A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
New algorithm identifies good arms with fewer samples when thresholds are close.
The paper proposes a novel upper confidence bound (UCB) procedure for identifying the arm with the largest mean in a multi-armed bandit game in the fixed confidence setting using a small number of total samples. The procedure cannot be improved in the sense that the number of samples required to identify the best arm i…
Algorithm optimizes two objectives in bandits: minimizing regret and identifying best arm.
Law of iterated logarithm derived from betting strategy.
Paper tackles good arm identification in stochastic bandits.
We propose a new algorithmic framework for sequential hypothesis testing with i.i.d. data, which includes A/B testing, nonparametric two-sample testing, and independence testing as special cases. It is novel in several ways: (a) it takes linear time and constant space to compute on the fly, (b) it has the same power gu…
The paper proves a regret bound for a sub-Gaussian mixture on unbounded data.