PARWiS: Winner determination under shoestring budgets using active pairwise comparisons
Determining a winner among a set of items using active pairwise comparisons under a limited budget is a challenging problem in preference-based learning. The goal of this study is to implement and evaluate the PARWiS algorithm, which shows spectral ranking and disruptive pair selection to identify the best item under shoestring budgets. This work have extended the PARWiS with a contextual variant (Contextual PARWiS) and a reinforcement learning-based variant (RL PARWiS), comparing them against baselines, including Double Thompson Sampling and a random selection strategy. This evaluation spans synthetic and real-world datasets (Jester and MovieLens), using budgets of 40, 60, and 80 compari...
| Authors | Shailendra Bhandari |
|---|---|
| Primary category | cs.LG |
| Published | 1 March 2026 |
| Last updated at source | 1 March 2026 |
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