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Found 12 papers, 3 papers with code
Learning-Based Efficient Approximation of Data-enabled Predictive Control
However, the size of the optimization problem in DeePC grows linearly with respect to the data size, which prohibits its application due to high computational costs.
FlashSpeech: Efficient Zero-Shot Speech Synthesis
The generation processes of FlashSpeech can be achieved efficiently with one or two sampling steps while maintaining high audio quality and high similarity to the audio prompt for zero-shot speech generation.
CoMoSVC: Consistency Model-based Singing Voice Conversion
The diffusion-based Singing Voice Conversion (SVC) methods have achieved remarkable performances, producing natural audios with high similarity to the target timbre.
MPC-Inspired Reinforcement Learning for Verifiable Model-Free Control
In this paper, we introduce a new class of parameterized controllers, drawing inspiration from Model Predictive Control (MPC).
Boosting Spectral Clustering on Incomplete Data via Kernel Correction and Affinity Learning
Secondly, we develop a series of affinity learning methods that equip the selfexpressive framework with ℓp-norm to construct an intrinsic affinity matrix with an adaptive extension.
Generalized Activation via Multivariate Projection
Activation functions are essential to introduce nonlinearity into neural networks, with the Rectified Linear Unit (ReLU) often favored for its simplicity and effectiveness.
Almost Surely $\sqrt{T}$ Regret Bound for Adaptive LQR
The Linear-Quadratic Regulation (LQR) problem with unknown system parameters has been widely studied, but it has remained unclear whether $\tilde{ \mathcal{O}}(\sqrt{T})$ regret, which is the best known dependence on time, can be achieved almost surely.
Safe and Efficient Switching Controller Design for Partially Observed Linear-Gaussian Systems
Switching control strategies that unite a potentially high-performance but uncertified controller and a stabilizing albeit conservative controller are shown to be able to balance safety with efficiency, but have been less studied under partial observation of state.
Safe and Efficient Switching Mechanism Design for Uncertified Linear Controller
We show that the switching strategy is both safe and efficient, in the sense that: 1) the linear-quadratic cost of the system is always bounded even if original uncertified controller is destabilizing; 2) in case the uncertified controller is stabilizing, the performance loss caused by switching converges super-exponentially to $0$ for Gaussian noise, while the converging polynomially for general heavy-tailed noise.
Ensuring the Safety of Uncertified Linear State-Feedback Controllers via Switching
Sustained research efforts have been devoted to learning optimal controllers for linear stochastic dynamical systems with unknown parameters, but due to the corruption of noise, learned controllers are usually uncertified in the sense that they may destabilize the system.
Safe Linear-Quadratic Dual Control with Almost Sure Performance Guarantee
This paper considers the linear-quadratic dual control problem where the system parameters need to be identified and the control objective needs to be optimized in the meantime.
Modeling the Interaction between Agents in Cooperative Multi-Agent Reinforcement Learning
Value-based methods of multi-agent reinforcement learning (MARL), especially the value decomposition methods, have been demonstrated on a range of challenging cooperative tasks.
