Publications
Preprints
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Replay Buffer With Local Forgetting for Adaptive Deep Model-Based Reinforcement Learning
Ali Rahimi-Kalahroudi, Janarthanan Rajendran, Ida Momennejad, Harm van Seijen, and Sarath Chandar
Deep Reinforcement Learning Workshop, NeurIPS, 2022.
[arxiv] -
Segmentation of Multiple Sclerosis Lesions across Hospitals: Learn Continually or Train from Scratch?
Naga Karthik Enamundram, Anne Kerbrat, Pierre Labauge, Tobias Granberg, Jason Talbott, Daniel S. Reich, Massimo Filippi, Rohit Bakshi, Virginie Callot, Sarath Chandar, and Julien Cohen-Adad
In ArXiv, 2022.
[arxiv], [code] -
Sharpness-Aware Training for Accurate Inference on Noisy DNN Accelerators
Gonçalo Mordido, Sarath Chandar, and François Leduc-Primeau
Conference on Lifelong Learning Agents (CoLLAs) workshop, Edge Intelligence Workshop, 2022.
[arxiv] -
An Introduction to Lifelong Supervised Learning
Shagun Sodhani, Mojtaba Farmazi, Sanket Vaibhav Mehta, Pranshu Malviya, Mohamed Abdelsalam, Janarthanan Rajendran, and Sarath Chandar
In ArXiv, 2022.
[arxiv] -
RECOVER: Sequential Model Optimization Platform for Combination Drug Repurposing Identifies Novel Synergistic Compounds in vitro
Paul Bertin, Jarrid Rector-Brooks, Deepak Sharma, Thomas Gaudelet, Andrew Anighoro, Torsten Gross, Francisco Martínez-Peña, Eileen L. Tang, Suraj M S, Cristian Regep, Jeremy Hayter, Maksym Korablyov, Nicholas Valiante, Almer van der Sloot, Mike Tyers, Charles Roberts, Michael M. Bronstein, Luke L. Lairson, Jake P. Taylor-King, and Yoshua Bengio
In arXiv, 2022.
[arXiv], [code] -
An Empirical Investigation of the Role of Pre-training in Lifelong Learning
Sanket Vaibhav Mehta, Darshan Patil, Sarath Chandar, and Emma Strubell
In ArXiv, 2021.
[arxiv] -
Maximum Reward Formulation In Reinforcement Learning
Sai Krishna Gottipati, Yashaswi Pathak, Rohan Nuttall, Raviteja Chunduru, Ahmed Touati, Sriram Ganapathi Subramanian, Matthew E Taylor, and Sarath Chandar
In arXiv, 2020.
[arXiv]
Conference and Journal Papers
2023
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Multi-Agent Reinforcement Learning for Fast-Timescale Demand Response of Residential Loads
Vincent Mai, Philippe Maisonneuve, Tianyu Zhang, Hadi Nekoei, Liam Paull, and Antoine Lesage-Landry
International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2023.
[arxiv] -
Deep Learning on a Healthy Data Diet: Finding Important Examples for Fairness
Abdelrahman Zayed, Prasanna Parthasarathi, Gonçalo Mordido, Hamid Palangi, Samira Shabanian, and Sarath Chandar
AAAI Conference on Artificial Intelligence (AAAI), 2023.
[arxiv] -
DEUP: Direct Epistemic Uncertainty Prediction
Moksh Jain, Salem Lahlou, Hadi Nekoei, Victor Butoi, Paul Bertin, Jarrid Rector-Brooks, Maksym Korablyov, and Yoshua Bengio
Transactions on Machine Learning Research (TMLR), 2023.
[arXiv], [code] -
Label fusion and training methods for reliable representation of inter-rater uncertainty
Andreanne Lemay, Charley Gros, Naga Karthik Enamundram, and Julien Cohen-Adad
The Journal of Machine Learning for Biomedical Imaging (MELBA), 2023.
[PDF]
2022
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Detecting Languages Unintelligible to Multilingual Models through Local Structure Probes
Louis Clouâtre, Prasanna Parthasarathi, Amal Zouaq, and Sarath Chandar
Findings of Empirical Methods in Natural Language Processing (EMNLP), 2022.
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Evaluating the Faithfulness of Importance Measures in NLP by Recursively Masking Allegedly Important Tokens and Retraining
Andreas Madsen, Nicholas Meade, Vaibhav Adlakha, and Siva Reddy
Findings of Empirical Methods in Natural Language Processing (EMNLP), 2022.
[BlackboxNLP Workshop, 2022]
[arXiv], [code] -
Local Structure Matters Most in Most Languages
Louis Clouâtre, Prasanna Parthasarathi, Amal Zouaq, and Sarath Chandar
AACL-IJCNLP 2022, 2022.
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TAG: Task-based Accumulated Gradients for Lifelong learning
Pranshu Malviya, Balaraman Ravindran, and Sarath Chandar
Conference on Lifelong Learning Agents (CoLLAs), 2022.
[Workshop on Theory and Foundation of Continual Learning, ICML, 2021]
[arxiv], [code] -
Combining Reinforcement Learning and Constraint Programming for Sequence-Generation Tasks with Hard Constraints
Daphné Lafleur, Sarath Chandar, and Gilles Pesant
Principles and Practice of Constraint Programming (CP), 2022.
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Biological Sequence Design with GFlowNets
Moksh Jain, Emmanuel Bengio, Alex-Hernandez Garcia, Jarrid Rector-Brooks, Bonaventure F. P. Dossou, Chanakya Ekbote, Jie Fu, Tianyu Zhang, Micheal Kilgour, Dinghuai Zhang, Lena Simine, Payel Das, and Yoshua Bengio
International Conference on Machine Learning (ICML), 2022.
[arXiv], [code] -
Towards Evaluating Adaptivity of Model-Based Reinforcement Learning Methods
Yi Wan*, Ali Rahimi-Kalahroudi*, Janarthanan Rajendran, Ida Momennejad, Sarath Chandar, and Harm van Seijen
International Conference on Machine Learning (ICML), 2022.
[arXiv], [code] -
Improving Meta-Learning Generalization with Activation-Based Early-Stopping
Simon Guiroy, Christopher Pal, Gonçalo Mordido, and Sarath Chandar
Conference on Lifelong Learning Agents (CoLLAs), 2022.
[arxiv], [code], [video] -
Post-hoc Interpretability for Neural NLP: A Survey
Andreas Madsen, Siva Reddy, and Sarath Chandar
ACM Computing Surveys, 2022.
[arXiv] -
Local Structure Matters Most: Perturbation Study in NLU
Louis Clouâtre, Prasanna Parthasarathi, Amal Zouaq, and Sarath Chandar
Findings of ACL, 2022.
[arxiv] -
Towards Language-independent Brown Build Detection
Doriane Olewicki, Mathieu Nayrolles, and Bram Adams
International Conference on Software Engineering (ICSE), 2022.
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Memory Augmented Optimizers for Deep Learning
Paul-Aymeric McRae, Prasanna Parthasarathi, Mido Assran, and Sarath Chandar
International Conference on Learning Representations (ICLR), 2022.
[openreview], [code] -
PatchUp: A Feature-Space Block-Level Regularization Technique for Convolutional Neural Networks
Mojtaba Faramarzi, Mohammad Amini, Akilesh Badrinaaraayanan, Vikas Verma, and Sarath Chandar
AAAI Conference on Artificial Intelligence (AAAI), 2022.
[arXiv], [code]
2021
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MLMLM: Link Prediction with Mean Likelihood Masked Language Model
Louis Clouâtre, Philippe Trempe, Amal Zouaq, and Sarath Chandar
Findings of ACL, 2021.
[arXiv] -
Benchmarking Bias Mitigation Algorithms in Representation Learning through Fairness Metrics
Charan Reddy, Deepak Sharma, Soroush Mehri, Adriana Romero, Samira Shabanian, and Sina Honari
Proceedings of the Neural Information Processing Systems (NeurIPS) Track on Datasets and Benchmarks, 2021.
[openreview], [code] -
A Brief Study on the Effects of Training Generative Dialogue Models with a Semantic loss
Prasanna Parthasarathi, Mohamed Abdelsalam, Joelle Pineau, and Sarath Chandar
Proceedings of the 22nd Annual SIGdial Meeting on Discourse and Dialogue, 2021.
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Do Encoder Representations of Generative Dialogue Models Encode Sufficient Information about the Task ?
Prasanna Parthasarathi, Sarath Chandar, and Joelle Pineau
Proceedings of the 22nd Annual SIGdial Meeting on Discourse and Dialogue, 2021.
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Continuous Coordination As a Realistic Scenario for Lifelong Learning
Hadi Nekoei, Akilesh Badrinaaraayanan, Aaron Courville, and Sarath Chandar
International Conference on Machine Learning (ICML), 2021.
[arXiv], [code] -
A Survey of Data Augmentation Approaches for NLP
Steven Y. Feng, Varun Gangal, Jason Wei, Sarath Chandar, Soroush Vosoughi, Teruko Mitamura, and Eduard Hovy
Findings of ACL, 2021.
[arXiv] -
Towered Actor Critic for Handling Multiple Action Types in Reinforcement Learning For Drug Discovery
Sai Krishna Gottipati, Yashaswi Pathak, Boris Sattarov, Sahir, Rohan Nuttall, Mohammad Amini, Matthew E. Taylor, and Sarath Chandar
AAAI Conference on Artificial Intelligence, 2021.
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IIRC: Incremental Implicitly-Refined Classification
Mohamed Abdelsalam, Mojtaba Faramarzi, Shagun Sodhani, and Sarath Chandar
Conference on Computer Vision and Pattern Recognition (CVPR), 2021.
[arXiv], [code], [website], [PyPI], [docs]
2020
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The LoCA Regret: A Consistent Metric to Evaluate Model-Based Behavior in Reinforcement Learning
Harm van Seijen, Hadi Nekoei, Evan Racah, and Sarath Chandar
Neural Information Processing Systems (NeurIPS), 2020.
[arXiv], [code] -
Learning To Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning
Sai Krishna Gottipati*, Boris Sattarov*, Sufeng Niu, Yashaswi Pathak, Haoran Wei, Shengchao Liu, Karam MJ Thomas, Simon Blackburn, Connor W Coley, Jian Tang, Sarath Chandar, and Yoshua Bengio
International Conference on Machine Learning (ICML), 2020.
[arXiv]