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Found 8 papers, 1 papers with code
Minimal Realization Problems for Hidden Markov Models
Consider a stationary discrete random process with alphabet size d, which is assumed to be the output process of an unknown stationary Hidden Markov Model (HMM).
Real-Time Decoding of an Integrate and Fire Encoder
Neuronal encoding models range from the detailed biophysically-based Hodgkin Huxley model, to the statistical linear time invariant model specifying firing rates in terms of the extrinsic signal.
Learning Good State and Action Representations via Tensor Decomposition
The transition kernel of a continuous-state-action Markov decision process (MDP) admits a natural tensor structure.
QuickFlex: a Fast Algorithm for Flexible Region Construction for the TSO-DSO Coordination
For unlocking such capabilities, it is essential to understand the aggregated flexibility that can be harvested from the large population of new technologies located in distribution grids.
Causal Matrix Completion
In particular, we establish entry-wise, i. e., max-norm, finite-sample consistency and asymptotic normality results for matrix completion with MNAR data.
SAMoSSA: Multivariate Singular Spectrum Analysis with Stochastic Autoregressive Noise
However, a theoretical underpinning of multi-stage learning algorithms involving both deterministic and stationary components has been absent in the literature despite its pervasiveness.
Estimation of Models with Limited Data by Leveraging Shared Structure
Modern data sets, such as those in healthcare and e-commerce, are often derived from many individuals or systems but have insufficient data from each source alone to separately estimate individual, often high-dimensional, model parameters.
Sample Efficient Reinforcement Learning with Partial Dynamics Knowledge
In the setting of finite episodic Markov decision processes with $S$ states, $A$ actions, and episode length $H$, we present an optimistic Q-learning algorithm that achieves $\tilde{\mathcal{O}}(\text{Poly}(H)\sqrt{T})$ regret under perfect knowledge of $f$, where $T$ is the total number of interactions with the system.
