A control-variable method using generalized additive modeling and cross-fitted ridge refitting removes omitted-variable bias from deep networks, yielding unbiased predictions.
AlphaQ allocates MoE quantization bits without calibration using heavy-tailed spectral analysis, outperforming calibration-based methods and achieving near full-precision accuracy at 3.5-bit average precision.
MA-BC partitions conflicting expert trajectories while pooling compatible data to recover Pareto-optimal policies in multi-objective imitation with minimax optimal rates.
Topological and geometric embedding measures are redundant, so Unified Topological Signatures holistically characterize spaces to predict model properties and retrieval performance.