Lorne Applebaum — accepted AI-conference papers

2026

Optimal Learning from Label Proportions with General Loss Functions

Lorne Applebaum, Travis Dick, Claudio Gentile, Haim Kaplan, Tomer Koren

ICML 2026poster

Motivated by problems in online advertising, we address the task of Learning from Label Proportions (LLP). We introduce a novel and versatile low-variance debiasing methodology to learn from aggregate label information, significantly advancing the state of the art in LLP. Our debiasing approach exhi…

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2024

Generalization and Learnability in Multiple Instance Regression

Kushal Chauhan, Rishi Saket, Lorne Applebaum, Ashwinkumar Badanidiyuru, Chandan Giri, Aravindan Raghuveer

UAI 2024poster

Multiple instance regression (MIR) was introduced by Ray and Page (2001) as an analogue of multiple instance learning (MIL) in which we are given bags of feature-vectors (instances) and for each bag there is a bag-label which matches the label of one (unknown) primary instance from that bag. The goa…

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