11 citations · 33 across the 9 of their papers we have counts for
10 papers
Laguna M.1/XS.2 Technical Report
Julien Abadji, Marah Abdin, Connor Adams +93
We present Laguna M.1 and Laguna XS.2, two Mixture-of-Experts foundation models built for long-horizon, agentic coding: M.1 has B total parameters (B activated per tok…
A Different Approach to AI Safety: Proceedings from the Columbia Convening on Openness in Artificial Intelligence and AI Safety
Camille François, Ludovic Péran, Ayah Bdeir +17
The rapid rise of open-weight and open-source foundation models is intensifying the obligation and reshaping the opportunity to make AI systems safe. This paper reports outcomes fr…
Towards a Framework for Openness in Foundation Models: Proceedings from the Columbia Convening on Openness in Artificial Intelligence
Adrien Basdevant, Camille François, Victor Storchan +13
Over the past year, there has been a robust debate about the benefits and risks of open sourcing foundation models. However, this discussion has often taken place at a high level o…
On the Societal Impact of Open Foundation Models
Sayash Kapoor, Rishi Bommasani, Kevin Klyman +22
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, defin…
Transductive Learning for Textual Few-Shot Classification in API-based Embedding Models
Pierre Colombo, Victor Pellegrain, Malik Boudiaf +5
Proprietary and closed APIs are becoming increasingly common to process natural language, and are impacting the practical applications of natural language processing, including few…
On the Current and Emerging Challenges of Developing Fair and Ethical AI Solutions in Financial Services
Eren Kurshan, Jiahao Chen, Victor Storchan +1
Artificial intelligence (AI) continues to find more numerous and more critical applications in the financial services industry, giving rise to fair and ethical AI as an industry-wi…
Beyond Fairness Metrics: Roadblocks and Challenges for Ethical AI in Practice
Jiahao Chen, Victor Storchan, Eren Kurshan
We review practical challenges in building and deploying ethical AI at the scale of contemporary industrial and societal uses. Apart from the purely technical concerns that are the…
Seven challenges for harmonizing explainability requirements
Jiahao Chen, Victor Storchan
Regulators have signalled an interest in adopting explainable AI(XAI) techniques to handle the diverse needs for model governance, operational servicing, and compliance in the fina…
Learning who is in the market from time series: market participant discovery through adversarial calibration of multi-agent simulators
Victor Storchan, Svitlana Vyetrenko, Tucker Balch
In electronic trading markets often only the price or volume time series, that result from interaction of multiple market participants, are directly observable. In order to test tr…
Explainable Clustering and Application to Wealth Management Compliance
Enguerrand Horel, Kay Giesecke, Victor Storchan +1
Many applications from the financial industry successfully leverage clustering algorithms to reveal meaningful patterns among a vast amount of unstructured financial data. However,…