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Found 11 papers, 2 papers with code
Heat Death of Generative Models in Closed-Loop Learning
The aim of this paper is to provide insights into this process (that we refer to as "generative closed-loop learning") by studying the learning dynamics of generative models that are fed back their own produced content in addition to their original training dataset.
Taming AI Bots: Controllability of Neural States in Large Language Models
We then characterize the subset of meanings that can be reached by the state of the LLMs for some input prompt, and show that a well-trained bot can reach any meaning albeit with small probability.
Learning to control from expert demonstrations
In this paper, we revisit the problem of learning a stabilizing controller from a finite number of demonstrations by an expert.
Automaton-based Implicit Controlled Invariant Set Computation for Discrete-Time Linear Systems
In this paper, we derive closed-form expressions for implicit controlled invariant sets for discrete-time controllable linear systems with measurable disturbances.
Decentralized Secure State-Tracking in Multi-Agent Systems
This motivates a 2-step solution to the decentralized secure state-tracking problem: (1) each node tracks the compressed version of all the network measurements, and (2) each node asymptotically reconstructs the state from the output of step (1).
Joint Continuous and Discrete Model Selection via Submodularity
In model selection problems for machine learning, the desire for a well-performing model with meaningful structure is typically expressed through a regularized optimization problem.
On the Computational Complexity of the Secure State-Reconstruction Problem
To better understand this, we show that when the $\mathbf{A}$ matrix of the linear system has unitary geometric multiplicity, the gap disappears, i. e., eigenvalue observability coincides with sparse observability, and there exists a polynomial time algorithm to reconstruct the state provided the state can be reconstructed.
Universal Approximation Power of Deep Residual Neural Networks via Nonlinear Control Theory
In this paper, we explain the universal approximation capabilities of deep residual neural networks through geometric nonlinear control.
Data-driven Stabilization of SISO Feedback Linearizable Systems
In this paper we propose a methodology for stabilizing single-input single-output feedback linearizable systems when no system model is known and no prior data is available to identify a model.
Cloud-based Quadratic Optimization with Partially Homomorphic Encryption
The development of large-scale distributed control systems has led to the outsourcing of costly computations to cloud-computing platforms, as well as to concerns about privacy of the collected sensitive data.
Optimization and Control Cryptography and Security Systems and Control
Robust Linear Temporal Logic
Although it is widely accepted that every system should be robust, in the sense that "small" violations of environment assumptions should lead to "small" violations of system guarantees, it is less clear how to make this intuitive notion of robustness mathematically precise.
Logic in Computer Science Systems and Control Optimization and Control 03B44 F.4.1
