Joëlle Pineau

Tiny Aya: Bridging Scale and Multilingual Depth

Alejandro Salamanca, Diana Abagyan, Daniel D'souza, Ammar Khairi et autres

Tiny Aya redefines what a small multilingual language model can achieve. Trained on 70 languages and refined through region-aware posttraining, it delivers state-of-the-art in translation quality, strong multilingual understanding, and high-quality target-language generation, all with just 3.35B parameters. The release includes a …

Tiny Aya: Bridging Scale and Multilingual Depth

Alejandro Salamanca, Diana Abagyan, Daniel D'souza, Ammar Khairi et autres

Tiny Aya redefines what a small multilingual language model can achieve. Trained on 70 languages and refined through region-aware posttraining, it delivers state-of-the-art in translation quality, strong multilingual understanding, and high-quality target-language generation, all with just 3.35B parameters. The release includes a …

us, nl (code pays fourni par la source)

Advancing science- and evidence-based AI policy

Rishi Bommasani, Sanjeev Arora, Jennifer Chayes, Yejin Choi et autres

Policy must be informed by, but also facilitate the generation of, scientific evidence.

us, ca, gb, fr (code pays fourni par la source)

Safe Domain Randomization via Uncertainty-Aware Out-of-Distribution Detection and Policy Adaptation

Mohamad H. Danesh, Maxime Wabartha, Stanley Wu, Joëlle Pineau et autres

Deploying reinforcement learning (RL) policies in real-world involves significant challenges, including distribution shifts, safety concerns, and the impracticality of direct interactions during policy refinement. Existing methods, such as domain randomization (DR) and off-dynamics RL, enhance policy robustness by direct interaction with the …

A flexible machine learning Mendelian randomization estimator applied to predict the safety and efficacy of sclerostin inhibition

Marc‐André Legault, Jason Hartford, Benoît J. Arsenault, Archer Y. Yang et autres

ca, us (code pays fourni par la source)

When AIs outperform doctors: confronting the challenges of a tort-induced over-reliance on machine learning

A. Michael Froomkin, Ian Kerr, Joëlle Pineau

Someday, perhaps soon, diagnostics generated by machine learning (ML) will have demonstrably better success rates than those generated by human doctors. What will the dominance of ML diagnostics mean for medical malpractice law, for the future of medical service provision, for the …

us, ca (code pays fourni par la source)

On the Societal Impact of Open Foundation Models

Sayash Kapoor, Rishi Bommasani, Kevin Klyman, Shayne Longpre et autres

Foundation models are powerful technologies: how they are released publicly directly shapes their societal impact. In this position paper, we focus on open foundation models, defined here as those with broadly available model weights (e.g. Llama 2, Stable Diffusion XL). We identify …

Rethinking Machine Learning Benchmarks in the Context of Professional Codes of Conduct

Peter Henderson, Jieru Hu, Mona Diab, Joëlle Pineau

Benchmarking efforts for machine learning have often mimicked (or even explicitly used) professional licensing exams to assess capabilities in a given area, focusing primarily on accuracy as the metric of choice. However, this approach neglects a variety of essential skills required in …

us, ca (code pays fourni par la source)

A novel and efficient machine learning Mendelian randomization estimator applied to predict the safety and efficacy of sclerostin inhibition

Marc‐André Legault, Jason Hartford, Benoît J. Arsenault, Archer Y. Yang et autres

1 Abstract Mendelian Randomization (MR) enables estimation of causal effects while controlling for unmeasured confounding factors. However, traditional MR’s reliance on strong parametric assumptions can introduce bias if these are violated. We introduce a new machine learning MR estimator named Quantile Instrumental …

ca (code pays fourni par la source)

Position: On the Societal Impact of Open Foundation Models

Sayash Kapoor, Rishi Bommasani, Kevin Klyman, Shayne Longpre et autres

Foundation models are powerful technologies: how they are released publicly directly shapes their societal impact. In this position paper, we focus on open foundation models, defined here as those with broadly available model weights (e.g. Llama 3, Stable Diffusion XL). We identify …

ML Reproducibility Challenge 2022

Koustuv Sinha, Maurits Bleeker, Samarth Bhargav, Jessica Zosa Forde et autres

Editorial

Circulating proteins to predict COVID-19 severity

Chen‐Yang Su, Sirui Zhou, Edgar Gonzalez‐Kozlova, Guillaume Butler‐Laporte et autres

Predicting COVID-19 severity is difficult, and the biological pathways involved are not fully understood. To approach this problem, we measured 4701 circulating human protein abundances in two independent cohorts totaling 986 individuals. We then trained prediction models including protein abundances and clinical …

ca, us, jp, gb, de, ch (code pays fourni par la source)