Publications

Dealing With Non-stationarity in Decentralized Cooperative Multi-Agent Deep Reinforcement Learning via Multi-Timescale Learning

Hadi Nekoei

Akilesh Badrinaaraayanan

Amit Sinha

Mohammad Amin Amini

Janarthanan Rajendran

Aditya Mahajan

Sarath Chandar

2023-11-20

Proceedings of The 2nd Conference on Lifelong Learning Agents (publié)

An Empirical Study of Self-Admitted Technical Debt in Machine Learning Software

Aaditya Bhatia

Foutse Khomh

Bram Adams

Ahmed E. Hassan

The emergence of open-source ML libraries such as TensorFlow and Google Auto ML has enabled developers to harness state-of-the-art ML algori… (voir plus)thms with minimal overhead. However, during this accelerated ML development process, said developers may often make sub-optimal design and implementation decisions, leading to the introduction of technical debt that, if not addressed promptly, can have a significant impact on the quality of the ML-based software. Developers frequently acknowledge these sub-optimal design and development choices through code comments during software development. These comments, which often highlight areas requiring additional work or refinement in the future, are known as self-admitted technical debt (SATD). This paper aims to investigate SATD in ML code by analyzing 318 open-source ML projects across five domains, along with 318 non-ML projects. We detected SATD in source code comments throughout the different project snapshots, conducted a manual analysis of the identified SATD sample to comprehend the nature of technical debt in the ML code, and performed a survival analysis of the SATD to understand the evolution of such debts. We observed: i) Machine learning projects have a median percentage of SATD that is twice the median percentage of SATD in non-machine learning projects. ii) ML pipeline components for data preprocessing and model generation logic are more susceptible to debt than model validation and deployment components. iii) SATDs appear in ML projects earlier in the development process compared to non-ML projects. iv) Long-lasting SATDs are typically introduced during extensive code changes that span multiple files exhibiting low complexity.

2023-11-20

ArXiv (prépublication)

Responsible AI Research Needs Impact Statements Too

Alexandra Olteanu

Michael Ekstrand

Carlos Castillo

Jina Suh

All types of research, development, and policy work can have unintended, adverse consequences - work in responsible artificial intelligence … (voir plus)(RAI), ethical AI, or ethics in AI is no exception.

2023-11-20

ArXiv (prépublication)

Task-Agnostic Continual Reinforcement Learning: Gaining Insights and Overcoming Challenges

Massimo Caccia

Jonas Mueller

Taesup Kim

Laurent Charlin

Rasool Fakoor

2023-11-20

Proceedings of The 2nd Conference on Lifelong Learning Agents (publié)

proceedings.mlr.press

Towards Few-shot Coordination: Revisiting Ad-hoc Teamplay Challenge In the Game of Hanabi

Hadi Nekoei

Xutong Zhao

Janarthanan Rajendran

Miao Liu

Sarath Chandar

2023-11-20

Proceedings of The 2nd Conference on Lifelong Learning Agents (publié)

Inferring dynamic regulatory interaction graphs from time series data with perturbations

Dhananjay Bhaskar

Daniel Sumner Magruder

Edward De Brouwer

Matheo Morales

Aarthi Venkat

Frederik Wenkel

Guy Wolf

Smita Krishnaswamy

2023-11-18

logconference.io/LOG/2023/Conference (poster)

MUDiff: Unified Diffusion for Complete Molecule Generation

Chenqing Hua

Sitao Luan

Minkai Xu

Zhitao Ying

Rex Ying

Jie Fu

Stefano Ermon

Doina Precup

2023-11-18

logconference.io/LOG/2023/Conference (poster)

The evidence mismatch in pediatric surgical practice

Marina Broomfield

Zena Agabani

Elena Guadagno

Dan Poenaru

Robert Baird

2023-11-18

Pediatric surgery international (Print) (publié)

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 (publié)

Generalizable Imitation Learning Through Pre-Trained Representations

Wei-Di Chang

Francois 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… (voir plus)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 (prépublication)

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 (publié)