18 citations · 25 across the 7 of their papers we have counts for
8 papers
Granular feedback merits sophisticated aggregation
Anmol Kagrecha, Henrik Marklund, Potsawee Manakul +2
Human feedback is increasingly used across diverse applications like training AI models, developing recommender systems, and measuring public opinion -- with granular feedback ofte…
SkillAggregation: Reference-free LLM-Dependent Aggregation
Guangzhi Sun, Anmol Kagrecha, Potsawee Manakul +2
Large Language Models (LLMs) are increasingly used to assess NLP tasks due to their ability to generate human-like judgments. Single LLMs were used initially, however, recent work…
Adaptive Crowdsourcing Via Self-Supervised Learning
Anmol Kagrecha, Henrik Marklund, Benjamin Van Roy +2
Common crowdsourcing systems average estimates of a latent quantity of interest provided by many crowdworkers to produce a group estimate. We develop a new approach -- predict-each…
Statistically Robust, Risk-Averse Best Arm Identification in Multi-Armed Bandits
Anmol Kagrecha, Jayakrishnan Nair, Krishna Jagannathan
Traditional multi-armed bandit (MAB) formulations usually make certain assumptions about the underlying arms' distributions, such as bounds on the support or their tail behaviour.…
Bandit algorithms: Letting go of logarithmic regret for statistical robustness
Kumar Ashutosh, Jayakrishnan Nair, Anmol Kagrecha +1
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 ro…
Constrained regret minimization for multi-criterion multi-armed bandits
Anmol Kagrecha, Jayakrishnan Nair, Krishna Jagannathan
We consider a stochastic multi-armed bandit setting and study the problem of constrained regret minimization over a given time horizon. Each arm is associated with an unknown, poss…
Please come back later: Benefiting from deferrals in service systems
Anmol Kagrecha, Jayakrishnan Nair
The performance evaluation of loss service systems, where customers who cannot be served upon arrival get dropped, has a long history going back to the classical Erlang B model. In…
Distribution oblivious, risk-aware algorithms for multi-armed bandits with unbounded rewards
Anmol Kagrecha, Jayakrishnan Nair, Krishna Jagannathan
Classical multi-armed bandit problems use the expected value of an arm as a metric to evaluate its goodness. However, the expected value is a risk-neutral metric. In many applicati…