Jonathan Lebensold

Tapered Off-Policy REINFORCE: Stable and efficient reinforcement learning for LLMs

Nicolas Le Roux

Marc Gendron-Bellemare

Arnaud Bergeron

Joshua Greaves

Alex Fr'echette

Carolyne Pelletier

Eric Thibodeau-Laufer

S'andor Toth

Sam Work

2025-03-18

ArXiv (prépublication)

Mitigating Downstream Model Risks via Model Provenance

Keyu Wang

Abdullah Norozi Iranzad

Scott Schaffter

Doina Precup

Meg Risdal

Research and industry are rapidly advancing the innovation and adoption of foundation model-based systems, yet the tools for managing these … (voir plus)models have not kept pace. Understanding the provenance and lineage of models is critical for researchers, industry, regulators, and public trust. While model cards and system cards were designed to provide transparency, they fall short in key areas: tracing model genealogy, enabling machine readability, offering reliable centralized management systems, and fostering consistent creation incentives. This challenge mirrors issues in software supply chain security, but AI/ML remains at an earlier stage of maturity. Addressing these gaps requires industry-standard tooling that can be adopted by foundation model publishers, open-source model innovators, and major distribution platforms. We propose a machine-readable model specification format to simplify the creation of model records, thereby reducing error-prone human effort, notably when a new model inherits most of its design from a foundation model. Our solution explicitly traces relationships between upstream and downstream models, enhancing transparency and traceability across the model lifecycle. To facilitate the adoption, we introduce the unified model record (UMR) repository , a semantically versioned system that automates the publication of model records to multiple formats (PDF, HTML, LaTeX) and provides a hosted web interface (https://modelrecord.com/). This proof of concept aims to set a new standard for managing foundation models, bridging the gap between innovation and responsible model management.

2024-10-09

NeurIPS.cc/2024/Workshop/SoLaR (poster)

openreview.net

An Introduction to Vision-Language Modeling

Richard Yuanzhe Pang

Anurag Ajay

Alexander C. Li

Adrien Bardes

Suzanne Petryk

Zhiqiu Lin

Bargav Jayaraman

Mark Ibrahim

Melissa Hall

Yunyang Xiong

Candace Ross

Srihari Jayakumar

Chuan Guo

Diane Bouchacourt

Haider Al-Tahan

Karthik Padthe … (voir 21 de plus)

Vasu Sharma

Huijuan Xu 0001

Xiaoqing Ellen Tan

Megan Richards

Samuel Lavoie

Pietro Astolfi

Jun Chen

Kushal Tirumala

Mazda Moayeri

Arjang Talattof

Kamalika Chaudhuri

Zechun Liu

Xilun Chen

Quentin Garrido

Karen Ullrich

Kate Saenko

Asli Celikyilmaz

Vikas Chandra

Following the recent popularity of Large Language Models (LLMs), several attempts have been made to extend them to the visual domain. From h… (voir plus)aving a visual assistant that could guide us through unfamiliar environments to generative models that produce images using only a high-level text description, the vision-language model (VLM) applications will significantly impact our relationship with technology. However, there are many challenges that need to be addressed to improve the reliability of those models. While language is discrete, vision evolves in a much higher dimensional space in which concepts cannot always be easily discretized. To better understand the mechanics behind mapping vision to language, we present this introduction to VLMs which we hope will help anyone who would like to enter the field. First, we introduce what VLMs are, how they work, and how to train them. Then, we present and discuss approaches to evaluate VLMs. Although this work primarily focuses on mapping images to language, we also discuss extending VLMs to videos.

2024-05-27

ArXiv (prépublication)

An Introduction to Vision-Language Modeling

Richard Yuanzhe Pang

Anurag Ajay

Alexander C. Li

Adrien Bardes

Suzanne Petryk

Zhiqiu Lin

Bargav Jayaraman

Mark Ibrahim

Melissa Hall

Yunyang Xiong

Candace Ross

Srihari Jayakumar

Chuan Guo

Diane Bouchacourt

Haider Al-Tahan

Karthik Padthe … (voir 21 de plus)

Vasu Sharma

Huijuan Xu 0001

Xiaoqing Ellen Tan

Megan Richards

Samuel Lavoie

Pietro Astolfi

Jun Chen

Kushal Tirumala

Mazda Moayeri

Arjang Talattof

Kamalika Chaudhuri

Zechun Liu

Xilun Chen

Quentin Garrido

Karen Ullrich

Kate Saenko

Asli Celikyilmaz

Vikas Chandra

Following the recent popularity of Large Language Models (LLMs), several attempts have been made to extend them to the visual domain. From h… (voir plus)aving a visual assistant that could guide us through unfamiliar environments to generative models that produce images using only a high-level text description, the vision-language model (VLM) applications will significantly impact our relationship with technology. However, there are many challenges that need to be addressed to improve the reliability of those models. While language is discrete, vision evolves in a much higher dimensional space in which concepts cannot always be easily discretized. To better understand the mechanics behind mapping vision to language, we present this introduction to VLMs which we hope will help anyone who would like to enter the field. First, we introduce what VLMs are, how they work, and how to train them. Then, we present and discuss approaches to evaluate VLMs. Although this work primarily focuses on mapping images to language, we also discuss extending VLMs to videos.

2024-05-27

ArXiv (prépublication)

An Introduction to Vision-Language Modeling

Richard Yuanzhe Pang

Anurag Ajay

Alexander C. Li

Adrien Bardes

Suzanne Petryk

Zhiqiu Lin

Bargav Jayaraman

Mark Ibrahim

Melissa Hall

Yunyang Xiong

Candace Ross

Srihari Jayakumar

Chuan Guo

Diane Bouchacourt

Haider Al-Tahan

Karthik Padthe … (voir 21 de plus)

Vasu Sharma

Huijuan Xu 0001

Xiaoqing Ellen Tan

Megan Richards

Samuel Lavoie

Pietro Astolfi

Jun Chen

Kushal Tirumala

Mazda Moayeri

Arjang Talattof

Kamalika Chaudhuri

Zechun Liu

Xilun Chen

Quentin Garrido

Karen Ullrich

Kate Saenko

Asli Celikyilmaz

Vikas Chandra

Following the recent popularity of Large Language Models (LLMs), several attempts have been made to extend them to the visual domain. From h… (voir plus)aving a visual assistant that could guide us through unfamiliar environments to generative models that produce images using only a high-level text description, the vision-language model (VLM) applications will significantly impact our relationship with technology. However, there are many challenges that need to be addressed to improve the reliability of those models. While language is discrete, vision evolves in a much higher dimensional space in which concepts cannot always be easily discretized. To better understand the mechanics behind mapping vision to language, we present this introduction to VLMs which we hope will help anyone who would like to enter the field. First, we introduce what VLMs are, how they work, and how to train them. Then, we present and discuss approaches to evaluate VLMs. Although this work primarily focuses on mapping images to language, we also discuss extending VLMs to videos.

2024-05-27

ArXiv (prépublication)

An Introduction to Vision-Language Modeling

Richard Yuanzhe Pang

Anurag Ajay

Alexander C. Li

Adrien Bardes

Suzanne Petryk

Zhiqiu Lin

Bargav Jayaraman

Mark Ibrahim

Melissa Hall

Yunyang Xiong

Candace Ross

Srihari Jayakumar

Chuan Guo

Diane Bouchacourt

Haider Al-Tahan

Karthik Padthe … (voir 21 de plus)

Vasu Sharma

Huijuan Xu 0001

Xiaoqing Ellen Tan

Megan Richards

Samuel Lavoie

Pietro Astolfi

Jun Chen

Kushal Tirumala

Mazda Moayeri

Arjang Talattof

Kamalika Chaudhuri

Zechun Liu

Xilun Chen

Quentin Garrido

Karen Ullrich

Kate Saenko

Asli Celikyilmaz

Vikas Chandra

2024-05-27

ArXiv (prépublication)

An Introduction to Vision-Language Modeling

Richard Yuanzhe Pang

Anurag Ajay

Alexander C. Li

Adrien Bardes

Suzanne Petryk

Zhiqiu Lin

Bargav Jayaraman

Mark Ibrahim

Melissa Hall

Yunyang Xiong

Candace Ross

Srihari Jayakumar

Chuan Guo

Diane Bouchacourt

Haider Al-Tahan

Karthik Padthe … (voir 21 de plus)

Vasu Sharma

Huijuan Xu 0001

Xiaoqing Ellen Tan

Megan Richards

Samuel Lavoie

Pietro Astolfi

Jun Chen

Kushal Tirumala

Mazda Moayeri

Arjang Talattof

Kamalika Chaudhuri

Zechun Liu

Xilun Chen

Quentin Garrido

Karen Ullrich

Kate Saenko

Asli Celikyilmaz

Vikas Chandra

Following the recent popularity of Large Language Models (LLMs), several attempts have been made to extend them to the visual domain. From h… (voir plus)aving a visual assistant that could guide us through unfamiliar environments to generative models that produce images using only a high-level text description, the vision-language model (VLM) applications will significantly impact our relationship with technology. However, there are many challenges that need to be addressed to improve the reliability of those models. While language is discrete, vision evolves in a much higher dimensional space in which concepts cannot always be easily discretized. To better understand the mechanics behind mapping vision to language, we present this introduction to VLMs which we hope will help anyone who would like to enter the field. First, we introduce what VLMs are, how they work, and how to train them. Then, we present and discuss approaches to evaluate VLMs. Although this work primarily focuses on mapping images to language, we also discuss extending VLMs to videos.

2024-05-27

ArXiv (prépublication)

An Introduction to Vision-Language Modeling

Richard Yuanzhe Pang

Anurag Ajay

Alexander C. Li

Adrien Bardes

Suzanne Petryk

Zhiqiu Lin

Bargav Jayaraman

Mark Ibrahim

Melissa Hall

Yunyang Xiong

Candace Ross

Srihari Jayakumar

Chuan Guo

Diane Bouchacourt

Haider Al-Tahan

Karthik Padthe … (voir 21 de plus)

Vasu Sharma

Huijuan Xu 0001

Xiaoqing Ellen Tan

Megan Richards

Samuel Lavoie

Pietro Astolfi

Jun Chen

Kushal Tirumala

Mazda Moayeri

Arjang Talattof

Kamalika Chaudhuri

Zechun Liu

Xilun Chen

Quentin Garrido

Karen Ullrich

Kate Saenko

Asli Celikyilmaz

Vikas Chandra

Following the recent popularity of Large Language Models (LLMs), several attempts have been made to extend them to the visual domain. From h… (voir plus)aving a visual assistant that could guide us through unfamiliar environments to generative models that produce images using only a high-level text description, the vision-language model (VLM) applications will significantly impact our relationship with technology. However, there are many challenges that need to be addressed to improve the reliability of those models. While language is discrete, vision evolves in a much higher dimensional space in which concepts cannot always be easily discretized. To better understand the mechanics behind mapping vision to language, we present this introduction to VLMs which we hope will help anyone who would like to enter the field. First, we introduce what VLMs are, how they work, and how to train them. Then, we present and discuss approaches to evaluate VLMs. Although this work primarily focuses on mapping images to language, we also discuss extending VLMs to videos.

2024-05-27

ArXiv (prépublication)

On the Privacy of Selection Mechanisms with Gaussian Noise

Doina Precup

Borja Balle

Report Noisy Max and Above Threshold are two classical differentially private (DP) selection mechanisms. Their output is obtained by adding … (voir plus)noise to a sequence of low-sensitivity queries and reporting the identity of the query whose (noisy) answer satisfies a certain condition. Pure DP guarantees for these mechanisms are easy to obtain when Laplace noise is added to the queries. On the other hand, when instantiated using Gaussian noise, standard analyses only yield approximate DP guarantees despite the fact that the outputs of these mechanisms lie in a discrete space. In this work, we revisit the analysis of Report Noisy Max and Above Threshold with Gaussian noise and show that, under the additional assumption that the underlying queries are bounded, it is possible to provide pure ex-ante DP bounds for Report Noisy Max and pure ex-post DP bounds for Above Threshold. The resulting bounds are tight and depend on closed-form expressions that can be numerically evaluated using standard methods. Empirically we find these lead to tighter privacy accounting in the high privacy, low data regime. Further, we propose a simple privacy filter for composing pure ex-post DP guarantees, and use it to derive a fully adaptive Gaussian Sparse Vector Technique mechanism. Finally, we provide experiments on mobility and energy consumption datasets demonstrating that our Sparse Vector Technique is practically competitive with previous approaches and requires less hyper-parameter tuning.

2024-04-18

Proceedings of The 27th International Conference on Artificial Intelligence and Statistics (publié)

DP-RDM: Adapting Diffusion Models to Private Domains Without Fine-Tuning

Maziar Sanjabi

Pietro Astolfi

Adriana Romero Soriano

Kamalika Chaudhuri

Michael Rabbat

Chuan Guo

2024-03-21

ArXiv (prépublication)

DP-RDM: Adapting Diffusion Models to Private Domains Without Fine-Tuning

Maziar Sanjabi

Pietro Astolfi

Adriana Romero Soriano

Kamalika Chaudhuri

Michael Rabbat

Chuan Guo

Text-to-image diffusion models have been shown to suffer from sample-level memorization, possibly reproducing near-perfect replica of images… (voir plus) that they are trained on, which may be undesirable. To remedy this issue, we develop the first differentially private (DP) retrieval-augmented generation algorithm that is capable of generating high-quality image samples while providing provable privacy guarantees. Specifically, we assume access to a text-to-image diffusion model trained on a small amount of public data, and design a DP retrieval mechanism to augment the text prompt with samples retrieved from a private retrieval dataset. Our \emph{differentially private retrieval-augmented diffusion model} (DP-RDM) requires no fine-tuning on the retrieval dataset to adapt to another domain, and can use state-of-the-art generative models to generate high-quality image samples while satisfying rigorous DP guarantees. For instance, when evaluated on MS-COCO, our DP-RDM can generate samples with a privacy budget of

2024-03-21

ArXiv (prépublication)