Calibrated alpha-Barycenters for Multiobjective f-Divergence Optimization

arXiv:2610.16819v1 Announce Type: cross Abstract: Matching one distribution to many targets can require repeated evaluations of every target. CAL-alpha-MODM aggregates a nonlinear predictor into one barycenter, calibrates its residual derivatives, and checks steps against the original f-divergence objective. Under smoothness assumptions, calibration controls parameter-step model error without requiring targets to be close to the model. Exact-oracle stationarity and local convergence guarantees are established. At common matching, canonical geometry cancels the leading quadratic iteration error, giving cubic convergence with second-order calibration and sufficiently accurate inner solves. High-precision experiments support this prediction. Sensitivity-based initialization also accelerates preference continuation along regular stationary branches. On 300-target Jensen--Shannon problems, CAL-p2 achieves median paired speedups of 3.15imes over L-BFGS and 1.10imes over second-order Direct-TR, across 23/25 and 24/25 objective-comparable stationary pairs. The savings arise when cheaper predictor queries offset calibration overhead.

Source: arXiv cs.LG | 2026-10-14