{"schema_version":"onlylabs.public_signal.v1","title":"Google (DeepMind / Gemini) Repo: google-deepmind/llp_bp","description":"Google (DeepMind / Gemini) repo signal with public source context, captured evidence pages, related signals, and data-business radar classification.","url":"https://onlylabs.fyi/signals/f2404fc6-cc80-46b2-a1ac-4a7b300b1ac1","json_url":"https://onlylabs.fyi/signals/f2404fc6-cc80-46b2-a1ac-4a7b300b1ac1/signal.json","generated_at":"2026-06-11T04:00:23.893554+00:00","org":{"slug":"google-deepmind","name":"Google (DeepMind / Gemini)","category":"frontier-lab","category_label":"Frontier lab","dossier_url":"https://onlylabs.fyi/labs/google-deepmind","dossier_json_url":"https://onlylabs.fyi/labs/google-deepmind/dossier.json"},"related_urls":{"signal":"https://onlylabs.fyi/signals/f2404fc6-cc80-46b2-a1ac-4a7b300b1ac1","signal_json":"https://onlylabs.fyi/signals/f2404fc6-cc80-46b2-a1ac-4a7b300b1ac1/signal.json","source":"https://github.com/google-deepmind/llp_bp","lab_dossier":"https://onlylabs.fyi/labs/google-deepmind","lab_dossier_json":"https://onlylabs.fyi/labs/google-deepmind/dossier.json","analysis":"https://onlylabs.fyi/analysis/google-deepmind","analysis_json":"https://onlylabs.fyi/analysis/google-deepmind/analysis.json","analysis_evidence_json":"https://onlylabs.fyi/analysis/google-deepmind/evidence.json","category":"https://onlylabs.fyi/frontier","category_json":"https://onlylabs.fyi/frontier.json","category_feed":"https://onlylabs.fyi/frontier/feed.xml","category_signals_json":"https://onlylabs.fyi/signals.json","topic":null,"topic_signals_json":null,"topic_feed":null,"data_business":null},"answer_pack":{"answer":"Google (DeepMind / Gemini) published google-deepmind/llp_bp (Python). This repository signal exposes tooling, eval, infrastructure, or model-adjacent work before it may appear in a launch post. 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Abstract Learning from Label Proportions (LLP) is a learning problem where only aggregate level labels are available for groups of instances, called bags, during training, and the aim is to get the best performance at the instance-level on the test data. This setting arises in domains like advertising and medicine due to privacy considerations. We propose a novel algorithmic framework foßr this problem that iteratively performs two main steps. For the first step (Pseudo Labeling) in every iteration, we define a Gibbs distribution over binary instance labels that incorporates a. covariate information through the constraint that instances with similar covariates should have similar labels and b. the bag level aggregated label. We then use Belief Propagation..."},"evidence_pages":[{"url":"https://github.com/google-deepmind/llp_bp","final_url":"https://github.com/google-deepmind/llp_bp","title":"google-deepmind/llp_bp repository metadata","http_status":200,"content_type":"application/json","capture_method":"plain","fetched_at":"2026-06-11T04:00:23.893554+00:00","bytes":11566,"raw_path":"d497fb5b63f72c62f9ed4999cd96a82b3b07bec240c4ed22bf8b60b166417644.json","content_hash":"d6c14f48f5aead9043e6740daa8fc26a543450a472f80decfff2152b2a01abff","excerpt_chars":1200,"truncated":true,"excerpt":"google-deepmind/llp_bp Language: Python License: Apache-2.0 Stars: 2 Forks: 0 Open issues: 0 Created: 2025-06-01T10:06:16Z Pushed: 2025-06-01T11:00:59Z Default branch: main Fork: no Archived: no README: llp_bp This repository provides the code for implementing the LLP-BP algorithm described in the paper [Learning from Label Proportions: Bootstrapping Supervised Learners via Belief Propagation](https://arxiv.org/abs/2310.08056). Abstract Learning from Label Proportions (LLP) is a learning problem where only aggregate level labels are available for groups of instances, called bags, during training, and the aim is to get the best performance at the instance-level on the test data. This setting arises in domains like advertising and medicine due to privacy considerations. We propose a novel algorithmic framework foßr this problem that iteratively performs two main steps. For the first step (Pseudo Labeling) in every iteration, we define a Gibbs distribution over binary instance labels that incorporates a. covariate information through the constraint that instances with similar covariates should have similar labels and b. the bag level aggregated label. 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