Publications

Adaptive Integration of Categorical and Multi-relational Ontologies with EHR Data for Medical Concept Embedding

Chin Wang Cheong

Kejing Yin

William K. Cheung

Benjamin Fung

Jonathan Poon

2023-11-14

ACM Transactions on Intelligent Systems and Technology (published)

doi.org

Using Representation Expressiveness and Learnability to Evaluate Self-Supervised Learning Methods

Yuchen Lu

Zhen Liu

Aristide Baratin

Romain Laroche

Aaron Courville

Alessandro Sordoni

2023-11-14

TMLR (accepted)

openreview.net

Language Model-In-The-Loop: Data Optimal Approach to Learn-To-Recommend Actions in Text Games

Arjun Vaithilingam Sudhakar

Prasanna Parthasarathi

Janarthanan Rajendran

Sarath Chandar Anbil Parthipan

2023-11-13

ArXiv (preprint)

doi.org

arxiv.org

Ex Post Conditions for the Exactness of Optimal Power Flow Conic Relaxations

Jean-Luc Lupien

Antoine Lesage-Landry

Convex relaxations of the optimal power flow (OPF) problem provide an efficient alternative to solving the intractable alternating current (… (see more)AC) optimal power flow. The conic subset of OPF convex relaxations, in particular, greatly accelerate resolution while leading to high-quality approximations that are exact in several scenarios. However, the sufficient conditions guaranteeing exactness are stringent, e.g., requiring radial topologies. In this short communication, we present two equivalent ex post conditions for the exactness of any conic relaxation of the OPF. These rely on obtaining either a rank-1 voltage matrix or self-coherent cycles. Instead of relying on sufficient conditions a priori, satisfying one of the presented ex post conditions acts as an exactness certificate for the computed solution. The operator can therefore obtain an optimality guarantee when solving a conic relaxation even when a priori exactness requirements are not met. Finally, we present numerical examples from the MATPOWER library where the ex post conditions hold even though the exactness sufficient conditions do not, thereby illustrating the use of the conditions.

2023-11-13

ArXiv (preprint)

arxiv.org

Bridging the Gap Between Offline and Online Reinforcement Learning Evaluation Methodologies

Shiva Kanth Sujit

Pedro Braga

Jorg Bornschein

Samira Ebrahimi Kahou

Reinforcement learning (RL) has shown great promise with algorithms learning in environments with large state and action spaces purely from … (see more)scalar reward signals. A crucial challenge for current deep RL algorithms is that they require a tremendous amount of environment interactions for learning. This can be infeasible in situations where such interactions are expensive, such as in robotics. Offline RL algorithms try to address this issue by bootstrapping the learning process from existing logged data without needing to interact with the environment from the very beginning. While online RL algorithms are typically evaluated as a function of the number of environment interactions, there isn't a single established protocol for evaluating offline RL methods. In this paper, we propose a sequential approach to evaluate offline RL algorithms as a function of the training set size and thus by their data efficiency. Sequential evaluation provides valuable insights into the data efficiency of the learning process and the robustness of algorithms to distribution changes in the dataset while also harmonizing the visualization of the offline and online learning phases. Our approach is generally applicable and easy to implement. We compare several existing offline RL algorithms using this approach and present insights from a variety of tasks and offline datasets.

2023-11-10

TMLR (accepted)

doi.org

openreview.net

CD3ζ ITAMs enable ligand discrimination and antagonism by inhibiting TCR signaling in response to low-affinity peptides

Guillaume Gaud

Sooraj R. Achar

François X. P. Bourassa

John S. Davies

Teri Hatzihristidis

Seeyoung Choi

Taisuke Kondo

Selamawit Gossa

Jan Lee

Paul Juneau

Naomi Taylor

Christian S. Hinrichs

Dorian B. McGavern

Paul François

Grégoire Altan-Bonnet

Paul E. Love

2023-11-09

Nature Immunology (published)

doi.org

Stochastic Mirror Descent: Convergence Analysis and Adaptive Variants via the Mirror Stochastic Polyak Stepsize

Ryan D'Orazio

Nicolas Loizou

Issam Hadj Laradji

Ioannis Mitliagkas

We investigate the convergence of stochastic mirror descent (SMD) under interpolation in relatively smooth and smooth convex optimization. I… (see more)n relatively smooth convex optimization we provide new convergence guarantees for SMD with a constant stepsize. For smooth convex optimization we propose a new adaptive stepsize scheme --- the mirror stochastic Polyak stepsize (mSPS). Notably, our convergence results in both settings do not make bounded gradient assumptions or bounded variance assumptions, and we show convergence to a neighborhood that vanishes under interpolation. Consequently, these results correspond to the first convergence guarantees under interpolation for the exponentiated gradient algorithm for fixed or adaptive stepsizes. mSPS generalizes the recently proposed stochastic Polyak stepsize (SPS) (Loizou et al. 2021) to mirror descent and remains both practical and efficient for modern machine learning applications while inheriting the benefits of mirror descent. We complement our results with experiments across various supervised learning tasks and different instances of SMD, demonstrating the effectiveness of mSPS.

2023-11-08

TMLR (accepted)

openreview.net

Capture the Flag: Uncovering Data Insights with Large Language Models

Issam Hadj Laradji

Perouz Taslakian

Sai Rajeswar

Valentina Zantedeschi

Alexandre Lacoste

Nicolas Chapados

David Vazquez

Chris Pal

Alexandre Drouin

2023-11-07

NeurIPS.cc/2023/Workshop/FMDM (published)

doi.org

openreview.net

Multi-resolution Time-Series Transformer for Long-term Forecasting

Yitian Zhang

Liheng Ma

Soumyasundar Pal

Yingxue Zhang

Mark Coates

2023-11-07

ArXiv (preprint)

doi.org

arxiv.org

Neuro-GPT: Towards A Foundation Model for EEG

Wenhui Cui

Woojae Jeong

Philipp Thölke

Takfarinas Medani

Karim Jerbi

Anand A. Joshi

Richard M. Leahy

To handle the scarcity and heterogeneity of electroencephalography (EEG) data for Brain-Computer Interface (BCI) tasks, and to harness the p… (see more)ower of large publicly available data sets, we propose Neuro-GPT, a foundation model consisting of an EEG encoder and a GPT model. The foundation model is pre-trained on a large-scale data set using a self-supervised task that learns how to reconstruct masked EEG segments. We then fine-tune the model on a Motor Imagery Classification task to validate its performance in a low-data regime (9 subjects). Our experiments demonstrate that applying a foundation model can significantly improve classification performance compared to a model trained from scratch, which provides evidence for the generalizability of the foundation model and its ability to address challenges of data scarcity and heterogeneity in EEG. The code is publicly available at github.com/wenhui0206/NeuroGPT.

2023-11-07

ArXiv (preprint)

arxiv.org