3 papers
math.OC2025
Interpretable Gradient Descent for Kalman Gain
M. A. Belabbas, A. Olshevsky
We derive a decomposition for the gradient of the innovation loss with respect to the filter gain in a linear time-invariant system, decomposing as a product of an observability Gr…
cs.LG2024
Closing the gap between SVRG and TD-SVRG with Gradient Splitting
Arsenii Mustafin, Alex Olshevsky, Ioannis Ch. Paschalidis
Temporal difference (TD) learning is a policy evaluation in reinforcement learning whose performance can be enhanced by variance reduction methods. Recently, multiple works have so…
cs.MA2024
Tree Search for Simultaneous Move Games via Equilibrium Approximation
Ryan Yu, Alex Olshevsky, Peter Chin
Neural network supported tree-search has shown strong results in a variety of perfect information multi-agent tasks. However, the performance of these methods on partial informatio…