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Georgia Tech
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Optimistic FTRL achieves linear convergence to Nash equilibria in harmonic games, a significant improvement over prior sublinear bounds.
SGD can learn spurious features exponentially faster than true signals, fundamentally altering our understanding of neural network training dynamics.