Publications

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 … (voir plus)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 (prépublication)
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
Explicit Knowledge Factorization Meets In-Context Learning: What Do We Gain?
Sarthak Mittal
Eric Elmoznino
Leo Gagnon
Sangnie Bhardwaj
Dhanya Sridhar
Guillaume Lajoie
2024-03-05
ICLR.cc/2024/Workshop/R2-FM (poster)
openreview.net
openreview.net
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 (publié)
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… (voir plus)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 (publié)
doi.org
Smoothness-Adaptive Sharpness-Aware Minimization for Finding Flatter Minima
Hiroki Naganuma
Junhyung Lyle Kim
Anastasios Kyrillidis
Ioannis Mitliagkas
The sharpness-aware minimization (SAM) procedure recently gained increasing attention due to its favorable generalization ability to unseen … (voir plus)data. SAM aims to find flatter (local) minima, utilizing a minimax objective. An immediate challenge in the application of SAM is the adjustment of two pivotal step sizes, which significantly influence its effectiveness. We introduce a novel, straightforward approach for adjusting step sizes that adapts to the smoothness of the objective function, thereby reducing the necessity for manual tuning. This method, termed Smoothness-Adaptive SAM (SA-SAM), not only simplifies the optimization process but also promotes the method's inherent tendency to converge towards flatter minima, enhancing performance in specific models.
2024-03-05
ICLR.cc/2024/Workshop/PML4LRS (poster)
openreview.net
openreview.net
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 … (voir plus)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 (prépublication)
doi.org
arxiv.org
The World Health Organization as an engine of ideational robustness
Jean-Louis Denis
Gaelle Foucault
Pierre Larouche
Catherine Régis
Miriam Cohen
Marie-Andree Girard
2024-03-05
Policy and Society (publié)
doi.org
F$^3$low: Frame-to-Frame Coarse-grained Molecular Dynamics with SE(3) Guided Flow Matching
Shaoning Li
Yusong Wang
Mingyu Li
Bin Shao
Nanning Zheng
Zhang Jian
Jian Tang
2024-03-04
ICLR.cc/2024/Workshop/GEM (poster)
openreview.net
openreview.net
Enhancing and Evaluating Logical Reasoning Abilities of Large Language Models
Shujie Deng
Honghua Dong
Xujie Si
2024-03-04
ICLR.cc/2024/Workshop/SeT_LLM (publié)
openreview.net
openreview.net
Fusing Neural and Physical: Augment Protein Conformation Sampling with Tractable Simulations
Jiarui Lu
Zuobai Zhang
Bozitao Zhong
Chence Shi
Jian Tang
The protein dynamics are common and important for their biological functions and properties, the study of which usually involves time-consum… (voir plus)ing molecular dynamics (MD) simulations *in silico*. Recently, generative models has been leveraged as a surrogate sampler to obtain conformation ensembles with orders of magnitude faster and without requiring any simulation data (a "zero-shot" inference). However, being agnostic of the underlying energy landscape, the accuracy of such generative model may still be limited. In this work, we explore the few-shot setting of such pre-trained generative sampler which incorporates MD simulations in a tractable manner. Specifically, given a target protein of interest, we first acquire some seeding conformations from the pre-trained sampler followed by a number of physical simulations in parallel starting from these seeding samples. Then we fine-tuned the generative model using the simulation trajectories above to become a target-specific sampler. Experimental results demonstrated the superior performance of such few-shot conformation sampler at a tractable computational cost.
2024-03-04
ICLR.cc/2024/Workshop/GEM (poster)
openreview.net
openreview.net
A Generative Model of Symmetry Transformations
James U. Allingham
Bruno Mlodozeniec
Shreyas Padhy
Javier Antor'an
David Scott Krueger
Richard E. Turner
Eric T. Nalisnick
Jos'e Miguel Hern'andez-Lobato
Correctly capturing the symmetry transformations of data can lead to efficient models with strong generalization capabilities, though method… (voir plus)s incorporating symmetries often require prior knowledge. While recent advancements have been made in learning those symmetries directly from the dataset, most of this work has focused on the discriminative setting. In this paper, we construct a generative model that explicitly aims to capture symmetries in the data, resulting in a model that learns which symmetries are present in an interpretable way. We provide a simple algorithm for efficiently learning our generative model and demonstrate its ability to capture symmetries under affine and color transformations. Combining our symmetry model with existing generative models results in higher marginal test-log-likelihoods and robustness to data sparsification.
2024-03-04
ArXiv (prépublication)
doi.org
arxiv.org