Publications by Sarath Chandar

• 

  Sarath Chandar

Activity

• Principal Investigator: Jan 2020 - now

Preprints

• Protein Language Models: Is Scaling Necessary?
  Quentin Fournier, Robert M. Vernon, Almer van der Sloot, Benjamin Schulz, Sarath Chandar, and Christopher James Langmead
  In bioRxiv, 2024.
  #DL, #Other
  [bioRxiv], [code]

• Exploring the Plasticity of Neural Network for NLP Tasks in Continual Learning
  Maryam Hashemzadeh, Pranshu Malviya*, Darshan Patil*, and Sarath Chandar
  Conference on Lifelong Learning Agents (CoLLAs) workshop, 2024.
  #DL, #NLP
  

• BindGPT: A Scalable Framework for 3D Molecular Design via Language Modeling and Reinforcement Learning
  Artem Zholus, Maksim Kuznetsov, Roman Schutski, Rim Shayakhmetov, Daniil Polykovskiy, Sarath Chandar, and Alex Zhavoronkov
  In arXiv, 2024.
  #DL, #RL
  [arXiv], [website]

• Predicting the Impact of Model Expansion through the Minima Manifold: A Loss Landscape Perspective
  Pranshu Malviya, Jerry Huang, Quentin Fournier, and Sarath Chandar
  In ArXiv, 2024.
  #DL
  [arXiv]

• Interpretability Needs a New Paradigm
  Andreas Madsen, Himabindu Lakkaraju, Siva Reddy, and Sarath Chandar
  In ArXiv, 2024.
  #NLP, #DL, #Other
  [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.
  #DL
  [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, 2022.
  [Edge Intelligence Workshop (EIW), 2022]
  #DL
  [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.
  #DL
  [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.
  #RL
  [arXiv]

Conference and Journal Papers

2024

48. 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
    

49. 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]

50. 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]

51. 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
    

52. 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
    

53. 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]

54. 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]

55. 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]

56. 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]

57. 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]

58. 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]

59. 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]

60. 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]

61. 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
    

62. 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
    

63. 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]

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

65. 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]

66. 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]

67. 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

28. 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]

29. 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]

30. 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]

31. 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]

32. 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]

33. 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]

34. 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]

35. 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]

36. 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]

2022

19. 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
    

20. 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
    

21. 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]

22. 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]

23. 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
    

24. 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]

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

26. 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]

27. 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]

28. 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

9. 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]

10. 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
    

11. 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
    

12. 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]

13. 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]

14. 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
    

15. 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]