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Divyat Mahajan
Divyat Mahajan
- Ph.D. Candidate, Mila
- Visiting Researcher, Meta FAIR
I am a final year Ph.D. candidate at Mila & Université de Montréal, advised by Ioannis Mitliagkas.
Currently, I am a visiting researcher at Meta FAIR working on pretraining large language models with the memory and generalization team.
My research interests can be outlined broadly as follows.
Prior to starting my Ph.D., I was a research fellow at Microsoft Research India, where I worked with Amit Sharma on trustworthy machine Learning from the lens of causality, specifically on out-of-distribution generalization, privacy, and explainable machine learning. Earlier, I completed my undergraduate in Mathematics and Computer Science from the Indian Institute of Technology, Kanpur.
- Learning algorithms for efficient adaptation under distribution shifts and robustness to spurious correlations.
- Disentangled (causal) representation learning for sample efficient generalization and interpretability.
- In-context learning and prior-fitted networks for probabilistic (causal) inference.
Prior to starting my Ph.D., I was a research fellow at Microsoft Research India, where I worked with Amit Sharma on trustworthy machine Learning from the lens of causality, specifically on out-of-distribution generalization, privacy, and explainable machine learning. Earlier, I completed my undergraduate in Mathematics and Computer Science from the Indian Institute of Technology, Kanpur.
Select Publications & Preprints
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Beyond Multi-Token Prediction: Pretraining LLMs with Future Summaries
Divyat Mahajan, Sachin Goyal, Badr Youbi Idrissi, Mohammad Pezeshki, Ioannis Mitliagkas, David Lopez-Paz, Kartik Ahuja
Preprint. Under Review. -
Amortized Inference of Causal Models via Conditional Fixed-Point Iterations
Divyat Mahajan*, Jannes Gladrow, Agrin Hilmkil, Cheng Zhang, Meyer Scetbon*
TMLR 2025 (J2C Certification) [arxiv] [code] -
Compositional Risk Minimization
Divyat Mahajan, Mohammad Pezeshki, Charles Arnal, Ioannis Mitliagkas, Kartik Ahuja, Pascal Vincent
ICML 2025 [arxiv] [code] [presentation] [poster] [twitter] -
Empirical Analysis of Model Selection for Heterogeneous Causal Effect Estimation
Divyat Mahajan, Ioannis Mitliagkas, Brady Neal, Vasilis Syrgkanis
ICLR 2024 (Spotlight) [arxiv] [code] [presentation] [poster] [twitter] -
Additive Decoders for Latent Variables Identification and Cartesian-Product Extrapolation
Sébastien Lachapelle*, Divyat Mahajan*, Ioannis Mitliagkas, Simon Lacoste-Julien
NeurIPS 2023 (Oral)
[arxiv] [code] [blog] [talk(conference)] [talk(reading group)] [presentation] [poster] -
Interventional Causal Representation Learning
Kartik Ahuja, Divyat Mahajan, Yixin Wang, Yoshua Bengio
ICML 2023 (Oral)
[arxiv] [code] [talk] [presentation] [poster] -
Towards efficient representation identification in supervised learning
Kartik Ahuja*, Divyat Mahajan*, Vasilis Syrgkanis, Ioannis Mitliagkas
CleaR 2022
[arxiv] [code] [talk] [presentation] [poster] -
Domain Generalization using Causal Matching
Divyat Mahajan, Shruti Tople, Amit Sharma
ICML 2021 (Oral) [arxiv] [code] [talk] [presentation] [poster] -
Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers
Divyat Mahajan, Chenhao Tan, Amit Sharma
CausalML@NeurIPS 2019 (Oral) [arxiv] [code] [talk] [presentation] [poster]
Select Awards & Honours
- Outstanding Reviewer: ICML 2022 , ML RC 2021 , ML RC 2022
- Top Reviewer: NeurIPS 2022 , NeurIPS 2024
- FRQNT Doctoral Scholarship: Competition 2024-25
- Academic Excellence Award, IIT Kanpur: Session 2017-18
Software
- RobustDG Toolkit for Building Robust ML models that generalize to unseen domains | Github | Microsoft
- Diverse Counterfactual Explanations (DiCE) for ML Toolkit to generate truthful explainations for machine learning models | Github | InterpretML
