Showing papers from Technion and Google Research Show all papers
Paris Poster Session 2, Wed, Dec 9, 5:00 PM–7:00 PM, Paris Poster Hall · Published 2026
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Multiclass PAC learning with bandit feedback is characterized by the new bandit DS dimension via pseudo-boxes, yielding sharp sample complexity scaling with total neighbors.
Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026
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Online set learning with randomized precision or recall feedback is learnable exactly when the hypothesis class has finite VC dimension, though standard empirical risk minimization can fail and algorithms must handle feedback dependencies to achieve regret bounds.
Paris Poster Session 3, Thu, Dec 10, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026
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Strategic classification can make VC dimension 1 problems unlearnable, but geometric definability via first-order formulas over real exponentiation preserves learnability with controlled sample complexity.
Paris Poster Session 4, Thu, Dec 10, 5:30 PM–7:30 PM, Paris Poster Hall · Published 2026
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