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On Mixtures of Markov Chains
We study the problem of reconstructing a mixture of Markov chains from the trajectories generated by random walks through the state space. Under mild nondegeneracy conditions, we show that we can uniquely reconstruct the underlying chains by only considering trajectories of length three, which represent triples of states. Our algorithm is spectral in nature, and is easy to implement.
Provable and Efficient Dataset Distillation for Kernel Ridge Regression
Our code is available at GitHub.
We thank the reviewers for their careful reading of our work and for their helpful comments
We thank the reviewers for their careful reading of our work and for their helpful comments. We will also clarify that the text in sections 2.1 and 2.2 In terms of experimental predictions, our work predicts the synaptic weights in the SFA circuit. One mechanism for implementing a quadratic expansion are so-called "Sigma-Pi units" (Rumelhart, Hinton and (Mel and Koch, 1990). In this case, the derivation proceeds exactly as laid out in the paper. Thank you for pointing out the typos.
Provable and Efficient Dataset Distillation for Kernel Ridge Regression
Our code is available at GitHub.
