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Found 7 papers, 1 papers with code
Steering game dynamics towards desired outcomes
To tackle this challenge, in this work we introduce the SIAR-MPC method, combining the recently introduced Side Information Assisted Regression (SIAR) method for system identification with Model Predictive Control (MPC).
Discovering How Agents Learn Using Few Data
In this work, we propose a theoretical and algorithmic framework for real-time identification of the learning dynamics that govern agent behavior using a short burst of a single system trajectory.
Multiplicative Updates for Online Convex Optimization over Symmetric Cones
We study online convex optimization where the possible actions are trace-one elements in a symmetric cone, generalizing the extensively-studied experts setup and its quantum counterpart.
Multiplicative updates for symmetric-cone factorizations
Given a matrix $X\in \mathbb{R}^{m\times n}_+$ with non-negative entries, the cone factorization problem over a cone $\mathcal{K}\subseteq \mathbb{R}^k$ concerns computing $\{ a_1,\ldots, a_{m} \} \subseteq \mathcal{K}$ and $\{ b_1,\ldots, b_{n} \} \subseteq~\mathcal{K}^*$ belonging to its dual so that $X_{ij} = \langle a_i, b_j \rangle$ for all $i\in [m], j\in [n]$.
A Non-commutative Extension of Lee-Seung's Algorithm for Positive Semidefinite Factorizations
The most widely used algorithm for computing NMFs of a matrix is the Multiplicative Update algorithm developed by Lee and Seung, in which nonnegativity of the updates is preserved by scaling with positive diagonal matrices.
Convergence to Second-Order Stationarity for Non-negative Matrix Factorization: Provably and Concurrently
Non-negative matrix factorization (NMF) is a fundamental non-convex optimization problem with numerous applications in Machine Learning (music analysis, document clustering, speech-source separation etc).
Analysis of Optimization Algorithms via Sum-of-Squares
Program., 145(1):451--482, 2014], a powerful framework for determining convergence rates of first-order optimization algorithms.
