We lifted 6 functions out of this paper's own repositories and ran 4 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| antoninschrab/ksdagg-paper | canonical | 1 of 2 |
| antoninschrab/ksdagg | canonical | 1 of 1 |
| copy not recorded | — | 2 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| create_weights | Ran | this paper's copy was not recorded; identical code first harvested from antoninschrab/ksdagg pointer only · get_code("35e8a1dce671edd0") |
| create_weights | Ran | antoninschrab/ksdagg/ksdagg/np.py code served (permissive licence) · get_code("297f50cd6da36f51") |
| jax_distances | Ran | this paper's copy was not recorded; identical code first harvested from antoninschrab/ksdagg pointer only · get_code("f3d6af9cdda0f3d5") |
| ksdagg | Ran | antoninschrab/ksdagg-paper/ksdagg/np.py code served (permissive licence) · get_code("312c1dced4120601") |
| ksdagg | Not yet run | this paper's copy was not recorded; identical code first harvested from antoninschrab/ksdagg pointer only · get_code("c07fb17178973f65") |
| ksdagg | Not yet run | antoninschrab/ksdagg-paper/ksdagg/jax.py code served (permissive licence) · get_code("c7d475d23ef062c8") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We investigate properties of goodness-of-fit tests based on the Kernel Stein Discrepancy (KSD). We introduce a strategy to construct a test, called KSDAgg, which aggregates multiple tests with different kernels. KSDAgg avoids splitting the data to perform kernel selection (which leads to a loss in test power), and rather maximises the test power over a collection of kernels. We provide non-asymptotic guarantees on the power of KSDAgg: we show it achieves the smallest uniform separation rate of the collection, up to a logarithmic term. For compactly supported densities with bounded model score function, we derive the rate for KSDAgg over restricted Sobolev balls; this rate corresponds to the minimax optimal rate over unrestricted Sobolev balls, up to an iterated logarithmic term. KSDAgg can be computed exactly in practice as it relies either on a parametric bootstrap or on a wild bootstrap to estimate the quantiles and the level corrections. In particular, for the crucial choice of bandwidth of a fixed kernel, it avoids resorting to arbitrary heuristics (such as median or standard deviation) or to data splitting. We find on both synthetic and real-world data that KSDAgg outperforms other state-of-the-art quadratic-time adaptive KSD-based goodness-of-fit testing procedures.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2202.00824")
get_code_for_paper("2202.00824")
have("2202.00824")
Connect an agent — have() is free.