Derives first lower bound on γ_t for mean-based algorithms in unknown-horizon bandit settings, proposes two new algorithms, and shows some are also no-regret.
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Sublevel sets of invex functions are connected under mild assumptions, with the result extended to solution sets in invex-incave minimax problems and incave games.
Characterizes iterated admissibility via the 'all I know' operator, first with LPSs to address Samuelson's concern and then with approximate belief and approximate 'all I know' in standard probability structures.
citing papers explorer
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Mean-based algorithms: A lower bound and regret
Derives first lower bound on γ_t for mean-based algorithms in unknown-horizon bandit settings, proposes two new algorithms, and shows some are also no-regret.
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On the Connectedness of Sublevel Sets in Invex Optimization
Sublevel sets of invex functions are connected under mild assumptions, with the result extended to solution sets in invex-incave minimax problems and incave games.
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A Conceptually Well-Founded Characterization of Iterated Admissibility Using an "All I Know" Operator
Characterizes iterated admissibility via the 'all I know' operator, first with LPSs to address Samuelson's concern and then with approximate belief and approximate 'all I know' in standard probability structures.