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A Sinkhorn-type Algorithm for Constrained Optimal Transport
We derive the corresponding entropy regularization formulation and introduce a Sinkhorn-type algorithm for such constrained OT problems supported by theoretical guarantees.
Accelerating Sinkhorn Algorithm with Sparse Newton Iterations
To achieve possibly super-exponential convergence, we present Sinkhorn-Newton-Sparse (SNS), an extension to the Sinkhorn algorithm, by introducing early stopping for the matrix scaling steps and a second stage featuring a Newton-type subroutine.
Generative Modeling via Tree Tensor Network States
In this paper, we present a density estimation framework based on tree tensor-network states.
Endogeneity in Weakly Separable Models without Monotonicity
We identify and estimate treatment effects when potential outcomes are weakly separable with a binary endogenous treatment.
Parallel Trends and Dynamic Choices
Difference-in-differences is a common method for estimating treatment effects, and the parallel trends condition is its main identifying assumption: the trend in mean untreated outcomes is independent of the observed treatment status.
Operator Shifting for Model-based Policy Evaluation
When the error is in the residual norm, we prove that the shifting factor is always positive and upper bounded by $1+O\left(1/n\right)$, where $n$ is the number of samples used in learning each row of the transition matrix.
Model-based Reinforcement Learning
reinforcement-learning
+1
