Publications

Differentiable visual computing for inverse problems and machine learning

Andrew Spielberg

Fangcheng Zhong

Konstantinos Rematas

Krishna Murthy

Cengiz Oztireli

Tzu-Mao Li

Derek Nowrouzezahrai

2023-11-17

Nature Machine Intelligence (published)

doi.org

arxiv.org

AfriMTE and AfriCOMET: Enhancing COMET to Embrace Under-resourced African Languages

Jiayi Wang

David Ifeoluwa Adelani

Sweta Agrawal

Marek Masiak

Ricardo Rei

Eleftheria Briakou

Marine Carpuat

Xuanli He

Sofia Bourhim

Andiswa Bukula

Muhidin A. Mohamed

Temitayo Olatoye

Tosin Adewumi

Hamam Mokayede

Christine Mwase

Wangui Kimotho

Foutse Yuehgoh

Aremu Anuoluwapo

Jessica Ojo

Shamsuddeen Hassan Muhammad … (see 38 more)

Salomey Osei

Abdul-Hakeem Omotayo

Chiamaka Ijeoma Chukwuneke

Perez Ogayo

Oumaima Hourrane

Salma El Anigri

Lolwethu Ndolela

Thabiso Mangwana

Shafie Abdi Mohamed

Ayinde Hassan

Oluwabusayo Olufunke Awoyomi

Lama Alkhaled

sana Sabah al-azzawi

Naome A. Etori

Millicent A. Ochieng

Clemencia Siro

Samuel Njoroge

Eric Muchiri

Wangari Kimotho

Lyse Naomi Wamba Momo

Daud Abolade

Simbiat Ajao

Iyanuoluwa Shode

Ricky Macharm

Ruqayya Nasir Iro

Saheed Salahudeen Abdullahi

Stephen E. Moore

Bernard Opoku

Zainab Akinjobi

Abeeb Afolabi

Nnaemeka Casmir Obiefuna

Onyekachi Ogbu

Sam Brian

Verrah Akinyi Otiende

CHINEDU EMMANUEL MBONU

Toadoum Sari Sakayo

Yao Lu

Pontus Stenetorp

Despite the recent progress on scaling multilingual machine translation (MT) to several under-resourced African languages, accurately measur… (see more)ing this progress remains challenging, since evaluation is often performed on n-gram matching metrics such as BLEU, which typically show a weaker correlation with human judgments. Learned metrics such as COMET have higher correlation; however, the lack of evaluation data with human ratings for under-resourced languages, complexity of annotation guidelines like Multidimensional Quality Metrics (MQM), and limited language coverage of multilingual encoders have hampered their applicability to African languages. In this paper, we address these challenges by creating high-quality human evaluation data with simplified MQM guidelines for error detection and direct assessment (DA) scoring for 13 typologically diverse African languages. Furthermore, we develop AfriCOMET: COMET evaluation metrics for African languages by leveraging DA data from well-resourced languages and an African-centric multilingual encoder (AfroXLM-R) to create the state-of-the-art MT evaluation metrics for African languages with respect to Spearman-rank correlation with human judgments (0.441).

2023-11-16

ArXiv (preprint)

arxiv.org

Evaluating In-Context Learning of Libraries for Code Generation

Arkil Patel

Siva Reddy

Dzmitry Bahdanau

Pradeep Dasigi

2023-11-16

ArXiv (preprint)

doi.org

arxiv.org

Generalizable Imitation Learning Through Pre-Trained Representations

Wei-Di Chang

Francois R. Hogan

David Meger

Gregory Dudek

In this paper we leverage self-supervised vision transformer models and their emergent semantic abilities to improve the generalization abil… (see more)ities of imitation learning policies. We introduce BC-ViT, an imitation learning algorithm that leverages rich DINO pre-trained Visual Transformer (ViT) patch-level embeddings to obtain better generalization when learning through demonstrations. Our learner sees the world by clustering appearance features into semantic concepts, forming stable keypoints that generalize across a wide range of appearance variations and object types. We show that this representation enables generalized behaviour by evaluating imitation learning across a diverse dataset of object manipulation tasks. Our method, data and evaluation approach are made available to facilitate further study of generalization in Imitation Learners.

2023-11-15

ArXiv (preprint)

doi.org

arxiv.org

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