Leonardo Nunes

Principal Research SDE

出版物

表示方法:

- Direct Reasoning Optimization: LLMs Can Reward And Refine Their Own Reasoning for Open-Ended Tasks
  
  Yifei Xu, Tusher Chakraborty, Srinagesh Sharma, Leonardo Nunes, Emre Kiciman, Songwu Lu, Ranveer Chandra
  
  Arxiv | June 2025
  
  Preprint
- RLTHF: Targeted Human Feedback for LLM Alignment
  
  Yifei Xu, Tusher Chakraborty, Emre Kiciman, Bibek Aryal, Eduardo Rodrigues, Srinagesh Sharma, Roberto Estevao, Maria Angels de Luis Balaguer, Jessica Wolk, Rafael Padilha, Leonardo Nunes, Shobana Balakrishna, Songwu Lu, Ranveer Chandra
  
  ICML'25 | February 2025
  
  PDF
- Injecting New Knowledge into Large Language Models via Supervised Fine-Tuning
  
  Nick Mecklenburg, Yiyou Lin, Xiaoxiao Li, Daniel Holstein, Leonardo Nunes, Sara Malvar, Bruno Silva, Ranveer Chandra, Vijay Aski, Pavan Kumar Reddy Yannam, Tolga Aktas
  
  ArXiv | April 2024
  
  Publication プロジェクトプロジェクト
- RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture
  
  Maria Angels de Luis Balaguer, Vinamra Benara, Renato L. de F. Cunha, Roberto Estevão, Todd Hendry, Daniel Holstein, Jennifer Marsman, Nick Mecklenburg, Sara Malvar, Leonardo Nunes, Rafael Padilha, Morris Sharp, Bruno Silva, Swati Sharma, Vijay Aski, Ranveer Chandra
  
  January 2024
  
  DOI Publication プロジェクトプロジェクト
- GPT-4 as an Agronomist Assistant? Answering Agriculture Exams Using Large Language Models
  
  Bruno Silva, Leonardo Nunes, Roberto Estevão, Vijay Aski, Ranveer Chandra
  
  October 2023
  
  Publication プロジェクトプロジェクト
- Source Attribution and Emissions Quantification for Methane Leak Detection: A Non-Linear Bayesian Regression Approach
  
  Mirco Milletari, Sara Malvar, Yagna Oruganti, Leonardo Nunes, Yazeed Alaudah, Anirudh Badam
  
  The 8th International Online & Onsite Conference on Machine Learning, Optimization, and Data Science | September 2022
  
  Best Paper Award Special Mention
  
  Publication プロジェクト
- Unsupervised Machine Learning framework for sensor placement optimization: analyzing methane leaks
  
  Shirui Wang, Sara Malvar, Leonardo Nunes, Kim Whitehall, Yagna Oruganti, Yazeed Alaudah, Anirudh Badam
  
  NeurIPS 2021 Workshop Tackling Climate Change with Machine Learning | December 2021
  
  Publication プロジェクト

- Direct Reasoning Optimization: LLMs Can Reward And Refine Their Own Reasoning for Open-Ended Tasks
  
  Yifei Xu, Tusher Chakraborty, Srinagesh Sharma, Leonardo Nunes, Emre Kiciman, Songwu Lu, Ranveer Chandra
  
  Arxiv | June 2025
  
  Preprint
- RLTHF: Targeted Human Feedback for LLM Alignment
  
  Yifei Xu, Tusher Chakraborty, Emre Kiciman, Bibek Aryal, Eduardo Rodrigues, Srinagesh Sharma, Roberto Estevao, Maria Angels de Luis Balaguer, Jessica Wolk, Rafael Padilha, Leonardo Nunes, Shobana Balakrishna, Songwu Lu, Ranveer Chandra
  
  ICML'25 | February 2025
  
  PDF
- Injecting New Knowledge into Large Language Models via Supervised Fine-Tuning
  
  Nick Mecklenburg, Yiyou Lin, Xiaoxiao Li, Daniel Holstein, Leonardo Nunes, Sara Malvar, Bruno Silva, Ranveer Chandra, Vijay Aski, Pavan Kumar Reddy Yannam, Tolga Aktas
  
  ArXiv | April 2024
  
  Publication プロジェクトプロジェクト
- RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture
  
  Maria Angels de Luis Balaguer, Vinamra Benara, Renato L. de F. Cunha, Roberto Estevão, Todd Hendry, Daniel Holstein, Jennifer Marsman, Nick Mecklenburg, Sara Malvar, Leonardo Nunes, Rafael Padilha, Morris Sharp, Bruno Silva, Swati Sharma, Vijay Aski, Ranveer Chandra
  
  January 2024
  
  DOI Publication プロジェクトプロジェクト
- GPT-4 as an Agronomist Assistant? Answering Agriculture Exams Using Large Language Models
  
  Bruno Silva, Leonardo Nunes, Roberto Estevão, Vijay Aski, Ranveer Chandra
  
  October 2023
  
  Publication プロジェクトプロジェクト
- Source Attribution and Emissions Quantification for Methane Leak Detection: A Non-Linear Bayesian Regression Approach
  
  Mirco Milletari, Sara Malvar, Yagna Oruganti, Leonardo Nunes, Yazeed Alaudah, Anirudh Badam
  
  The 8th International Online & Onsite Conference on Machine Learning, Optimization, and Data Science | September 2022
  
  Best Paper Award Special Mention
  
  Publication プロジェクト
- Unsupervised Machine Learning framework for sensor placement optimization: analyzing methane leaks
  
  Shirui Wang, Sara Malvar, Leonardo Nunes, Kim Whitehall, Yagna Oruganti, Yazeed Alaudah, Anirudh Badam
  
  NeurIPS 2021 Workshop Tackling Climate Change with Machine Learning | December 2021
  
  Publication プロジェクト
- Injecting New Knowledge into Large Language Models via Supervised Fine-Tuning
  
  Nick Mecklenburg, Yiyou Lin, Xiaoxiao Li, Daniel Holstein, Leonardo Nunes, Sara Malvar, Bruno Silva, Ranveer Chandra, Vijay Aski, Pavan Kumar Reddy Yannam, Tolga Aktas
  
  ArXiv | April 2024
  
  Publication プロジェクトプロジェクト
- RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture
  
  Maria Angels de Luis Balaguer, Vinamra Benara, Renato L. de F. Cunha, Roberto Estevão, Todd Hendry, Daniel Holstein, Jennifer Marsman, Nick Mecklenburg, Sara Malvar, Leonardo Nunes, Rafael Padilha, Morris Sharp, Bruno Silva, Swati Sharma, Vijay Aski, Ranveer Chandra
  
  January 2024
  
  DOI Publication プロジェクトプロジェクト
- Source Attribution and Emissions Quantification for Methane Leak Detection: A Non-Linear Bayesian Regression Approach
  
  Mirco Milletari, Sara Malvar, Yagna Oruganti, Leonardo Nunes, Yazeed Alaudah, Anirudh Badam
  
  The 8th International Online & Onsite Conference on Machine Learning, Optimization, and Data Science | September 2022
  
  Best Paper Award Special Mention
  
  Publication プロジェクト
- Unsupervised Machine Learning framework for sensor placement optimization: analyzing methane leaks
  
  Shirui Wang, Sara Malvar, Leonardo Nunes, Kim Whitehall, Yagna Oruganti, Yazeed Alaudah, Anirudh Badam
  
  NeurIPS 2021 Workshop Tackling Climate Change with Machine Learning | December 2021
  
  Publication プロジェクト
- Unsupervised Machine Learning framework for sensor placement optimization: analyzing methane leaks
  
  Shirui Wang, Sara Malvar, Leonardo Nunes, Kim Whitehall, Yagna Oruganti, Yazeed Alaudah, Anirudh Badam
  
  NeurIPS 2021 Workshop Tackling Climate Change with Machine Learning | December 2021
  
  Publication プロジェクト

- Direct Reasoning Optimization: LLMs Can Reward And Refine Their Own Reasoning for Open-Ended Tasks
  
  Yifei Xu, Tusher Chakraborty, Srinagesh Sharma, Leonardo Nunes, Emre Kiciman, Songwu Lu, Ranveer Chandra
  
  Arxiv | June 2025
  
  Preprint
- RLTHF: Targeted Human Feedback for LLM Alignment
  
  Yifei Xu, Tusher Chakraborty, Emre Kiciman, Bibek Aryal, Eduardo Rodrigues, Srinagesh Sharma, Roberto Estevao, Maria Angels de Luis Balaguer, Jessica Wolk, Rafael Padilha, Leonardo Nunes, Shobana Balakrishna, Songwu Lu, Ranveer Chandra
  
  ICML'25 | February 2025
  
  PDF
- Source Attribution and Emissions Quantification for Methane Leak Detection: A Non-Linear Bayesian Regression Approach
  
  Mirco Milletari, Sara Malvar, Yagna Oruganti, Leonardo Nunes, Yazeed Alaudah, Anirudh Badam
  
  The 8th International Online & Onsite Conference on Machine Learning, Optimization, and Data Science | September 2022
  
  Best Paper Award Special Mention
  
  Publication プロジェクト
- Unsupervised Machine Learning framework for sensor placement optimization: analyzing methane leaks
  
  Shirui Wang, Sara Malvar, Leonardo Nunes, Kim Whitehall, Yagna Oruganti, Yazeed Alaudah, Anirudh Badam
  
  NeurIPS 2021 Workshop Tackling Climate Change with Machine Learning | December 2021
  
  Publication プロジェクト
- Injecting New Knowledge into Large Language Models via Supervised Fine-Tuning
  
  Nick Mecklenburg, Yiyou Lin, Xiaoxiao Li, Daniel Holstein, Leonardo Nunes, Sara Malvar, Bruno Silva, Ranveer Chandra, Vijay Aski, Pavan Kumar Reddy Yannam, Tolga Aktas
  
  ArXiv | April 2024
  
  Publication プロジェクトプロジェクト
- RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture
  
  Maria Angels de Luis Balaguer, Vinamra Benara, Renato L. de F. Cunha, Roberto Estevão, Todd Hendry, Daniel Holstein, Jennifer Marsman, Nick Mecklenburg, Sara Malvar, Leonardo Nunes, Rafael Padilha, Morris Sharp, Bruno Silva, Swati Sharma, Vijay Aski, Ranveer Chandra
  
  January 2024
  
  DOI Publication プロジェクトプロジェクト
- GPT-4 as an Agronomist Assistant? Answering Agriculture Exams Using Large Language Models
  
  Bruno Silva, Leonardo Nunes, Roberto Estevão, Vijay Aski, Ranveer Chandra
  
  October 2023
  
  Publication プロジェクトプロジェクト

Leonardo Nunes

出版物

Direct Reasoning Optimization: LLMs Can Reward And Refine Their Own Reasoning for Open-Ended Tasks

RLTHF: Targeted Human Feedback for LLM Alignment

Injecting New Knowledge into Large Language Models via Supervised Fine-Tuning

RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture

GPT-4 as an Agronomist Assistant? Answering Agriculture Exams Using Large Language Models

Source Attribution and Emissions Quantification for Methane Leak Detection: A Non-Linear Bayesian Regression Approach

Unsupervised Machine Learning framework for sensor placement optimization: analyzing methane leaks

Direct Reasoning Optimization: LLMs Can Reward And Refine Their Own Reasoning for Open-Ended Tasks

RLTHF: Targeted Human Feedback for LLM Alignment

Injecting New Knowledge into Large Language Models via Supervised Fine-Tuning

RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture

GPT-4 as an Agronomist Assistant? Answering Agriculture Exams Using Large Language Models

Source Attribution and Emissions Quantification for Methane Leak Detection: A Non-Linear Bayesian Regression Approach

Unsupervised Machine Learning framework for sensor placement optimization: analyzing methane leaks

Injecting New Knowledge into Large Language Models via Supervised Fine-Tuning

RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture

Source Attribution and Emissions Quantification for Methane Leak Detection: A Non-Linear Bayesian Regression Approach

Unsupervised Machine Learning framework for sensor placement optimization: analyzing methane leaks

Unsupervised Machine Learning framework for sensor placement optimization: analyzing methane leaks

Direct Reasoning Optimization: LLMs Can Reward And Refine Their Own Reasoning for Open-Ended Tasks

RLTHF: Targeted Human Feedback for LLM Alignment

Source Attribution and Emissions Quantification for Methane Leak Detection: A Non-Linear Bayesian Regression Approach

Unsupervised Machine Learning framework for sensor placement optimization: analyzing methane leaks

Injecting New Knowledge into Large Language Models via Supervised Fine-Tuning

RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture

GPT-4 as an Agronomist Assistant? Answering Agriculture Exams Using Large Language Models

お問い合わせ Leonardo Nunes

Microsoft Research Lab – Redmond