An interventional perspective on identifiability in gaussian lti systems with independent component analysis
We investigate the relationship between system identification and intervention design in
dynamical systems. While previous research demonstrated how identifiable representation …
dynamical systems. While previous research demonstrated how identifiable representation …
Joint problems in learning multiple dynamical systems
Clustering of time series is a well-studied problem, with applications ranging from
quantitative, personalized models of metabolism obtained from metabolite concentrations to …
quantitative, personalized models of metabolism obtained from metabolite concentrations to …
Structure learning of Hamiltonians from real-time evolution
We initiate the study of Hamiltonian structure learning from real-time evolution: given the
ability to apply $ e^{-\mathrm {i} Ht} $ for an unknown local Hamiltonian $ H=\sum_ {a= 1} …
ability to apply $ e^{-\mathrm {i} Ht} $ for an unknown local Hamiltonian $ H=\sum_ {a= 1} …
Model Stealing for Any Low-Rank Language Model
Model stealing, where a learner tries to recover an unknown model via carefully chosen
queries, is a critical problem in machine learning, as it threatens the security of proprietary …
queries, is a critical problem in machine learning, as it threatens the security of proprietary …
Finite Sample Analysis of Tensor Decomposition for Learning Mixtures of Linear Systems
M Rui, M Dahleh - arxiv preprint arxiv:2412.10615, 2024 - arxiv.org
We study the problem of learning mixtures of linear dynamical systems (MLDS) from input-
output data. This mixture setting allows us to leverage observations from related dynamical …
output data. This mixture setting allows us to leverage observations from related dynamical …
PURE: Prompt Evolution with Graph ODE for Out-of-distribution Fluid Dynamics Modeling
This work studies the problem of out-of-distribution fluid dynamics modeling. Previous works
usually design effective neural operators to learn from mesh-based data structures …
usually design effective neural operators to learn from mesh-based data structures …
Learning Algorithms for Mixtures of Linear Dynamical Systems: A Practical Approach
NA Kumar - 2024 - dspace.mit.edu
In this work, we give the first implementation of an algorithm to learn a mixture of linear
dynamical systems (LDS's), and an analysis of algorithms to learn a single linear dynamical …
dynamical systems (LDS's), and an analysis of algorithms to learn a single linear dynamical …