Publications

Evaluating Self-Supervised Learning for Molecular Graph Embeddings

Hanchen Wang

Jean Kaddour

Matt J. Kusner

Joan Lasenby

Qi Liu

Graph Self-Supervised Learning (GSSL) provides a robust pathway for acquiring embeddings without expert labelling, a capability that carries… (see more) profound implications for molecular graphs due to the staggering number of potential molecules and the high cost of obtaining labels. However, GSSL methods are designed not for optimisation within a specific domain but rather for transferability across a variety of downstream tasks. This broad applicability complicates their evaluation. Addressing this challenge, we present"Molecular Graph Representation Evaluation"(MOLGRAPHEVAL), generating detailed profiles of molecular graph embeddings with interpretable and diversified attributes. MOLGRAPHEVAL offers a suite of probing tasks grouped into three categories: (i) generic graph, (ii) molecular substructure, and (iii) embedding space properties. By leveraging MOLGRAPHEVAL to benchmark existing GSSL methods against both current downstream datasets and our suite of tasks, we uncover significant inconsistencies between inferences drawn solely from existing datasets and those derived from more nuanced probing. These findings suggest that current evaluation methodologies fail to capture the entirety of the landscape.

2023-09-24

NeurIPS.cc/2023/Track/Datasets_and_Benchmarks (poster)

doi.org

openreview.net

Generating QM1B with PySCF$_{\text{IPU}}$

Alexander Mathiasen

Hatem Helal

Kerstin Klaser

Paul Balanca

Josef Dean

Carlo Luschi

Dominique Beaini

Andrew William Fitzgibbon

Dominic Masters

2023-09-24

NeurIPS.cc/2023/Track/Datasets_and_Benchmarks (poster)

openreview.net

Minigrid & Miniworld: Modular & Customizable Reinforcement Learning Environments for Goal-Oriented Tasks

Maxime Chevalier-Boisvert

Bolun Dai

Mark Towers

Rodrigo De Lazcano Perez-Vicente

Lucas Willems

Salem Lahlou

Suman Pal

Pablo Samuel Castro

J K Terry

We present the Minigrid and Miniworld libraries which provide a suite of goal-oriented 2D and 3D environments. The libraries were explicitly… (see more) created with a minimalistic design paradigm to allow users to rapidly develop new environments for a wide range of research-specific needs. As a result, both have received widescale adoption by the RL community, facilitating research in a wide range of areas. In this paper, we outline the design philosophy, environment details, and their world generation API. We also showcase the additional capabilities brought by the unified API between Minigrid and Miniworld through case studies on transfer learning (for both RL agents and humans) between the different observation spaces. The source code of Minigrid and Miniworld can be found at https://github.com/Farama-Foundation/Minigrid and https://github.com/Farama-Foundation/Miniworld along with their documentation at https://minigrid.farama.org/ and https://miniworld.farama.org/.

2023-09-24

NeurIPS.cc/2023/Track/Datasets_and_Benchmarks (poster)

doi.org

openreview.net

Temporal Graph Benchmark for Machine Learning on Temporal Graphs

Shenyang Huang

Farimah Poursafaei

Jacob Danovitch

Matthias Fey

Weihua Hu

Emanuele Rossi

Jure Leskovec

Michael M. Bronstein

Guillaume Rabusseau

Reihaneh Rabbany

We present the Temporal Graph Benchmark (TGB), a collection of challenging and diverse benchmark datasets for realistic, reproducible, and r… (see more)obust evaluation of machine learning models on temporal graphs. TGB datasets are of large scale, spanning years in duration, incorporate both node and edge-level prediction tasks and cover a diverse set of domains including social, trade, transaction, and transportation networks. For both tasks, we design evaluation protocols based on realistic use-cases. We extensively benchmark each dataset and find that the performance of common models can vary drastically across datasets. In addition, on dynamic node property prediction tasks, we show that simple methods often achieve superior performance compared to existing temporal graph models. We believe that these findings open up opportunities for future research on temporal graphs. Finally, TGB provides an automated machine learning pipeline for reproducible and accessible temporal graph research, including data loading, experiment setup and performance evaluation. TGB will be maintained and updated on a regular basis and welcomes community feedback. TGB datasets, data loaders, example codes, evaluation setup, and leaderboards are publicly available at https://tgb.complexdatalab.com/.

2023-09-24

NeurIPS.cc/2023/Track/Datasets_and_Benchmarks (poster)

doi.org

openreview.net

Augmenting Transit Network Design Algorithms with Deep Learning

Andrew Holliday

Gregory Dudek

This paper considers the use of deep learning models to enhance optimization algorithms for transit network design. Transit network design i… (see more)s the problem of determining routes for transit vehicles that minimize travel time and operating costs, while achieving full service coverage. State-of-the-art meta-heuristic search algorithms give good results on this problem, but can be very time-consuming. In contrast, neural networks can learn sub-optimal but fast-to-compute heuristics based on large amounts of data. Combining these approaches, we develop a fast graph neural network model for transit planning, and use it to initialize state-of-the-art search algorithms. We show that this combination can improve the results of these algorithms on a variety of metrics by up to 17%, without increasing their run time; or they can match the quality of the original algorithms while reducing the computing time by up to a factor of 50.

2023-09-23

2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC) (published)

doi.org

Substituting Data Annotation with Balanced Updates and Collective Loss in Multi-label Text Classification

Muberra Ozmen

Joseph Cotnareanu

Mark J. Coates

Multi-label text classification (MLTC) is the task of assigning multiple labels to a given text, and has a wide range of application domains… (see more). Most existing approaches require an enormous amount of annotated data to learn a classifier and/or a set of well-defined constraints on the label space structure, such as hierarchical relations which may be complicated to provide as the number of labels increases. In this paper, we study the MLTC problem in annotation-free and scarce-annotation settings in which the magnitude of available supervision signals is linear to the number of labels. Our method follows three steps, (1) mapping input text into a set of preliminary label likelihoods by natural language inference using a pre-trained language model, (2) calculating a signed label dependency graph by label descriptions, and (3) updating the preliminary label likelihoods with message passing along the label dependency graph, driven with a collective loss function that injects the information of expected label frequency and average multi-label cardinality of predictions. The experiments show that the proposed framework achieves effective performance under low supervision settings with almost imperceptible computational and memory overheads added to the usage of pre-trained language model outperforming its initial performance by 70\% in terms of example-based F1 score.

2023-09-23

ArXiv (preprint)

doi.org

arxiv.org

Disorganized Communication and Social Dysfunction in Schizophrenia: Emerging Concepts and Methods

Emmanuel Olarewaju

Guillaume Dumas

L. Palaniyappan

2023-09-22

Current Psychiatry Reports (published)

doi.org

Autonomic nervous system modulation during self-induced non-ordinary states of consciousness

Victor Oswald

Audrey Vanhaudenhuyse

Jitka Annen

Charlotte Martial

Aminata Bicego

Floriane Rousseaux

Corine Sombrun

Yann Harel

Marie-Elisabeth Faymonville

Steven Laureys

Karim Jerbi CoCo Lab

Olivia Gosseries

2023-09-21

Scientific Reports (published)

doi.org

Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets (Ultra Large Dataset)

Dominique Beaini

Shenyang Huang

Joao Alex Cunha

Zhiyi Li

Gabriela Moisescu-Pareja

Oleksandr Dymov

Samuel Maddrell-Mander

Callum McLean

Ali Parviz

Luis T. Díaz Müller

Jama Hussein Mohamud

Michael Craig

Cristian Gabellini

Jian Tang … (see 8 more)

Christopher G. Morris

Mirco Ravanelli

Guy Wolf

Prudencio Tossou

Hadrien Mary

Błażej Banaszewski

Chad Martin

Dominic Masters

2023-09-21

Zenodo (unknown)

doi.org

Auxiliary Losses for Learning Generalizable Concept-based Models

Ivaxi Sheth

S Ebrahimi Kahou

2023-09-20

NeurIPS.cc/2023/Conference (poster)

doi.org

openreview.net

Decision-Aware Actor-Critic with Function Approximation and Theoretical Guarantees

Sharan Vaswani

Amirreza Kazemi

Reza Babanezhad Harikandeh

Nicolas Roux

Actor-critic (AC) methods are widely used in reinforcement learning (RL) and benefit from the flexibility of using any policy gradient metho… (see more)d as the actor and value-based method as the critic. The critic is usually trained by minimizing the TD error, an objective that is potentially decorrelated with the true goal of achieving a high reward with the actor. We address this mismatch by designing a joint objective for training the actor and critic in a decision-aware fashion. We use the proposed objective to design a generic, AC algorithm that can easily handle any function approximation. We explicitly characterize the conditions under which the resulting algorithm guarantees monotonic policy improvement, regardless of the choice of the policy and critic parameterization. Instantiating the generic algorithm results in an actor that involves maximizing a sequence of surrogate functions (similar to TRPO, PPO) and a critic that involves minimizing a closely connected objective. Using simple bandit examples, we provably establish the benefit of the proposed critic objective over the standard squared error. Finally, we empirically demonstrate the benefit of our decision-aware actor-critic framework on simple RL problems.

2023-09-20

NeurIPS.cc/2023/Conference (poster)

doi.org

openreview.net

A Definition of Continual Reinforcement Learning

David Abel

Andre Barreto

Benjamin Van Roy

Doina Precup

Hado van Hasselt

Satinder Singh

2023-09-20