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This paper characterizes when regression is possible under sample selection biases that depend on both covariates and outcomes, showing the regression function can be identified even when the selection filter is not. It provides finite-sample estimation guarantees, oracle-efficient algorithms, and the first general-purpose estimation method for this class of selection problems.
The CLEER dashboard estimates the energy consumption and CO2 emissions of closed AI models using a new approach that combines research, empirical testing, and production data. It provides comparisons across models in chat and agentic sessions.
This paper proposes a reformulation to apply tabular foundation models (TFMs) to discrete choice estimation, addressing the structural gap of row-independent assumptions. The best reformulation outperforms hierarchical Bayesian estimation by 8% in holdout log-likelihood and 3.6% in hit rate while running 16 times faster.
A developer created an offline, single-file GPU build picker that estimates which local AI models a system can run and at what token generation speed.
This paper revises the estimated proportion of newly written code that is generated or reviewed by AI, analyzing its impact on software development.
Rudus is an AI-powered takeoff and estimation platform for concrete subcontractors, automating the measurement and quantification of materials from plan sheets to speed up bidding.