11 citations · 48 across the 34 of their papers we have counts for
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cs.LG2026
Online Algorithms via Minimax and Posterior Matching
Thomas Kesselheim, Marco Molinaro, Kalen Patton +1
Competitive analysis is central to the study of online algorithms, but upper bounds are often highly problem-specific. We develop a more unifying methodology via the minimax viewpo…
cs.LG2025
Contextual Learning for Stochastic Optimization
Anna Heuser, Thomas Kesselheim
Motivated by stochastic optimization, we introduce the problem of learning from samples of contextual value distributions. A contextual value distribution can be understood as a fa…
cs.LG2020★ 5 cited
Online Learning with Vector Costs and Bandits with Knapsacks
Thomas Kesselheim, Sahil Singla
We introduce online learning with vector costs (\OLVCp) where in each time step , we need to play an action that incurs an unknown vec…