Publications

Properties from Mechanisms: An Equivariance Perspective on Identifiable Representation Learning

Jason Hartford

A key goal of unsupervised representation learning is "inverting" a data generating process to recover its latent properties. Existing work … (voir plus)that provably achieves this goal relies on strong assumptions on relationships between the latent variables (e.g., independence conditional on auxiliary information). In this paper, we take a very different perspective on the problem and ask, "Can we instead identify latent properties by leveraging knowledge of the mechanisms that govern their evolution?" We provide a complete characterization of the sources of non-identifiability as we vary knowledge about a set of possible mechanisms. In particular, we prove that if we know the exact mechanisms under which the latent properties evolve, then identification can be achieved up to any equivariances that are shared by the underlying mechanisms. We generalize this characterization to settings where we only know some hypothesis class over possible mechanisms, as well as settings where the mechanisms are stochastic. We demonstrate the power of this mechanism-based perspective by showing that we can leverage our results to generalize existing identifiable representation learning results. These results suggest that by exploiting inductive biases on mechanisms, it is possible to design a range of new identifiable representation learning approaches.

2022-04-24

International Conference on Learning Representations (Accept (Spotlight))

doi.org

openreview.net

QEN: Applicable Taxonomy Completion via Evaluating Full Taxonomic Relations

Suyuchen Wang

Ruihui Zhao

Yefeng Zheng

Bang Liu

Taxonomy is a fundamental type of knowledge graph for a wide range of web applications like searching and recommendation systems. To keep a … (voir plus)taxonomy automatically updated with the latest concepts, the taxonomy completion task matches a pair of proper hypernym and hyponym in the original taxonomy with the new concept as its parent and child. Previous solutions utilize term embeddings as input and only evaluate the parent-child relations between the new concept and the hypernym-hyponym pair. Such methods ignore the important sibling relations, and are not applicable in reality since term embeddings are not available for the latest concepts. They also suffer from the relational noise of the “pseudo-leaf” node, which is a null node acting as a node’s hyponym to enable the new concept to be a leaf node. To tackle the above drawbacks, we propose the Quadruple Evaluation Network (QEN), a novel taxonomy completion framework that utilizes easily accessible term descriptions as input, and applies pretrained language model and code attention for accurate inference while reducing online computation. QEN evaluates both parent-child and sibling relations to both enhance the accuracy and reduce the noise brought by pseudo-leaf. Extensive experiments on three real-world datasets in different domains with different sizes and term description sources prove the effectiveness and robustness of QEN on overall performance and especially the performance for adding non-leaf nodes, which largely surpasses previous methods and achieves the new state-of-the-art of the task.1

2022-04-24

The Web Conference (publié)

doi.org

Rare CNVs and phenome-wide profiling: a tale of brain-structural divergence and phenotypical convergence

J. Kopal

K. Kumar

K. Saltoun

C. Modenato

C. A. Moreau

S. Martin-Brevet

G. Huguet

M. Jean-Louis

C.O. Martin

Z. Saci

N. Younis

P. Tamer

E. Douard

A. M. Maillard

B. Rodriguez-Herreros

A. Pain

S. Richetin

L. Kushan

A. I. Silva

M. B. M. van den Bree … (voir 12 de plus)

D. E. J. Linden

M. J. Owen

J. Hall

S. Lippé

B. Draganski

I. E. Sønderby

O. A. Andreassen

D. C. Glahn

P. M. Thompson

C. E. Bearden

S. Jacquemont

D. Bzdok

Copy number variations (CNVs) are rare genomic deletions and duplications that can exert profound effects on brain and behavior. Previous re… (voir plus)ports of pleiotropy in CNVs imply that they converge on shared mechanisms at some level of pathway cascades, from genes to large-scale neural circuits to the phenome. However, studies to date have primarily examined single CNV loci in small clinical cohorts. It remains unknown how distinct CNVs escalate the risk for the same developmental and psychiatric disorders. Here, we quantitatively dissect the impact on brain organization and behavioral differentiation across eight key CNVs. In 534 clinical CNV carriers from multiple sites, we explored CNV-specific brain morphology patterns. We extensively annotated these CNV-associated patterns with deep phenotyping assays through the UK Biobank resource. Although the eight CNVs cause disparate brain changes, they are tied to similar phenotypic profiles across ∼1000 lifestyle indicators. Our population-level investigation established brain structural divergences and phenotypical convergences of CNVs, with direct relevance to major brain disorders.

2022-04-24

bioRxiv (prépublication)

doi.org

Sample Efficient Deep Reinforcement Learning via Uncertainty Estimation

Vincent Mai

Kaustubh Mani

Liam Paull

In model-free deep reinforcement learning (RL) algorithms, using noisy value estimates to supervise policy evaluation and optimization is de… (voir plus)trimental to the sample efficiency. As this noise is heteroscedastic, its effects can be mitigated using uncertainty-based weights in the optimization process. Previous methods rely on sampled ensembles, which do not capture all aspects of uncertainty. We provide a systematic analysis of the sources of uncertainty in the noisy supervision that occurs in RL, and introduce inverse-variance RL, a Bayesian framework which combines probabilistic ensembles and Batch Inverse Variance weighting. We propose a method whereby two complementary uncertainty estimation methods account for both the Q-value and the environment stochasticity to better mitigate the negative impacts of noisy supervision. Our results show significant improvement in terms of sample efficiency on discrete and continuous control tasks.

2022-04-24

International Conference on Learning Representations (Accept (Spotlight))

doi.org

openreview.net

Shared and unique brain network features predict cognitive, personality, and mental health scores in the ABCD study

Jianzhong Chen

Angela Tam

Valeria Kebets

Csaba Orbán

Leon Qi Rong Ooi

Christopher L. Asplund

Scott Marek

Nico U.F. Dosenbach

Simon B. Eickhoff

Danilo Bzdok

Avram J. Holmes

B.T. Thomas Yeo

How individual differences in brain network organization track behavioral variability is a fundamental question in systems neuroscience. Rec… (voir plus)ent work suggests that resting-state and task-state functional connectivity can predict specific traits at the individual level. However, most studies focus on single behavioral traits, thus not capturing broader relationships across behaviors. In a large sample of 1858 typically developing children from the Adolescent Brain Cognitive Development (ABCD) study, we show that predictive network features are distinct across the domains of cognitive performance, personality scores and mental health assessments. On the other hand, traits within each behavioral domain are predicted by similar network features. Predictive network features and models generalize to other behavioral measures within the same behavioral domain. Although tasks are known to modulate the functional connectome, predictive network features are similar between resting and task states. Overall, our findings reveal shared brain network features that account for individual variation within broad domains of behavior in childhood.

2022-04-24

Nature Communications (publié)

doi.org

Staged independent learning: Towards decentralized cooperative multi-agent Reinforcement Learning

Hadi Nekoei

Akilesh Badrinaaraayanan

Amit Sinha

Mohammad Amini

Janarthanan Rajendran

Aditya Mahajan

A. Chandar

We empirically show that classic ideas from two-time scale stochastic approximation \citep{borkar1997stochastic} can be combined with sequen… (voir plus)tial iterative best response (SIBR) to solve complex cooperative multi-agent reinforcement learning (MARL) problems. We first start with giving a multi-agent estimation problem as a motivating example where SIBR converges while parallel iterative best response (PIBR) does not. Then we present a general implementation of staged multi-agent RL algorithms based on SIBR and multi-time scale stochastic approximation, and show that our new methods which we call Staged Independent Proximal Policy Optimization (SIPPO) and Staged Independent Q-learning (SIQL) outperform state-of-the-art independent learning on almost all the tasks in the epymarl \citep{papoudakis2020benchmarking} benchmark. This can be seen as a first step towards more decentralized MARL methods based on SIBR and multi-time scale learning.

2022-04-24

ICLR.cc/2022/Workshop/GMS (accepté)

openreview.net

RetroGNN: Fast Estimation of Synthesizability for Virtual Screening and De Novo Design by Learning from Slow Retrosynthesis Software

Cheng-Hao Liu

Maksym Korablyov

Stanisław Jastrzębski

Paweł Włodarczyk-Pruszyński

Yoshua Bengio

Marwin Segler

2022-04-21

Journal of Chemical Information and Modeling (publié)

doi.org

Learning how to Interact with a Complex Interface using Hierarchical Reinforcement Learning

Gheorghe Comanici

Amelia Glaese

Anita Gergely

Daniel Toyama

Zafarali Ahmed

Tyler Jackson

Philippe Hamel

Doina Precup

Hierarchical Reinforcement Learning (HRL) allows interactive agents to decompose complex problems into a hierarchy of sub-tasks. Higher-leve… (voir plus)l tasks can invoke the solutions of lower-level tasks as if they were primitive actions. In this work, we study the utility of hierarchical decompositions for learning an appropriate way to interact with a complex interface. Specifically, we train HRL agents that can interface with applications in a simulated Android device. We introduce a Hierarchical Distributed Deep Reinforcement Learning architecture that learns (1) subtasks corresponding to simple finger gestures, and (2) how to combine these gestures to solve several Android tasks. Our approach relies on goal conditioning and can be used more generally to convert any base RL agent into an HRL agent. We use the AndroidEnv environment to evaluate our approach. For the experiments, the HRL agent uses a distributed version of the popular DQN algorithm to train different components of the hierarchy. While the native action space is completely intractable for simple DQN agents, our architecture can be used to establish an effective way to interact with different tasks, significantly improving the performance of the same DQN agent over different levels of abstraction.

2022-04-20

ArXiv (prépublication)

doi.org

arxiv.org

Local Learning with Neuron Groups

Adeetya Patel

Michael Eickenberg

Eugene Belilovsky

2022-04-20

ICLR.cc/2022/Workshop/Cells2Societies (poster)

doi.org

openreview.net

Summarizing Societies: Agent Abstraction in Multi-Agent Reinforcement Learning

Maximilian Puelma Touzel

Matthew D Riemer

Rupali Bhati

Irina Rish

Agents cannot make sense of many-agent societies through direct consideration of small-scale, low-level agent identities, but instead must r… (voir plus)ecognize emergent collective identities. Here, we take a first step towards a framework for recognizing this structure in large groups of low-level agents so that they can be modeled as a much smaller number of high-level agents—a process that we call agent abstraction. We illustrate this process by extending bisimulation metrics for state abstraction in reinforcement learning to the setting of multi-agent reinforcement learning and analyze a straightforward, if crude, abstraction based on experienced joint actions. It addresses non-stationarity due to other learning agents by improving minimax regret by a intuitive factor. To test if this compression factor provides signal for higher-level agency, we applied it to a large dataset of human play of the popular social dilemma game Diplomacy. We find that it correlates strongly with the degree of ground-truth abstraction of low-level units into the human players.

2022-04-20

ICLR.cc/2022/Workshop/Cells2Societies (poster)

openreview.net

An interactive meta-analysis of MRI biomarkers of myelin

Matteo Mancini

Agah Karakuzu

Julien Cohen-Adad

Mara Cercignani

Thomas E Nichols

Nikola Stikov

Several MRI measures have been proposed as in vivo biomarkers of myelin, each with applications ranging from plasticity to pathology. Despit… (voir plus)e the availability of these myelin-sensitive modalities, specificity and sensitivity have been a matter of discussion. Debate about which MRI measure is the most suitable for quantifying myelin is still ongoing. In this study, we performed a systematic review of published quantitative validation studies to clarify how different these measures are when compared to the underlying histology. We analyzed the results from 43 studies applying meta-analysis tools, controlling for study sample size and using interactive visualization ( https://neurolibre.github.io/myelin-meta-analysis ). We report the overall estimates and the prediction intervals for the coefficient of determination and find that MT and relaxometry-based measures exhibit the highest correlations with myelin content. We also show which measures are, and which measures are not statistically different regarding their relationship with histology.

2022-04-19

NeuroLibre Reproducible Preprint Server (publié)

doi.org

A Strong Node Classification Baseline for Temporal Graphs

Farimah Poursafaei

Željko Žilić

Reihaneh Rabbany

2022-04-19

Proceedings of the 2022 SIAM International Conference on Data Mining (SDM) (publié)

doi.org