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University of Texas at Austin, kjtian@cs.utexas.edu
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Forget single-objective optimization鈥攖his work cracks omniprediction in multiclass settings, opening the door to algorithms that are robust across diverse loss functions and comparator classes.
Sparse recovery requires quadratically more samples in adaptive settings than oblivious ones, a surprising divergence from the well-understood $\ell_2$ norm.
