A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors. - Mikhail Khodak; Nikunj Saunshi; Yingyu Liang; Tengyu Ma 001; Brandon Stewart; Sanjeev Arora (ACL (1), 2018)

A Compressed Sensing View of Unsupervised Text Embeddings, Bag-of-n-Grams, and LSTMs. - Sanjeev Arora; Mikhail Khodak; Nikunj Saunshi; Kiran Vodrahalli (ICLR (Poster), 2018)

A Large Self-Annotated Corpus for Sarcasm. - Mikhail Khodak; Nikunj Saunshi; Kiran Vodrahalli (LREC, 2018)

A Theoretical Analysis of Contrastive Unsupervised Representation Learning. - Nikunj Saunshi; Orestis Plevrakis; Sanjeev Arora; Mikhail Khodak; Hrishikesh Khandeparkar (ICML, 2019)

Provable Representation Learning for Imitation Learning via Bi-level Optimization. - Sanjeev Arora; Simon S. Du; Sham M. Kakade; Yuping Luo; Nikunj Saunshi (ICML, 2020)

A Sample Complexity Separation between Non-Convex and Convex Meta-Learning. - Nikunj Saunshi; Yi Zhang 074; Mikhail Khodak; Sanjeev Arora (ICML, 2020)

A Mathematical Exploration of Why Language Models Help Solve Downstream Tasks. - Nikunj Saunshi; Sadhika Malladi; Sanjeev Arora (ICLR, 2021)

A Representation Learning Perspective on the Importance of Train-Validation Splitting in Meta-Learning. - Nikunj Saunshi; Arushi Gupta; Wei Hu (ICML, 2021)

Predicting What You Already Know Helps: Provable Self-Supervised Learning. - Jason D. Lee; Qi Lei; Nikunj Saunshi; Jiacheng Zhuo (NeurIPS, 2021)

On Predicting Generalization using GANs. - Yi Zhang 074; Arushi Gupta; Nikunj Saunshi; Sanjeev Arora (ICLR, 2022)

Understanding Contrastive Learning Requires Incorporating Inductive Biases. - Nikunj Saunshi; Jordan T. Ash; Surbhi Goel; Dipendra Misra; Cyril Zhang; Sanjeev Arora; Sham M. Kakade; Akshay Krishnamurthy (ICML, 2022)

New Definitions and Evaluations for Saliency Methods: Staying Intrinsic, Complete and Sound. - Arushi Gupta; Nikunj Saunshi; Dingli Yu; Kaifeng Lyu; Sanjeev Arora (NeurIPS, 2022)

Reasoning in Large Language Models Through Symbolic Math Word Problems. - Vedant Gaur; Nikunj Saunshi (ACL (Findings), 2023)

Understanding Influence Functions and Datamodels via Harmonic Analysis. - Nikunj Saunshi; Arushi Gupta; Mark Braverman; Sanjeev Arora (ICLR, 2023)

Task-Specific Skill Localization in Fine-tuned Language Models. - Abhishek Panigrahi; Nikunj Saunshi; Haoyu Zhao; Sanjeev Arora (ICML, 2023)

Can Looped Transformers Learn to Implement Multi-step Gradient Descent for In-context Learning? - Khashayar Gatmiry; Nikunj Saunshi; Sashank J. Reddi; Stefanie Jegelka; Sanjiv Kumar (ICML, 2024)

On the Inductive Bias of Stacking Towards Improving Reasoning. - Nikunj Saunshi; Stefani Karp; Shankar Krishnan; Sobhan Miryoosefi; Sashank Jakkam Reddi; Sanjiv Kumar (NeurIPS, 2024)

Landscape-Aware Growing: The Power of a Little LAG. - Stefani Karp; Nikunj Saunshi; Sobhan Miryoosefi; Sashank J. Reddi; Sanjiv Kumar (CoRR, 2024)

A Little Help Goes a Long Way: Efficient LLM Training by Leveraging Small LMs. - Ankit Singh Rawat; Veeranjaneyulu Sadhanala; Afshin Rostamizadeh; Ayan Chakrabarti; Wittawat Jitkrittum; Vladimir Feinberg; Seungyeon Kim 001; Hrayr Harutyunyan; Nikunj Saunshi; Zachary Nado; Rakesh Shivanna; Sashank J. Reddi; et al. (CoRR, 2024)

On the Role of Depth and Looping for In-Context Learning with Task Diversity. - Khashayar Gatmiry; Nikunj Saunshi; Sashank J. Reddi; Stefanie Jegelka; Sanjiv Kumar (CoRR, 2024)

Efficient stagewise pretraining via progressive subnetworks. - Abhishek Panigrahi; Nikunj Saunshi; Kaifeng Lyu; Sobhan Miryoosefi; Sashank J. Reddi; Satyen Kale; Sanjiv Kumar (ICLR, 2025)

Reasoning with Latent Thoughts: On the Power of Looped Transformers. - Nikunj Saunshi; Nishanth Dikkala; Zhiyuan Li 005; Sanjiv Kumar; Sashank J. Reddi (ICLR, 2025)

StagFormer: Time Staggering Transformer Decoding for RunningLayers In Parallel. - Dylan J. Cutler; Arun Kandoor; Nishanth Dikkala; Nikunj Saunshi; Xin Wang; Rina Panigrahy (CoRR, 2025)

Learning to Keep a Promise: Scaling Language Model Decoding Parallelism with Learned Asynchronous Decoding. - Tian Jin; Ellie Y. Cheng; Zachary Ankner; Nikunj Saunshi; Blake M. Elias; Amir Yazdanbakhsh; Jonathan Ragan-Kelley; Suvinay Subramanian; Michael Carbin (CoRR, 2025)

Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities. - Gheorghe Comanici; Eric Bieber; Mike Schaekermann; Ice Pasupat; Noveen Sachdeva; Inderjit S. Dhillon; Marcel Blistein; Ori Ram; Dan Zhang; Evan Rosen; Luke Marris; Sam Petulla; et al. (CoRR, 2025)