Can Credit Increase Revenue? - Nishanth Dikkala; Éva Tardos (WINE, 2013)

Brief Announcement: Effect of Strategic Grading and Early Offers in Matching Markets. - Hedyeh Beyhaghi; Nishanth Dikkala; Éva Tardos (SAGT, 2015)

Effect of Strategic Grading and Early Offers in Matching Markets. - Hedyeh Beyhaghi; Nishanth Dikkala; Éva Tardos (CoRR, 2015)

Tight Hardness Results for Maximum Weight Rectangles. - Arturs Backurs; Nishanth Dikkala; Christos Tzamos (ICALP, 2016)

Concentration of Multilinear Functions of the Ising Model with Applications to Network Data. - Constantinos Daskalakis; Nishanth Dikkala; Gautam Kamath 001 (NIPS, 2017)

Testing from One Sample: Is the casino really using a riffle shuffle? - Constantinos Daskalakis; Nishanth Dikkala; Nick Gravin (CoRR, 2017)

Testing Ising Models. - Constantinos Daskalakis; Nishanth Dikkala; Gautam Kamath 001 (Electron. Colloquium Comput. Complex., 2017)

Testing Symmetric Markov Chains From a Single Trajectory. - Constantinos Daskalakis; Nishanth Dikkala; Nick Gravin (COLT, 2018)

HOGWILD!-Gibbs can be PanAccurate. - Constantinos Daskalakis; Nishanth Dikkala; Siddhartha Jayanti (NeurIPS, 2018)

Learning from Weakly Dependent Data under Dobrushin's Condition. - Yuval Dagan; Constantinos Daskalakis; Nishanth Dikkala; Siddhartha Jayanti (COLT, 2019)

From Soft Classifiers to Hard Decisions: How fair can we be? - Ran Canetti; Aloni Cohen; Nishanth Dikkala; Govind Ramnarayan; Sarah Scheffler; Adam D. Smith 001 (FAT, 2019)

Regression from dependent observations. - Constantinos Daskalakis; Nishanth Dikkala; Ioannis Panageas (STOC, 2019)

Logistic regression with peer-group effects via inference in higher-order Ising models. - Constantinos Daskalakis; Nishanth Dikkala; Ioannis Panageas (AISTATS, 2020)

Minimax Estimation of Conditional Moment Models. - Nishanth Dikkala; Greg Lewis; Lester Mackey; Vasilis Syrgkanis (NeurIPS, 2020)

Estimating Ising Models from One Sample. - Yuval Dagan; Constantinos Daskalakis; Nishanth Dikkala; Anthimos Vardis Kandiros (CoRR, 2020)

Statistical Estimation from Dependent Data. - Anthimos Vardis Kandiros; Yuval Dagan; Nishanth Dikkala; Surbhi Goel; Constantinos Daskalakis (ICML, 2021)

Learning Ising models from one or multiple samples. - Yuval Dagan; Constantinos Daskalakis; Nishanth Dikkala; Anthimos Vardis Kandiros (STOC, 2021)

For Manifold Learning, Deep Neural Networks can be Locality Sensitive Hash Functions. - Nishanth Dikkala; Gal Kaplun; Rina Panigrahy (CoRR, 2021)

Do More Negative Samples Necessarily Hurt In Contrastive Learning? - Pranjal Awasthi; Nishanth Dikkala; Pritish Kamath (ICML, 2022)

A Theoretical View on Sparsely Activated Networks. - Cenk Baykal; Nishanth Dikkala; Rina Panigrahy; Cyrus Rashtchian; Xin Wang 016 (NeurIPS, 2022)

Sketching based Representations for Robust Image Classification with Provable Guarantees. - Nishanth Dikkala; Sankeerth Rao Karingula; Raghu Meka; Jelani Nelson; Rina Panigrahy; Xin Wang 016 (NeurIPS, 2022)

On the Benefits of Learning to Route in Mixture-of-Experts Models. - Nishanth Dikkala; Nikhil Ghosh; Raghu Meka; Rina Panigrahy; Nikhil Vyas 001; Xin Wang 016 (EMNLP, 2023)

Alternating Updates for Efficient Transformers. - Cenk Baykal; Dylan J. Cutler; Nishanth Dikkala; Nikhil Ghosh; Rina Panigrahy; Xin Wang 116 (NeurIPS, 2023)

Alternating Updates for Efficient Transformers. - Cenk Baykal; Dylan J. Cutler; Nishanth Dikkala; Nikhil Ghosh; Rina Panigrahy; Xin Wang 016 (CoRR, 2023)

The Power of External Memory in Increasing Predictive Model Capacity. - Cenk Baykal; Dylan J. Cutler; Nishanth Dikkala; Nikhil Ghosh; Rina Panigrahy; Xin Wang 016 (CoRR, 2023)

Learning Neural Networks with Sparse Activations. - Pranjal Awasthi; Nishanth Dikkala; Pritish Kamath; Raghu Meka (COLT, 2024)

ReMI: A Dataset for Reasoning with Multiple Images. - Mehran Kazemi; Nishanth Dikkala; Ankit Anand; Petar Devic; Ishita Dasgupta 001; Fangyu Liu; Bahare Fatemi; Pranjal Awasthi; Sreenivas Gollapudi; Dee Guo; Ahmed Qureshi (NeurIPS, 2024)

Causal language modeling can elicit search and reasoning capabilities on logic puzzles. - Kulin Shah; Nishanth Dikkala; Xin Wang; Rina Panigrahy (NeurIPS, 2024)

Causal Language Modeling Can Elicit Search and Reasoning Capabilities on Logic Puzzles. - Kulin Shah; Nishanth Dikkala; Xin Wang 016; Rina Panigrahy (CoRR, 2024)

Michelangelo: Long Context Evaluations Beyond Haystacks via Latent Structure Queries. - Kiran Vodrahalli; Santiago Ontanon; Nilesh Tripuraneni; Kelvin Xu; Sanil Jain; Rakesh Shivanna; Jeffrey Hui; Nishanth Dikkala; Mehran Kazemi; Bahare Fatemi; Rohan Anil; Ethan Dyer; et al. (CoRR, 2024)

How Transformers Solve Propositional Logic Problems: A Mechanistic Analysis. - Guanzhe Hong; Nishanth Dikkala; Enming Luo; Cyrus Rashtchian; Xin Wang 016; Rina Panigrahy (CoRR, 2024)

BIG-Bench Extra Hard. - Mehran Kazemi; Bahare Fatemi; Hritik Bansal; John Palowitch; Chrysovalantis Anastasiou; Sanket Vaibhav Mehta; Lalit K. Jain; Virginia Aglietti; Disha Jindal; Peter Chen; Nishanth Dikkala; Gladys Tyen; et al. (ACL (1), 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)

BIG-Bench Extra Hard. - Mehran Kazemi; Bahare Fatemi; Hritik Bansal; John Palowitch; Chrysovalantis Anastasiou; Sanket Vaibhav Mehta; Kshitij Jain 001; Virginia Aglietti; Disha Jindal; Peter Chen; Nishanth Dikkala; Gladys Tyen; et al. (CoRR, 2025)