Jonathan Lebensold

Sean Hughes

Massimo Caccia … (voir 6 de plus)

Alexandre Drouin

Siva Reddy

Tao Yu

Yu Su

Graham Neubig

Dawn Song

The proliferation of agent benchmarks has created critical fragmentation that threatens research productivity. Each new benchmark requires s… (voir plus)ubstantial custom integration, creating an "integration tax" that limits comprehensive evaluation. We propose CUBE (Common Unified Benchmark Environments), a universal protocol standard built on MCP and Gym that allows benchmarks to be wrapped once and used everywhere. By separating task, benchmark, package, and registry concerns into distinct API layers, CUBE enables any compliant platform to access any compliant benchmark for evaluation, RL training, or data generation without custom integration. We call on the community to contribute to the development of this standard before platform-specific implementations deepen fragmentation as benchmark production accelerates through 2026.

2026-03-15

arXiv (prépublication)

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

Nicolas Roux

Bellemare Marc-Emmanuel

Arnaud Bergeron

Joshua Greaves

Alex Fr'echette

Carolyne Pelletier

Éric Thibodeau-Laufer

S'andor Toth

Sam Work

2025-03-17

ArXiv (prépublication)

Mitigating Downstream Model Risks via Model Provenance

Keyu Wang

Abdullah Norozi Iranzad

Scott Schaffter

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.

2024-10-08

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

openreview.net

An Introduction to Vision-Language Modeling

Florian Bordes

Richard Yuanzhe Pang

Anurag Ajay

Alexander C. Li

Adrien Bardes

Suzanne Petryk

Oscar Mañas

Zhiqiu Lin

Anas Mahmoud

Bargav Jayaraman

Mark Ibrahim

Melissa Hall

Yunyang Xiong

Candace Ross

Srihari Jayakumar

Chuan Guo

Diane Bouchacourt

Haider Al-Tahan

Karthik Padthe … (voir 22 de plus)

Vasu Sharma

Huijuan Xu 0001

Hu Xu

Xiaoqing Ellen Tan

Megan Richards

Samuel Lavoie

Pietro Astolfi

Reyhane Askari Hemmat

Jun Chen

Kushal Tirumala

Rim Assouel

Mazda Moayeri

Arjang Talattof

Kamalika Chaudhuri

Zechun Liu

Xilun Chen

Quentin Garrido

Karen Ullrich

Aishwarya Agrawal

Kate Saenko

Asli Celikyilmaz

Vikas Chandra

2024-05-26

ArXiv (prépublication)

On the Privacy of Selection Mechanisms with Gaussian Noise

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.

2024-04-17

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

proceedings.mlr.press

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

Maziar Sanjabi

Pietro Astolfi

Adriana Romero

Kamalika Chaudhuri

Michael G. Rabbat

Chuan Guo

2024-03-20

ArXiv (prépublication)

Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims

Miles Brundage

Shahar Avin

Jasmine Wang

Haydn Belfield

Gretchen Krueger

Gillian Hadfield

Heidy Khlaaf

Jingying Yang

Helen Toner

Ruth Fong

Tegan Maharaj

Pang Wei Koh

Sara Hooker

Jade Leung

Andrew Trask

Emma Bluemke

Cullen O'Keefe

Mark Koren

Théo Ryffel … (voir 39 de plus)

JB Rubinovitz

Tamay Besiroglu

Federica Carugati

Jack Clark

Peter Eckersley

Sarah de Haas

Maritza Johnson

Ben Laurie

Alex Ingerman

Igor Krawczuk

Amanda Askell

Rosario Cammarota

Andrew Lohn

David Krueger

Charlotte Stix

Peter Henderson

Logan Graham

Carina Prunkl

Bianca Martin

Elizabeth Seger

Noa Zilberman

Seán Ó hÉigeartaigh

Frens Kroeger

Girish Sastry

Rebecca Kagan

Adrian Weller

Brian Tse

Elizabeth Barnes

Allan Dafoe

Paul Scharre

Ariel Herbert-Voss

Martijn Rasser

Shagun Sodhani

Carrick Flynn

Thomas Krendl Gilbert

Lisa Dyer

Saif Khan

Yoshua Bengio

Markus Anderljung

With the recent wave of progress in artificial intelligence (AI) has come a growing awareness of the large-scale impacts of AI systems, and … (voir plus)recognition that existing regulations and norms in industry and academia are insufficient to ensure responsible AI development. In order for AI developers to earn trust from system users, customers, civil society, governments, and other stakeholders that they are building AI responsibly, they will need to make verifiable claims to which they can be held accountable. Those outside of a given organization also need effective means of scrutinizing such claims. This report suggests various steps that different stakeholders can take to improve the verifiability of claims made about AI systems and their associated development processes, with a focus on providing evidence about the safety, security, fairness, and privacy protection of AI systems. We analyze ten mechanisms for this purpose--spanning institutions, software, and hardware--and make recommendations aimed at implementing, exploring, or improving those mechanisms.

2019-12-31

arXiv (prépublication)

Actor Critic with Differentially Private Critic

William Hamilton

Borja Balle

Reinforcement learning algorithms are known to be sample inefficient, and often performance on one task can be substantially improved by lev… (voir plus)eraging information (e.g., via pre-training) on other related tasks. In this work, we propose a technique to achieve such knowledge transfer in cases where agent trajectories contain sensitive or private information, such as in the healthcare domain. Our approach leverages a differentially private policy evaluation algorithm to initialize an actor-critic model and improve the effectiveness of learning in downstream tasks. We empirically show this technique increases sample efficiency in resource-constrained control problems while preserving the privacy of trajectories collected in an upstream task.

2019-10-13

ArXiv (prépublication)

Neural Transfer Learning for Cry-based Diagnosis of Perinatal Asphyxia

Charles C. Onu

William L. Hamilton

Despite continuing medical advances, the rate of newborn morbidity and mortality globally remains high, with over 6 million casualties every… (voir plus) year. The prediction of pathologies affecting newborns based on their cry is thus of significant clinical interest, as it would facilitate the development of accessible, low-cost diagnostic tools\cut{ based on wearables and smartphones}. However, the inadequacy of clinically annotated datasets of infant cries limits progress on this task. This study explores a neural transfer learning approach to developing accurate and robust models for identifying infants that have suffered from perinatal asphyxia. In particular, we explore the hypothesis that representations learned from adult speech could inform and improve performance of models developed on infant speech. Our experiments show that models based on such representation transfer are resilient to different types and degrees of noise, as well as to signal loss in time and frequency domains.

2019-09-12

Interspeech 2019 (publié)