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Actions: google/dpsynth
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155 workflow runs
155 workflow runs
workload from MechanismConfig subclasses (DirectConfig, AIMConfig, SWIFTConfig) to configure(..., workload=...) and calibrate(..., workload=...) across TabularConfig, DiscreteConfig, BeamTabularConfig, DirectConfig, AIMConfig, and SWIFTConfig, validating workloads against the schema/domain at configuration time via Workload.from_any(workload, domain).
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#156:
Pull request #244
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jax.tree_util.register_dataclass(mbi.Dataset) call inside _checkpoint.py.
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#150:
Pull request #238
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estimator configuration field to DirectConfig. The default MirrorDescent estimator is pretty robust but only has a O(1/t) convergence rate. mbi supports other estimators that enjoy improved O(1/t^2) convergence rates, which can be particularly helpful in regimes with high signal to noise ratio where getting close to the optimum is important.
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#148:
Pull request #236
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with_group_size in MultiTableMechanism instead of max_records_per_user plumbing.
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#146:
Pull request #234
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delta via delta_split in calibrate and equally across open-set columns in configure.
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#144:
Pull request #232
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group_size parameter to calibrate and export dpsynth.with_group_size.
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#142:
Pull request #230
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TabularSynthesizer alias from dpsynth.
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#141:
Pull request #229
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bin_edges to NumericalAttribute.
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#140:
Pull request #228
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configure(zcdp_rho) to configure(budget) across dpsynth and update docs as appropriate. The explicit mention of "ZCDP" in dpsynth is a point of confusion, we we want to point end users away from this API, and make it easier for developers to understand by explicitly calling it a "dummy budget" with a rough zcdp interpretation.
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#139:
Pull request #227
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protobuf.infer_domain and cache field getters in protobuf.to_tuple.
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#134:
Pull request #221
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numerical_bounds in dpsynth.adapters.pydantic.infer_domain.
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#132:
Pull request #219
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