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Indian Institute of Technology Bombay
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Optimizing arm selection in multi-agent bandit settings can eliminate dependence on the number of arms, achieving optimal regret even in adversarial environments.
Gradient EM provably converges to optimal solutions for fitting mixtures of *any* parametric function with strongly convex losses, extending beyond just linear regression in the agnostic setting.
