Publications

Conference and Journal Papers

2024

58. Balancing Context Length and Mixing Times for Reinforcement Learning at Scale
    Matthew Riemer, Khimya Khetarpal, Janarthanan Rajendran, and Sarath Chandar
    Neural Information Processing Systems (NeurIPS), 2024.
    #RL
    

59. Exploring Quantization for Efficient Pre-Training of Transformer Language Models
    Kamran Chitsaz, Quentin Fournier, Gonçalo Mordido, and Sarath Chandar
    Findings of the Association for Computational Linguistics (EMNLP), 2024.
    #NLP, #DL
    [arXiv]

60. Context-Aware Assistant Selection for Improved Inference Acceleration with Large Language Models
    Jerry Huang, Prasanna Parthasarathi, Mehdi Rezagholizadeh, and Sarath Chandar
    Conference on Empirical Methods in Natural Language Processing (EMNLP), 2024.
    #NLP
    [arXiv]

61. Do Large Language Models Know How Much They Know?
    Gabriele Prato, Jerry Huang, Prasanna Parthasarathi, Shagun Sodhani, and Sarath Chandar
    Conference on Empirical Methods in Natural Language Processing (EMNLP), 2024.
    #NLP
    

62. Toward Debugging Deep Reinforcement Learning Programs with RLExplorer
    Rached Bouchoucha, Ahmed Haj Yahmed, Darshan Patil, Janarthanan Rajendran, Amin Nikanjam, Sarath Chandar, and Foutse Khomh
    International Conference on Software Maintenance and Evolution (ICSME), 2024.
    #RL
    

63. WorkArena++: Towards Compositional Planning and Reasoning-based Common Knowledge Work Tasks
    Leo Boisvert*, Megh Thakkar*, Maxime Gasse, Massimo Caccia, Thibault Le Sellier De Chezelles, Quentin Cappart, Nicolas Chapados, Alexandre Lacoste, and Alexandre Drouin
    Neural Information Processing Systems (NeurIPS), Datasets and Benchmarks, 2024.
    #NLP
    [arXiv], [code]

64. Should We Attend More or Less? Modulating Attention for Fairness
    Abdelrahman Zayed, Gonçalo Mordido, Samira Shabanian, and Sarath Chandar
    Conference on Language Modeling (COLM), 2024.
    #NLP
    [arXiv]

65. Are self-explanations from Large Language Models faithful?
    Andreas Madsen, Sarath Chandar, and Siva Reddy
    Findings of the Association for Computational Linguistics (ACL), 2024.
    #NLP
    [arXiv], [code]

66. A deep-dive into the tradeoffs of preference alignment with PEFT
    Megh Thakkar, Quentin Fournier, Matthew Riemer, Pin-Yu Chen, Amal Zouaq, Payel Das, and Sarath Chandar
    Annual Meeting of the Association for Computational Linguistics (ACL), 2024.
    #NLP
    [arXiv]

67. Why Don’t Prompt-Based Fairness Metrics Correlate?
    Abdelrahman Zayed, Gonçalo Mordido, Ioana Baldini, and Sarath Chandar
    Annual Meeting of the Association for Computational Linguistics (ACL), 2024.
    #NLP
    [arXiv], [YouTube]

68. Sub-goal Distillation: A Method to Improve Small Language Agents
    Maryam Hashemzadeh, Elias Stengel-Eskin, Sarath Chandar, and Marc-Alexandre Cote
    Conference on Lifelong Learning Agents (CoLLAs), 2024. [Oral presentation.]
    #RL, #NLP
    [arXiv]

69. Partial Models for Building Adaptive Model-Based Reinforcement Learning Agents
    Safa Alver, Ali Rahimi-Kalahroudi, and Doina Precup
    Conference on Lifelong Learning Agents (CoLLAs), 2024.
    #RL
    [arXiv]

70. Lookbehind-SAM: k steps back, 1 step forward
    Gonçalo Mordido, Pranshu Malviya, Aristide Baratin, and Sarath Chandar
    International Conference on Machine Learning (ICML), 2024.
    #DL
    [arXiv], [code], [YouTube]

71. Faithfulness Measurable Masked Language Models
    Andreas Madsen, Siva Reddy, and Sarath Chandar
    International Conference on Machine Learning (ICML), 2024. [Spotlight award - top 3.5%]
    #NLP
    [arXiv], [code], [YouTube], [blogpost]

72. Promoting Exploration in Memory-Augmented Adam using Critical Momenta
    Pranshu Malviya, Gonçalo Mordido, Aristide Baratin, Reza Babanezhad Harikandeh, Jerry Huang, Simon Lacoste-Julien, Razvan Pascanu, and Sarath Chandar
    Transactions on Machine Learning Research (TMLR), 2024.
    #DL
    [arXiv]

73. A Responsible Framework for Applying Artificial Intelligence on Medical Images and Signals at the Point-of-care: the PACS-AI Platform
    Pascal Theriault-Lauzier, Denis Cobin, Olivier Tastet, Elodie Labrecque Langlais, Bahareh Taji, Guson Kang, Aun-Yeong Chong, Derek So, An Tang, Judy Wawira Gichoya, Sarath Chandar, Pierre-Luc Déziel, Julie G Hussin, Samuel Kadoury, and Robert Avram
    Canadian Journal of Cardiology, 2024.
    #DL, #Other
    

74. MVP: Minimal Viable Phrase for Long Text Understanding
    Louis Clouâtre, Amal Zouaq, and Sarath Chandar
    Joint International Conference on Computational Linguistics, Language, Resources and Evaluation (LREC-COLING), 2024.
    #NLP
    

75. Mastering Memory Tasks with World Models
    Mohammad Reza Samsami*, Artem Zholus*, Janarthanan Rajendran, and Sarath Chandar
    International Conference on Learning Representations (ICLR), 2024. [Oral presentation.]
    #RL, #DL
    [openreview]

76. Intelligent Switching for Reset-Free RL
    Darshan Patil, Janarthanan Rajendran, Glen Berseth, and Sarath Chandar
    International Conference on Learning Representations (ICLR), 2024.
    #RL
    [openreview]

77. On the Costs and Benefits of Adopting Lifelong Learning for Software Analytics - Empirical Study on Brown Build and Risk Prediction
    Doriane Olewicki, Sarra Habchi, Mathieu Nayrolles, Mojtaba Faramarzi, Sarath Chandar, and Bram Adams
    International Conference on Software Engineering (ICSE) - Software Engineering in Practice Track, 2024. [ICSE24 SEIP Distinguished Paper Award.]
    #DL
    [arXiv]

78. Fast and Accurate Output Error Estimation for Memristor-Based Deep Neural Networks
    Jonathan Kern, Sébastien Henwood, Gonçalo Mordido, Elsa Dupraz, Abdeldjalil Aïssa-El-Bey, Yvon Savaria, and François Leduc-Primeau
    IEEE Transactions on Signal Processing, 2024.
    #DL
    [paper]

79. Fairness-Aware Structured Pruning in Transformers
    Abdelrahman Zayed, Gonçalo Mordido, Samira Shabanian, Ioana Baldini, and Sarath Chandar
    AAAI Conference on Artificial Intelligence (AAAI), 2024.
    #NLP
    [arXiv], [YouTube]

80. Learning Conditional Policies for Crystal Design Using Offline Reinforcement Learning
    Prashant Govindarajan, Santiago Miret, Jarrid Rector-Brooks, Mariano Phielipp, Janarthanan Rajendran, and Sarath Chandar
    Digital Discovery Journal, 2024.
    #RL
    [openreview]

2023

35. Self-Influence Guided Data Reweighting for Language Model Pre-training
    Megh Thakkar, Tolga Bolukbasi, Sriram Ganapathy, Shikhar Vashishth, Sarath Chandar, and Partha Talukdar
    Conference on Empirical Methods in Natural Language Processing (EMNLP), 2023.
    #NLP
    [arXiv]

36. EpiK-Eval: Evaluation for Language Models as Epistemic Models
    Gabriele Prato, Jerry Huang, Prasanna Parthasarathi, Shagun Sodhani, and Sarath Chandar
    Conference on Empirical Methods in Natural Language Processing (EMNLP), 2023.
    #NLP
    [arXiv], [code]

37. Measuring the Knowledge Acquisition-Utilization Gap in Pretrained Language Models
    Amirhossein Kazemnejad, Mehdi Rezagholizadeh, Prasanna Parthasarathi, and Sarath Chandar
    Findings of the Association for Computational Linguistics (EMNLP), 2023.
    #NLP
    [arXiv]

38. Training DNNs Resilient to Adversarial and Random Bit-Flips by Learning Quantization Ranges
    Kamran Chitsaz, Gonçalo Mordido, Jean Pierre David, and François Leduc-Primeau
    Transactions on Machine Learning Research (TMLR), 2023.
    #DL
    [openreview], [code]

39. Replay Buffer with Local Forgetting for Adapting to Local Environment Changes in Deep Model-Based Reinforcement Learning
    Ali Rahimi-Kalahroudi, Janarthanan Rajendran, Ida Momennejad, Harm van Seijen, and Sarath Chandar
    Conference on Lifelong Learning Agents (CoLLAs), 2023.
    [Deep Reinforcement Learning Workshop, NeurIPS, 2022]
    #RL
    [arXiv]

40. Towards Few-shot Coordination: Revisiting Ad-hoc Teamplay Challenge In the Game of Hanabi
    Hadi Nekoei, Xutong Zhao, Janarthanan Rajendran, Miao Liu, and Sarath Chandar
    Conference on Lifelong Learning Agents (CoLLAs), 2023.
    #RL
    [paper]

41. Dealing With Non-stationarity in Decentralized Cooperative Multi-Agent Deep Reinforcement Learning via Multi-Timescale Learning
    Hadi Nekoei, Akilesh Badrinaaraayanan, Amit Sinha, Mohammad Amini, Janarthanan Rajendran, Aditya Mahajan, and Sarath Chandar
    Conference on Lifelong Learning Agents (CoLLAs), 2023.
    #RL
    [arXiv]

42. Conditionally Optimistic Exploration for Cooperative Deep Multi-Agent Reinforcement Learning
    Xutong Zhao, Yangchen Pan, Chenjun Xiao, Sarath Chandar, and Janarthanan Rajendran
    Conference on Uncertainty in Artificial Intelligence (UAI), 2023.
    #RL
    [arXiv]

43. An Empirical Investigation of the Role of Pre-training in Lifelong Learning
    Sanket Vaibhav Mehta, Darshan Patil, Sarath Chandar, and Emma Strubell
    Journal of Machine Learning Research, 2023.
    #DL
    [arXiv]

44. 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.
    #RL
    [arXiv]

45. 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.
    #NLP
    [arXiv], [YouTube]

46. 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.
    #DL
    [arXiv], [code]

47. 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.
    #DL
    [paper]

2022

22. 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 the Association for Computational Linguistics (EMNLP), 2022.
    [BlackboxNLP Workshop, 2022]
    #NLP
    [arXiv], [code]

23. Detecting Languages Unintelligible to Multilingual Models through Local Structure Probes
    Louis Clouâtre, Prasanna Parthasarathi, Amal Zouaq, and Sarath Chandar
    Findings of the Association for Computational Linguistics (EMNLP), 2022.
    #NLP
    

24. Local Structure Matters Most in Most Languages
    Louis Clouâtre, Prasanna Parthasarathi, Amal Zouaq, and Sarath Chandar
    Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the International Joint Conference on Natural Language Processing (AACL-IJCNLP), 2022.
    #NLP
    

25. 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]
    #DL
    [arXiv], [code]

26. 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.
    #DL
    [arXiv], [code], [YouTube]

27. 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.
    #RL
    

28. 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.
    #DL
    [arXiv], [code]

29. 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.
    #RL
    [arXiv], [code]

30. Post-hoc Interpretability for Neural NLP: A Survey
    Andreas Madsen, Siva Reddy, and Sarath Chandar
    ACM Computing Surveys, 2022.
    #NLP
    [arXiv]

31. Local Structure Matters Most: Perturbation Study in NLU
    Louis Clouâtre, Prasanna Parthasarathi, Amal Zouaq, and Sarath Chandar
    Findings of the Association for Computational Linguistics (ACL), 2022.
    #NLP
    [arXiv]

32. Memory Augmented Optimizers for Deep Learning
    Paul-Aymeric McRae, Prasanna Parthasarathi, Mido Assran, and Sarath Chandar
    International Conference on Learning Representations (ICLR), 2022.
    #DL
    [openreview], [code]

33. 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.
    #DL
    [arXiv], [code]

2021

10. MLMLM: Link Prediction with Mean Likelihood Masked Language Model
    Louis Clouâtre, Philippe Trempe, Amal Zouaq, and Sarath Chandar
    Findings of the Association for Computational Linguistics (ACL-IJCNLP), 2021.
    #NLP
    [arXiv]

11. 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.
    #NLP
    [openreview], [code]

12. 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.
    #NLP
    

13. 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.
    #NLP
    

14. 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.
    #RL
    [arXiv], [code]

15. 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 the Association for Computational Linguistics (ACL-IJCNLP), 2021.
    #NLP
    [arXiv]

16. 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 (AAAI), 2021.
    #RL
    

17. IIRC: Incremental Implicitly-Refined Classification
    Mohamed Abdelsalam, Mojtaba Faramarzi, Shagun Sodhani, and Sarath Chandar
    Conference on Computer Vision and Pattern Recognition (CVPR), 2021.
    #DL
    [arXiv], [code], [website], [PyPI], [docs]

2020

2. 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.
   #RL
   [arXiv], [code]

3. 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.
   #RL
   [arXiv]