Siebel School Speaker Series Master Calendar

Abstract: Score-based valuation methods, such as Shapley-style scores and leave-one-out (LOO), are widely used for credit assignment in data markets, yet theory offers limited guidance on when and why they succeed. In this talk, we discuss recent work that studies these methods through the lens of best data subset selection for learning tasks. We show that even for monotone submodular valuation functions, LOO and Shapley-style scores cannot achieve a constant-factor approximation due to duplicate archetypes and collapsed pointwise credit. More broadly, boundary effects in canonical learning problems can induce supermodular spikes, ruling out constant-factor guarantees for any valuation method, including adaptive methods such as greedy selection. We identify two conditions that avert these failures: bounded curvature, which controls redundancy and restores guarantees for score-based methods, and coverage, which yields approximate submodularity over a sufficiently rich core for uniformly stable learning algorithms. We also examine the role of monotonicity and show separations between adaptive and non-adaptive methods for non-monotone valuations. Our results justify common practices such as deduplication while highlighting the importance of ensuring coverage before applying score-based selection.

The talk is based on work with Sai Praneeth Karimireddy, Raul Castro Fernandez, and Manolis Zampetakis.