonconvex optimi…
Constrained Entropic Unlearning: A Primal-Dual Framework for Large Language Models
Taha Entesari, Arman Hatami, Rinat Khaziev +2
Large Language Models (LLMs) deployed in real-world settings increasingly face the need to unlearn sensitive, outdated, or proprietary information. Existing unlearning methods typi…
Sequential QCQP for Bilevel Optimization with Line Search
Sina Sharifi, Erfan Yazdandoost Hamedani, Mahyar Fazlyab
Bilevel optimization involves a hierarchical structure where one problem is nested within another, leading to complex interdependencies between levels. We propose a single-loop, tu…
Perturbed Gradient Descent via Convex Quadratic Approximation for Nonconvex Bilevel Optimization
Nazanin Abolfazli, Sina Sharifi, Mahyar Fazlyab +1
Bilevel optimization is a fundamental tool in hierarchical decision-making and has been widely applied to machine learning tasks such as hyperparameter tuning, meta-learning, and c…
Safe Physics-Informed Machine Learning for Dynamics and Control
Jan Drgona, Truong X. Nghiem, Thomas Beckers +6
This tutorial paper focuses on safe physics-informed machine learning in the context of dynamics and control, providing a comprehensive overview of how to integrate physical models…
Safe Gradient Flow for Bilevel Optimization
Sina Sharifi, Nazanin Abolfazli, Erfan Yazdandoost Hamedani +1
Bilevel optimization is a key framework in hierarchical decision-making, where one problem is embedded within the constraints of another. In this work, we propose a control-theoret…
Domain Adaptive Safety Filters via Deep Operator Learning
Lakshmideepakreddy Manda, Shaoru Chen, Mahyar Fazlyab
Learning-based approaches for constructing Control Barrier Functions (CBFs) are increasingly being explored for safety-critical control systems. However, these methods typically re…
Compositional Curvature Bounds for Deep Neural Networks
Taha Entesari, Sina Sharifi, Mahyar Fazlyab
A key challenge that threatens the widespread use of neural networks in safety-critical applications is their vulnerability to adversarial attacks. In this paper, we study the seco…
Provable Bounds on the Hessian of Neural Networks: Derivative-Preserving Reachability Analysis
Sina Sharifi, Mahyar Fazlyab
We propose a novel reachability analysis method tailored for neural networks with smooth activations. Our method relies on constructing an end-to-end first-order Taylor expansion o…
Gradient-Regularized Out-of-Distribution Detection
Sina Sharifi, Taha Entesari, Bardia Safaei +2
One of the challenges for neural networks in real-life applications is the overconfident errors these models make when the data is not from the original training distribution. Addr…
Verification-Aided Learning of Neural Network Barrier Functions with Termination Guarantees
Shaoru Chen, Lekan Molu, Mahyar Fazlyab
Barrier functions are a general framework for establishing a safety guarantee for a system. However, there is no general method for finding these functions. To address this shortco…
Actor-Critic Physics-informed Neural Lyapunov Control
Jiarui Wang, Mahyar Fazlyab
Designing control policies for stabilization tasks with provable guarantees is a long-standing problem in nonlinear control. A crucial performance metric is the size of the resulti…
Learning Performance-Oriented Control Barrier Functions Under Complex Safety Constraints and Limited Actuation
Lakshmideepakreddy Manda, Shaoru Chen, Mahyar Fazlyab
Control Barrier Functions (CBFs) provide an elegant framework for constraining nonlinear control system dynamics to remain within an invariant subset of a designated safe set. Howe…
Certified Robustness via Dynamic Margin Maximization and Improved Lipschitz Regularization
Mahyar Fazlyab, Taha Entesari, Aniket Roy +1
To improve the robustness of deep classifiers against adversarial perturbations, many approaches have been proposed, such as designing new architectures with better robustness prop…
Certified Invertibility in Neural Networks via Mixed-Integer Programming
Tianqi Cui, Thomas Bertalan, George J. Pappas +3
Neural networks are known to be vulnerable to adversarial attacks, which are small, imperceptible perturbations that can significantly alter the network's output. Conversely, there…
Automated Reachability Analysis of Neural Network-Controlled Systems via Adaptive Polytopes
Taha Entesari, Mahyar Fazlyab
Over-approximating the reachable sets of dynamical systems is a fundamental problem in safety verification and robust control synthesis. The representation of these sets is a key f…
ReachLipBnB: A branch-and-bound method for reachability analysis of neural autonomous systems using Lipschitz bounds
Taha Entesari, Sina Sharifi, Mahyar Fazlyab
We propose a novel Branch-and-Bound method for reachability analysis of neural networks in both open-loop and closed-loop settings. Our idea is to first compute accurate bounds on…