README.md

Reproduction: BALLAST (ICML 2026)

Independent, from-scratch reproduction of

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Rui-Yang Zhang, Henry Moss, Lachlan Astfalck, Edward Cripps, David Leslie ICML 2026 · OpenReview 0xOj6kVMbb · arXiv 2509.26005

There is no official code release for this paper. Everything here was written from the paper text (main text + appendices A–H).

Layout

ballast/
  kernels.py      Helmholtz + Matern-3/2 kernels, analytic derivatives,
                  extended GP f = [f, d_t f]^T  (paper Sec. 2.2, 4.1, B.2)
  spde.py         SPDE / state-space formulation, exact propagation (Sec. 4.1, App. F)
  gp.py           regression, posterior sampling, information-gain utilities,
                  rank-q Gram determinant updates (App. B.1, C.1, E.2)
  trajectory.py   Lagrangian advection + observation model (Sec. H.1)
  policies.py     UNIF, SOBOL, DIST-SEP, EIG, BALLAST-true, BALLAST-opt (Sec. H.3)
  experiment.py   active-learning campaign driver, iso-performance (Sec. 5.2)
tests/            correctness checks (see below)
run_job.py        entry point: bench | synth | suntans | ablation | spde_cost
hf_entry.py       Hugging Face Jobs wrapper
analyze.py        raw JSON -> per-claim numbers
plot.py           Plotly figures + raw CSV
fit_suntans.py    put the SUNTANS field in the paper's units; fit the surrogate
data/
  extract_suntans.py   derive surface u,v from the SUNTANS harmonic atlas

Running

uv venv && uv pip install jax numpy scipy matplotlib plotly pandas pytest
.venv/bin/python -m pytest tests/ -q            # 13 correctness tests
.venv/bin/python run_job.py bench               # one full-scale campaign per policy

On a GPU via HF Jobs:

hf jobs uv run --flavor a100-large --timeout 2h -d \
  -e BALLAST_ARGS="synth --seed-lo 0 --seed-hi 10 --out /tmp/o.json --bucket txus/ballast-repro-results" \
  -s HF_TOKEN=$HF_TOKEN hf_entry.py

Tests

tests/ encodes what must be true for the reproduction to mean anything:

• test_spde.py::test_posterior_sampling_is_exact_with_nongridded_observations — the Sec. 4.1 sampler is exact, compared analytically (not by Monte Carlo) against a dense GP, with observations at non-gridded Lagrangian locations.
• test_spde.py::test_prior_matches_dense_gp — SPDE prior ≡ k_tHelm exactly.
• test_kernels.py::test_matern32_second_derivative_at_zero — the Sec. H.2 clipped-distance autodiff trap (d^2_{tt'}k = 3 at t=t', not 0).
• test_padding.py — the shape-padding used for performance is provably inert.

Deviations from the paper, and why

1. Observations are conditioned at the cell centre, not the drifter's exact position. The ground-truth field exists only on the grid and a drifter measures "the velocity of the grid cell containing the location" (Sec. H.1), so the measurement is a noisy observation of f at the cell centre. Regressing it at the exact position is misspecified: for the Sec. 5.2 setup the within-cell field variation has sd ≈ 0.29, about 3× the assumed sigma_obs = 0.1. A GP told the noise is 0.1 then interpolates discretisation error — the posterior mean overshoots to ~3× the true field range and the field error rises above the prior as drifters are added. Snapping removes the misspecification exactly.

2. Utility uses logdet(I + sigma^-2 K), per App. C.1, not the main text's logdet(I + sigma^2 K). The main-text form is a sign-of-exponent typo; with sigma = 0.1 the two differ by 1e4 inside the logdet and rank candidates differently.

3. SUNTANS region and units are inferred (Sec. 5.3 states neither). See fit_suntans.py for the reasoning and the agreement it produces.

4. Iso-performance is averaged over deployment iterations, per the paper's Sec. 5.2 wording ("averaged over each iteration's results"), not read at the final iteration. The final-iteration value ("drifters to match uniform's final accuracy") is ~2x larger; the averaged value matches the paper's synthetic number almost exactly (3.4 vs 3). analyze.py reports both.

5. The paper's stated SUNTANS "true" stream-kernel values (psi_ls=4, psi_var=0.01) lie outside its own stated BALLAST-opt bounds ([0.1,1] and [0.1,5], Sec. H.3). We use the stated values for BALLAST-true and the stated bounds for BALLAST-opt.

Artifacts

• Code + data: https://huggingface.co/datasets/txus/ballast-repro
• Raw results: https://huggingface.co/datasets/txus/ballast-repro-results

SUNTANS ground truth derives from the Northern Australia Internal Tide Climatology (Rayson et al. 2021, CC BY, DOI 10.26182/8jx9-m532), read with the physics of mrayson/iwatlas.