6 citations · 8 across the 14 of their papers we have counts for
6 papers · 1 filter
Relational Programming with Foundation Models
Ziyang Li, Jiani Huang, Jason Liu +6
Foundation models have vast potential to enable diverse AI applications. The powerful yet incomplete nature of these models has spurred a wide range of mechanisms to augment them w…
Learning Variational Inequalities from Data: Fast Generalization Rates under Strong Monotonicity
Eric Zhao, Tatjana Chavdarova, Michael Jordan
Variational inequalities (VIs) are a broad class of optimization problems encompassing machine learning problems ranging from standard convex minimization to more complex scenarios…
Learning With Multi-Group Guarantees For Clusterable Subpopulations
Jessica Dai, Nika Haghtalab, Eric Zhao
A canonical desideratum for prediction problems is that performance guarantees should hold not just on average over the population, but also for meaningful subpopulations within th…
Algorithmic Content Selection and the Impact of User Disengagement
Emilio Calvano, Nika Haghtalab, Ellen Vitercik +1
Digital services face a fundamental trade-off in content selection: they must balance the immediate revenue gained from high-reward content against the long-term benefits of mainta…
Truthfulness of Calibration Measures
Nika Haghtalab, Mingda Qiao, Kunhe Yang +1
We initiate the study of the truthfulness of calibration measures in sequential prediction. A calibration measure is said to be truthful if the forecaster (approximately) minimizes…
Stacking as Accelerated Gradient Descent
Naman Agarwal, Pranjal Awasthi, Satyen Kale +1
Stacking, a heuristic technique for training deep residual networks by progressively increasing the number of layers and initializing new layers by copying parameters from older la…