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Triple/Debiased Lasso for Statistical Inference of Conditional Average Treatment Effects () arXiv:2403.03240v1 Announce Type: new
Abstract: This study investigates the estimation and the statistical inference about Conditional Average Treatment Effects (CATEs), which have garnered attent
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Query Mate เป็นมากกว่าการค้นหาเอกสารทั่วไป การดำเนินการเอกสารขั้นสูงของเราช่วยให้สามารถวิเคราะห์ เปรียบเทียบ และสร้างเนื้อหาที่ซับซ้อนตามเอกสารขององค์กรของคุณได้ การดำเนินการแต่ละรายการได้รับการออกแบบมาเพื่อดึงคุณค่าสูงสุดจากฐานความรู้ของคุณในขณะที่ยังคงรักษาความเป็นส่วนตัวของข้อมูลไว้อย่างสมบูรณ์ telegra.ph/Query-Mate-P...
@querymate.bsky.social การเติบโตของปัญญาประดิษฐ์ (AI) ได้เข้ามาเปลี่ยนแปลงวิธีการดำเนินธุรกิจ (LLMs) แบบดั้งเดิมมักจะมีข้อจำกัดในการจัดการกับข้อมูลที่เป็นกรรมสิทธิ์ของธุรกิจแต่ละแห่ง ซึ่งนี่คือจุดที่เทคโนโลยี Retrieval-Augmented Generation (RAG) เข้ามามีบทบาท และ QueryMate.ai ก็เป็นผู้นำในการทำให้เทคโนโลยีนี้เข้าถึงได้ง่ายสำหรับองค์กร x.com/frontware/st...
Query Mate AI revolutionizes how you interact with information by offering four distinct prompt types. Understanding these prompt types empowers you to choose the best approach for every query, ensuring reliable, contextual, and accurate responses. graph.org/Discover-the...
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🧪Oops—July’s #CompHealthCorner is a few days late (I was traveling)! Catch up on key papers in ML-driven MD, RAG in medicine, open-source ANS tools, and award-winning cancer simulations. Don’t miss these breakthroughs! 👉 comphealth.duke.edu/comphealth-c... #DigitalHealth #AI #Bioengineering
A Robust Pipeline for Differentially Private Federated Learning on Imbalanced Clinical Data using SMOTETomek and FedProx Rodrigo Tertulino http://arxiv.org/abs/2508.10017 Federated Learning (FL) presents a groundbreaking approach for collaborative health research, allowing model training on decentralized data while safeguarding patient privacy. FL offers formal security guarantees when combined with Differential Privacy (DP). The integration of these technologies, however, introduces a significant trade-off between privacy and clinical utility, a challenge further complicated by the severe class imbalance often present in medical datasets. The research presented herein addresses these interconnected issues through a systematic, multi-stage analysis. An FL framework was implemented for cardiovascular risk prediction, where initial experiments showed that standard methods struggled with imbalanced data, resulting in a recall of zero. To overcome such a limitation, we first integrated the hybrid Synthetic Minority Over-sampling Technique with Tomek Links (SMOTETomek) at the client level, successfully developing a clinically useful model. Subsequently, the framework was optimized for non-IID data using a tuned FedProx algorithm. Our final results reveal a clear, non-linear trade-off between the privacy budget (epsilon) and model recall, with the optimized FedProx consistently out-performing standard FedAvg. An optimal operational region was identified on the privacy-utility frontier, where strong privacy guarantees (with epsilon 9.0) can be achieved while maintaining high clinical utility (recall greater than 77%). Ultimately, our study provides a practical methodological blueprint for creating effective, secure, and accurate diagnostic tools that can be applied to real-world, heterogeneous healthcare data.
A Robust Pipeline for Differentially Private Federated Learning on Imbalanced Clinical Data using SMOTETomek and FedProx
Rodrigo Tertulino
http://arxiv.org/abs/2508.10017
Query Mate goes beyond simple document querying. Our advanced document actions enable sophisticated analysis, comparison, and content generation based on your enterprise documents. Each action is designed to extract maximum value from your knowledge base while maintaining complete data privacy.
@querymate.bsky.social See what Query Mate can do with your documents. graph.org/Query-Mate-P...
@querymate.bsky.social Query Mate AI revolutionizes knowledge work with private, secure AI-powered search. It delivers instant, context-aware answers from enterprise data, eliminating inefficiencies and keeping sensitive information safe within your infrastructure.
graph.org/Query-Mate-A...
Query Mate AI is built around a sophisticated Retrieval‑Augmented Generation (RAG) architecture and privacy‑first design, enabling intuitive access to enterprise data – documents, databases (including Odoo ERP), emails, and more – through natural‑language prompts, all without sharing your raw data with third parties. Across its capabilities, Query Mate supports several main prompt styles tailored for different needs. Here are 4 of these styles: 1. Natural‑Language Queries (Zero‑Shot / Basic Open Mode) This is the simplest form: you type a natural phrase like “What were sales in Q2 across Thailand?” and Query Mate dynamically reformulates it, retrieves contextually relevant documents or data, and generates an answer – optionally displaying underlying SQL or graphs. This style is ideal when you just know what you want – you don’t need to provide schema details or training examples. 2. Document‑Based Prompts Query Mate allows you to upload files (PDFs, Word, Markdown, etc.) and query them directly. You can issue prompts such as “Summarize key points in the uploaded contract” or “List all KPI definitions in the team manual.” The system fetches relevant text passages and provides targeted summaries via Query‑Focused Summarization (QFS) within RAG. 3. Text‑to‑SQL / Database Query Prompts When working with SQL databases, Query Mate translates your a natural language request into actual SQL. You might write: “Show average cost per product category last year” or “Top 10 customers by revenue.” You can also enable “view SQL” mode to inspect the generated query. Iterative refinements or follow‑up questions are supported to narrow down or expand results. 4. Template & Automation Prompts For recurring business queries, Query Mate supports saved prompt templates and scheduled execution. You can build and store templates such as “Monthly revenue report: show current versus previous month” or “Weekly inventory status” and schedule them to run automatically.
Query Mate AI is built around a sophisticated Retrieval‑Augmented Generation (RAG) architecture and privacy‑first design, enabling intuitive access to enterprise data – documents, databases (including Odoo ERP), emails, and more – through natural‑language prompts. graph.org/Query-Mate-A...
Imagine you can query your HRMS Weladee (weladee.com) database in plain English to get KPI about your HR data. It's coming soon ... Stay posted.
Weladee will be one of the first application to get a Qᴜᴇʀʏ Mᴀᴛᴇ plugin. As HR manager you will be able to chat with HRMS data from Weladee, privately. You data is not shared to OpenAI, Google, .... Query Mate is goinf to be release by Q4. Join the waiting list to be informed querymate.ai ...
Query mate AI use cases:
- Customer Support: Quickly retrieve answers from product manuals, knowledge bases, and support tickets.
- HR and Operations: Instantly answer employee questions using internal policies, guidelines, and past communications.
- Engineering and R&D
- Compliance and Legal
QueryMate Private rag gives companies full privacy AI power on their data. You data stays provide, it's not shared to third party.
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