Search Results for author:
Found 5 papers, 3 papers with code
Adaptive Crowdsourcing Via Self-Supervised Learning
Common crowdsourcing systems average estimates of a latent quantity of interest provided by many crowdworkers to produce a group estimate.
Statistically Robust, Risk-Averse Best Arm Identification in Multi-Armed Bandits
In this paper, we show that specialized algorithms that exploit such parametric information are prone to inconsistent learning performance when the parameter is misspecified.
Bandit algorithms: Letting go of logarithmic regret for statistical robustness
We study regret minimization in a stochastic multi-armed bandit setting and establish a fundamental trade-off between the regret suffered under an algorithm, and its statistical robustness.
Constrained regret minimization for multi-criterion multi-armed bandits
We consider a stochastic multi-armed bandit setting and study the problem of constrained regret minimization over a given time horizon.
Distribution oblivious, risk-aware algorithms for multi-armed bandits with unbounded rewards
We also compare the error bounds for our distribution oblivious algorithms with those corresponding to standard non-oblivious algorithms.
