Pith Number

pith:BZJ6JS2Z

pith:2026:BZJ6JS2ZRXCHPTPBHLGVXBOI46

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Transformed Latent Variable Multi-Output Gaussian Processes

Magnus Rattray, Mauricio A \'Alvarez, Sokratia Georgaka, Xiaoyu Jiang, Xinxing Shi

T-LVMOGP scales multi-output Gaussian processes to over 10,000 outputs by embedding inputs and per-output latent variables through a Lipschitz-regularised neural network.

arxiv:2605.05133 v2 · 2026-05-06 · cs.LG

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Claims

C1strongest claim

T-LVMOGP constructs a flexible multi-output deep kernel by mapping inputs and output-specific latent variables into an embedding space using a Lipschitz-regularised neural network, scales MOGPs to a massive number of outputs while preserving inter-output dependencies, and outperforms baselines in predictive accuracy and computational efficiency on climate modelling with over 10,000 outputs and zero-inflated spatial transcriptomics data.

C2weakest assumption

The Lipschitz-regularised neural network mapping of inputs and output-specific latent variables into an embedding space is sufficient to capture meaningful inter-output dependencies without excessive loss of expressiveness or introduction of new fitting artefacts when combined with stochastic variational inference.

C3one line summary

T-LVMOGP scales multi-output Gaussian processes to massive output dimensions using transformed latent variables, deep kernels, and stochastic variational inference while capturing inter-output dependencies.

Receipt and verification

First computed	2026-05-21T01:04:26.810538Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

0e53e4cb598dc477cde13acd5b85c8e7bf78ce0beb57d036d7b119e10b4dd18e

Aliases

arxiv: 2605.05133 · arxiv_version: 2605.05133v2 · doi: 10.48550/arxiv.2605.05133 · pith_short_12: BZJ6JS2ZRXCH · pith_short_16: BZJ6JS2ZRXCHPTPB · pith_short_8: BZJ6JS2Z

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/BZJ6JS2ZRXCHPTPBHLGVXBOI46 \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 0e53e4cb598dc477cde13acd5b85c8e7bf78ce0beb57d036d7b119e10b4dd18e

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "aaec98ddf0057b093fac21bdab3ab1077b00edc1e60942511c313fff002e33df",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-05-06T17:05:50Z",
    "title_canon_sha256": "a7028dfd5484ef29d92a6ba527663be4a18a33c7699c0a64ae5cc305a54e1afe"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.05133",
    "kind": "arxiv",
    "version": 2
  }
}