{"schema_version":"onlylabs.public_signal.v1","title":"Google (DeepMind / Gemini) Repo: google-deepmind/mir_uai25","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/f79f6727-0a88-45f6-b674-586c07005ac4","json_url":"https://onlylabs.fyi/signals/f79f6727-0a88-45f6-b674-586c07005ac4/signal.json","generated_at":"2026-06-11T04:00:22.79976+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/f79f6727-0a88-45f6-b674-586c07005ac4","signal_json":"https://onlylabs.fyi/signals/f79f6727-0a88-45f6-b674-586c07005ac4/signal.json","source":"https://github.com/google-deepmind/mir_uai25","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/mir_uai25 (Python). This repository signal exposes tooling, eval, infrastructure, or model-adjacent work before it may appear in a launch post. 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repositories can expose organization build priorities early, especially around internal tooling, eval infrastructure, data systems, deployment, and agent workflows.","required_sources":[{"label":"signal_json","url":"https://onlylabs.fyi/signals/f79f6727-0a88-45f6-b674-586c07005ac4/signal.json","required":true},{"label":"source","url":"https://github.com/google-deepmind/mir_uai25","required":true},{"label":"dossier_json","url":"https://onlylabs.fyi/labs/google-deepmind/dossier.json","required":true},{"label":"analysis_evidence_json","url":"https://onlylabs.fyi/analysis/google-deepmind/evidence.json","required":true},{"label":"topic_signals_json","url":null,"required":false},{"label":"data_radar_json","url":null,"required":false}],"expected_output":["one-paragraph source-grounded interpretation","category-specific implication","confidence and missing evidence","recommended next source to inspect"],"prompt_seed":"Using only the linked onlylabs JSON, captured source context, and cited 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channels.."},{"subject":"google-deepmind/mir_uai25","predicate":"has source host","object":"github.com","text":"google-deepmind/mir_uai25 has source host github.com."},{"subject":"google-deepmind/mir_uai25","predicate":"has lab","object":"Google (DeepMind / Gemini)","text":"google-deepmind/mir_uai25 has lab Google (DeepMind / Gemini)."},{"subject":"google-deepmind/mir_uai25","predicate":"has signal desk","object":"repos","text":"google-deepmind/mir_uai25 has signal desk repos."},{"subject":"google-deepmind/mir_uai25","predicate":"has source host","object":"github.com","text":"google-deepmind/mir_uai25 has source host github.com."},{"subject":"google-deepmind/mir_uai25","predicate":"has repository","object":"google-deepmind/mir_uai25","text":"google-deepmind/mir_uai25 has repository google-deepmind/mir_uai25."},{"subject":"google-deepmind/mir_uai25","predicate":"has language","object":"Python","text":"google-deepmind/mir_uai25 has language 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Installation Install numpy, pandas, scipy, scikit-learn, tensorflow, tensorflow-probability in a conda environment with versions as given in requirements.txt Activate the conda environment. ```bash optional: clear any previous venv hidden files rm -rf .venv use venv python --version # make sure you have python 3.12 python3 -m venv .venv source .venv/bin/activate install dependencies pip install -r requirements.txt ``` Datasets There are four folders linearreal, linearsynthetic, linearsyntheticerror, nnsynthetic corresponding to experiments on a real dataset with linear regression, linear regression on synthetic data, linear regression on noisy synthetic data, neural regression on synthetic data. In the linearreal folder, download the wine quality dataset using this link (https://archive.ics.uci.edu/dataset/186/wine+quality) as 'redwine.csv' and 'whitewine.csv'. Run..."},"evidence_pages":[{"url":"https://github.com/google-deepmind/mir_uai25","final_url":"https://github.com/google-deepmind/mir_uai25","title":"google-deepmind/mir_uai25 repository metadata","http_status":200,"content_type":"application/json","capture_method":"plain","fetched_at":"2026-06-11T04:00:22.79976+00:00","bytes":10836,"raw_path":"e6a0f9de5858d0d35bfad8f862d47f08ca101300627c50e62117b71aa7fdfeeb.json","content_hash":"a4cc495237030779f2956a3743767f31e61e56b6652b3db1d612cee1fd1a118c","excerpt_chars":1200,"truncated":true,"excerpt":"google-deepmind/mir_uai25 Language: Python License: Apache-2.0 Stars: 6 Forks: 0 Open issues: 0 Created: 2025-06-20T07:18:28Z Pushed: 2025-11-04T07:56:58Z Default branch: main Fork: no Archived: no README: Learning Algorithms for Multiple Instance Regression Author: Aaryan Gupta and Rishi Saket To Appear in UAI'25. Installation Install numpy, pandas, scipy, scikit-learn, tensorflow, tensorflow-probability in a conda environment with versions as given in requirements.txt Activate the conda environment. ```bash optional: clear any previous venv hidden files rm -rf .venv use venv python --version # make sure you have python 3.12 python3 -m venv .venv source .venv/bin/activate install dependencies pip install -r requirements.txt ``` Datasets There are four folders linearreal, linearsynthetic, linearsyntheticerror, nnsynthetic corresponding to experiments on a real dataset with linear regression, linear regression on synthetic data, linear regression on noisy synthetic data, neural regression on synthetic data. In the linearreal folder, download the wine quality dataset using this link (https://archive.ics.uci.edu/dataset/186/wine+quality) as 'redwine.csv' and 'whitewine.csv'. 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