Multi-source transfer learning incurs an intrinsic adaptation cost that can exceed one, with phase transitions separating regimes where bias-agnostic estimators match oracle performance from those where they cannot.
Angelopoulos, Stephen Bates, Clara Fannjiang, Michael I
7 Pith papers cite this work. Polarity classification is still indexing.
7
Pith papers citing it
citation-role summary
method 2
citation-polarity summary
verdicts
UNVERDICTED 7roles
method 2polarities
use method 2representative citing papers
NaiAD is a new dataset and framework for LLM-native advertising that uses decoupled generation and calibrated scoring to identify four semantic strategies for balancing user and commercial utilities.
LLMs show a consistent performance drop on arithmetic, spatial, and temporal reasoning tasks when framed in multi-turn dialogues versus isolated settings, demonstrated by the new BOULDER benchmark across eight travel-related tasks.
Large-scale study finds that counterfactual metrics on semi-simulated data do not select the same estimators as observable metrics on real data, and benchmark rankings fail to transfer.
Response times modeled as drift-diffusion processes enable consistent estimation of population-average preferences from heterogeneous anonymous binary choices.
PPI++ yields easy-to-compute confidence sets for any-dimensional parameters that always improve on classical intervals from labeled data alone by leveraging abundant ML predictions.
Proposes framing auditing of deployed AI systems as continuous statistical monitoring of risk-controlled constraints like fairness and safety under uncertainty.
citing papers explorer
No citing papers match the current filters.