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Found 7 papers, 3 papers with code
Spreading Code Optimization for Low-Earth Orbit Satellites via Mixed-Integer Convex Programming
Optimizing the correlation properties of spreading codes is critical for minimizing inter-channel interference in satellite navigation systems.
Binary sequence set optimization for CDMA applications via mixed-integer quadratic programming
Finding sets of binary sequences with low auto- and cross-correlation properties is a hard combinatorial optimization problem with numerous applications, including multiple-input-multiple-output (MIMO) radar and global navigation satellite systems (GNSS).
Input-to-State Stable Neural Ordinary Differential Equations with Applications to Transient Modeling of Circuits
This paper proposes a class of neural ordinary differential equations parametrized by provably input-to-state stable continuous-time recurrent neural networks.
Combining Experimental and Observational Data for Identification and Estimation of Long-Term Causal Effects
We consider the task of identifying and estimating the causal effect of a treatment variable on a long-term outcome variable using data from an observational domain and an experimental domain.
Causal Inference with Hidden Mediators
In this paper, we extend the proximal causal inference approach to settings where identification of causal effects hinges upon a set of mediators which are not observed, yet error prone proxies of the hidden mediators are measured.
Model-Augmented Estimation of Conditional Mutual Information for Feature Selection
In the second step, CI testing is performed by applying the $k$-NN conditional mutual information estimator to the learned feature maps.
Characterizing Distribution Equivalence and Structure Learning for Cyclic and Acyclic Directed Graphs
The main approach to defining equivalence among acyclic directed causal graphical models is based on the conditional independence relationships in the distributions that the causal models can generate, in terms of the Markov equivalence.
