I am a Ph.D. student at MILA & Université de Montréal, advised by Ioannis Mitliagkas. Prior to joining MILA, I was a Research Fellow at Microsoft Research Lab India, where I worked with Amit Sharma on Machine Learning and Causal Inference. I had completed my undergraduate double major program in Mathematics and Computer Science from the Indian Institute of Technology, Kanpur.
My research focus is on learning representations where we can provably identify/recover the latent factors from observations, which falls into the area of disentanglement/object-centric learning or more broadly causal representation learning. I also work on treatment effect estimation (causal inference) and trustworthy machine learning, where I have specifically tackled problems in out-of-distribution generalization, privacy robustness, and explainability of machine learning models.
Research Interests:
Causal Representation Learning | Trustworthy Machine Learning
Publications
Causal Representation Learning & Out-of-Distribution Generalization
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Domain Generalization using Causal Matching
Divyat Mahajan, Shruti Tople, Amit Sharma
ICML 2021 (Oral)
[arxiv]
[code]
[talk]
[presentation]
[poster]
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Additive Decoders for Latent Variables Identification and Cartesian-Product Extrapolation
Sébastien Lachapelle*, Divyat Mahajan*, Ioannis Mitliagkas, Simon Lacoste-Julien
NeurIPS 2023 (Oral)
[arxiv]
[code]
[poster]
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Interventional Causal Representation Learning
Kartik Ahuja, Divyat Mahajan, Yixin Wang, Yoshua Bengio
ICML 2023 (Oral)
[arxiv]
[code]
[talk]
[presentation]
[poster]
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Synergies Between Disentanglement and Sparsity: a Multi-Task Learning Perspective
Sébastien Lachapelle*, Tristan Deleu*, Divyat Mahajan, Ioannis Mitliagkas, Yoshua Bengio, Simon Lacoste-Julien, Quentin Bertrand
ICML 2023
[arxiv]
[code]
[talk]
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Towards efficient representation identification in supervised learning
Kartik Ahuja*, Divyat Mahajan*, Vasilis Syrgkanis, Ioannis Mitliagkas
CleaR 2022
[arxiv]
[code]
[presentation]
Causal Inference
Explainable Machine Learning
Software
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RobustDG
Toolkit for Building Robust ML models that generalize to unseen domains | Github | Microsoft