Publications

Global AI Cultures
Rida Qadri
Fernando Diaz
Arjun Subramonian
Sunipa Dev
Georgina Emma Born
Mary L. Gray
Jessica Quaye
Rachel Bergmann
2024-03-08
ICLR.cc/2024/Workshop_Proposals (published)
openreview.net
openreview.net
Integrating Generative and Experimental Platforms or Biomolecular Design
Cheng-Hao Liu
Jarrid Rector-Brooks
Jason Yim
Soojung Yang
Sidney Lisanza
Francesca-Zhoufan Li
Pranam Chatterjee
Tommi Jaakkola
Regina Barzilay
David Baker
Frances H. Arnold
Yoshua Bengio
2024-03-08
ICLR.cc/2024/Workshop_Proposals (published)
openreview.net
openreview.net
Tackling Climate Change with Machine Learning: Fostering the Maturity of ML Applications for Climate Change
Shiva Madadkhani
Olivia Mendivil Ramos
Millie Chapman
Jesse Dunietz
Arthur Ouaknine
David Rolnick
Yoshua Bengio
2024-03-08
ICLR.cc/2024/Workshop_Proposals (published)
openreview.net
openreview.net
Globally Stable Neural Imitation Policies
Amin Abyaneh
Mariana Sosa Guzm'an
Hsiu-Chin Lin
2024-03-07
ArXiv (preprint)
doi.org
arxiv.org
Machine learning and information theory concepts towards an AI Mathematician
Yoshua Bengio
Nikolay Malkin
The current state-of-the-art in artificial intelligence is impressive, especially in terms of mastery of language, but not so much in terms … (see more)of mathematical reasoning. What could be missing? Can we learn something useful about that gap from how the brains of mathematicians go about their craft? This essay builds on the idea that current deep learning mostly succeeds at system 1 abilities -- which correspond to our intuition and habitual behaviors -- but still lacks something important regarding system 2 abilities -- which include reasoning and robust uncertainty estimation. It takes an information-theoretical posture to ask questions about what constitutes an interesting mathematical statement, which could guide future work in crafting an AI mathematician. The focus is not on proving a given theorem but on discovering new and interesting conjectures. The central hypothesis is that a desirable body of theorems better summarizes the set of all provable statements, for example by having a small description length while at the same time being close (in terms of number of derivation steps) to many provable statements.
2024-03-07
ArXiv (preprint)
doi.org
arxiv.org
Personalized Negative Reservoir for Incremental Learning in Recommender Systems
Antonios Valkanas
Yuening Wang
Yingxue Zhang
Mark Coates
2024-03-06
ArXiv (preprint)
doi.org
arxiv.org
Stop Regressing: Training Value Functions via Classification for Scalable Deep RL
Jesse Farebrother
Jordi Orbay
Quan Ho Vuong
Adrien Ali Taiga
Yevgen Chebotar
Ted Xiao
A. Irpan
Sergey Levine
Pablo Samuel Castro
Aleksandra Faust
Aviral Kumar
Rishabh Agarwal
Value functions are a central component of deep reinforcement learning (RL). These functions, parameterized by neural networks, are trained … (see more)using a mean squared error regression objective to match bootstrapped target values. However, scaling value-based RL methods that use regression to large networks, such as high-capacity Transformers, has proven challenging. This difficulty is in stark contrast to supervised learning: by leveraging a cross-entropy classification loss, supervised methods have scaled reliably to massive networks. Observing this discrepancy, in this paper, we investigate whether the scalability of deep RL can also be improved simply by using classification in place of regression for training value functions. We demonstrate that value functions trained with categorical cross-entropy significantly improves performance and scalability in a variety of domains. These include: single-task RL on Atari 2600 games with SoftMoEs, multi-task RL on Atari with large-scale ResNets, robotic manipulation with Q-transformers, playing Chess without search, and a language-agent Wordle task with high-capacity Transformers, achieving state-of-the-art results on these domains. Through careful analysis, we show that the benefits of categorical cross-entropy primarily stem from its ability to mitigate issues inherent to value-based RL, such as noisy targets and non-stationarity. Overall, we argue that a simple shift to training value functions with categorical cross-entropy can yield substantial improvements in the scalability of deep RL at little-to-no cost.
2024-03-06
ArXiv (preprint)
doi.org
arxiv.org
Efficient Causal Graph Discovery Using Large Language Models
Thomas Jiralerspong
Xiaoyin Chen
Yash More
Vedant Shah
Yoshua Bengio
2024-03-05
ICLR.cc/2024/Workshop/AGI (poster)
doi.org
openreview.net
Mirror Descent Algorithms with Nearly Dimension-Independent Rates for Differentially-Private Stochastic Saddle-Point Problems
Tom'as Gonz'alez
Crist'obal Guzm'an
Courtney Paquette
2024-03-05
ArXiv (preprint)
doi.org
arxiv.org
Optimisation of quantitative brain diffusion-relaxation MRI acquisition protocols with physics-informed machine learning.
Álvaro Planchuelo-Gómez
Maxime Descoteaux
Hugo Larochelle
Jana Hutter
Derek K. Jones
C. Tax
2024-03-05
Medical Image Analysis (published)
doi.org
Plant invasion in Mediterranean Europe: current hotspots and future scenarios
Luigi Cao Pinna
Laure Gallien
Laura J. Pollock
Irena Axmanová
Milan Chytrý
Marco Malavasi
Alicia T. R. Acosta
Juan Antonio Campos
Marta Carboni
The Mediterranean Basin has historically been subject to alien plant invasions that threaten its unique biodiversity. This seasonally dry an… (see more)d densely populated region is undergoing severe climatic and socioeconomic changes, and it is unclear whether these changes will worsen or mitigate plant invasions. Predictions are often biased, as species may not be in equilibrium in the invaded environment, depending on their invasion stage and ecological characteristics. To address future predictions uncertainty, we identified invasion hotspots across multiple biased modelling scenarios and ecological characteristics of successful invaders. We selected 92 alien plant species widespread in Mediterranean Europe and compiled data on their distribution in the Mediterranean and worldwide. We combined these data with environmental and propagule pressure variables to model global and regional species niches, and map their current and future habitat suitability. We identified invasion hotspots, examined their potential future shifts, and compared the results of different modelling strategies. Finally, we generalised our findings by using linear models to determine the traits and biogeographic features of invaders most likely to benefit from global change. Currently, invasion hotspots are found near ports and coastlines throughout Mediterranean Europe. However, many species occupy only a small portion of the environmental conditions to which they are preadapted, suggesting that their invasion is still an ongoing process. Future conditions will lead to declines in many currently widespread aliens, which will tend to move to higher elevations and latitudes. Our trait models indicate that future climates will generally favour species with conservative ecological strategies that can cope with reduced water availability, such as those with short stature and low specific leaf area. Taken together, our results suggest that in future environments, these conservative aliens will move farther from the introduction areas and upslope, threatening mountain ecosystems that have been spared from invasions so far.
2024-03-05
Ecography (published)
doi.org
The Case for Globalizing Fairness: A Mixed Methods Study on Colonialism, AI, and Health in Africa
Mercy Nyamewaa Asiedu
Awa Dieng
Alexander Haykel
Negar Rostamzadeh
Stephen R. Pfohl
Chirag Nagpal
Maria Nagawa
Abigail Oppong
Sanmi Koyejo
Katherine Heller
With growing application of machine learning (ML) technologies in healthcare, there have been calls for developing techniques to understand … (see more)and mitigate biases these systems may exhibit. Fair-ness considerations in the development of ML-based solutions for health have particular implications for Africa, which already faces inequitable power imbalances between the Global North and South.This paper seeks to explore fairness for global health, with Africa as a case study. We conduct a scoping review to propose axes of disparities for fairness consideration in the African context and delineate where they may come into play in different ML-enabled medical modalities. We then conduct qualitative research studies with 672 general population study participants and 28 experts inML, health, and policy focused on Africa to obtain corroborative evidence on the proposed axes of disparities. Our analysis focuses on colonialism as the attribute of interest and examines the interplay between artificial intelligence (AI), health, and colonialism. Among the pre-identified attributes, we found that colonial history, country of origin, and national income level were specific axes of disparities that participants believed would cause an AI system to be biased.However, there was also divergence of opinion between experts and general population participants. Whereas experts generally expressed a shared view about the relevance of colonial history for the development and implementation of AI technologies in Africa, the majority of the general population participants surveyed did not think there was a direct link between AI and colonialism. Based on these findings, we provide practical recommendations for developing fairness-aware ML solutions for health in Africa.
2024-03-05
ArXiv (preprint)
doi.org
arxiv.org