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20182022
most citedHow to Learn when Data Reacts to Your Model: Performative Gradient Descent

11 citations · 42 across the 26 of their papers we have counts for

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7 papers · 1 filter

math.OC2020

A Note on Optimization Formulations of Markov Decision Processes

Lexing Ying, Yuhua Zhu

This note summarizes the optimization formulations used in the study of Markov decision processes. We consider both the discounted and undiscounted processes under the standard and…

math.OC20203 cited

Borrowing From the Future: Addressing Double Sampling in Model-free Control

Yuhua Zhu, Zach Izzo, Lexing Ying

In model-free reinforcement learning, the temporal difference method and its variants become unstable when combined with nonlinear function approximations. Bellman residual minimiz…

math.OC2020

Mirror Descent Algorithms for Minimizing Interacting Free Energy

Lexing Ying

This note considers the problem of minimizing interacting free energy. Motivated by the mirror descent algorithm, for a given interacting free energy, we propose a descent dynamics…

math.OC2020

Maximizing robustness of point-set registration by leveraging non-convexity

Cindy Orozco Bohorquez, Yuehaw Khoo, Lexing Ying

Point-set registration is a classical image processing problem that looks for the optimal transformation between two sets of points. In this work, we analyze the impact of outliers…

math.OC2019

Borrowing From the Future: An Attempt to Address Double Sampling

Yuhua Zhu, Lexing Ying

For model-free reinforcement learning, one of the main difficulty of stochastic Bellman residual minimization is the double sampling problem, i.e., while only one single sample for…

math.OC2019

Semidefinite relaxation of multi-marginal optimal transport for strictly correlated electrons in second quantization

Yuehaw Khoo, Lin Lin, Michael Lindsey +1

We consider the strictly correlated electron (SCE) limit of the fermionic quantum many-body problem in the second-quantized formalism. This limit gives rise to a multi-marginal opt…