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When can we learn from biased samples? We study regression when outcomes are observed only after passing through selection filters that depend on both covariates and outcomes themselves, a ubiquitous challenge spanning clinical trials with patient dropout, labor markets with self-selection, and auctions with strategic...
An algorithm is introduced that uses the base model's next-token entropy as a proxy to identify key decision points and resample from those positions and it is proved that the method's mixing time scales with the number of decisions in a trace rather than with the number of tokens, which can be much larger.
This work achieves the first local convergence algorithm for self-selection by providing the first local convergence algorithm for self-selection, thus resolving one of the main open questions of Cherapanamjeri, Daskalakis, Ilyas, and Zampetakis.
This model shows that adding correctly labeled examples can make learning harder by a logarithmic factor, even for classes that admit finite mistake bounds in online learning.
This work revisits and settles the question: a concept class is properly learnable from positive-only samples if and only if it has finite VC dimension and satisfies a new combinatorial condition, which is called uniform exterior separability.
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