Kiran Vodrahalli

According to our database, Kiran Vodrahalli authored at least 17 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

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, Siamak Shakeri, Roopali Vij, Harsh Mehta, Vinay V. Ramasesh, Quoc Le, Ed H. Chi, Yifeng Lu, Orhan Firat, Angeliki Lazaridou, Jean-Baptiste Lespiau, Nithya Attaluri, Kate Olszewska

CoRR, 2024

Online Learning with Bounded Recall.

Jon Schneider, Kiran Vodrahalli

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Is Learning in Games Good for the Learners?

William Brown, Jon Schneider, Kiran Vodrahalli

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

Resource-Efficient Methods in Machine Learning

Kiran Vodrahalli

PhD thesis, 2022

History-Restricted Online Learning.

Jon Schneider, Kiran Vodrahalli

CoRR, 2022

Algorithms for Efficiently Learning Low-Rank Neural Networks.

Kiran Vodrahalli, Rakesh Shivanna, Maheswaran Sathiamoorthy, Sagar Jain, Ed H. Chi

CoRR, 2022

2021

Learning and planning with logical automata.

Brandon Araki, Kiran Vodrahalli, Thomas Leech, Cristian-Ioan Vasile, Mark Donahue, Daniela Rus

Auton. Robots, 2021

The Platform Design Problem.

Christos Harilaos Papadimitriou, Kiran Vodrahalli, Mihalis Yannakakis

Proceedings of the Web and Internet Economics - 17th International Conference, 2021

The Logical Options Framework.

Brandon Araki, Xiao Li, Kiran Vodrahalli, Jonathan A. DeCastro, Micah J. Fry, Daniela Rus

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Deep Bayesian Nonparametric Learning of Rules and Plans from Demonstrations with a Learned Automaton Prior.

Brandon Araki, Kiran Vodrahalli, Thomas Leech, Cristian Ioan Vasile, Mark Donahue, Daniela Rus

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

Privacy accounting and quality control in the sage differentially private ML platform.

Mathias Lécuyer, Riley Spahn, Kiran Vodrahalli, Roxana Geambasu, Daniel Hsu

Proceedings of the 27th ACM Symposium on Operating Systems Principles, 2019

Learning to Plan with Logical Automata.

Brandon Araki, Kiran Vodrahalli, Thomas Leech, Cristian Ioan Vasile, Mark Donahue, Daniela Rus

Proceedings of the Robotics: Science and Systems XV, 2019

Attribute-efficient learning of monomials over highly-correlated variables.

Alexandr Andoni, Rishabh Dudeja, Daniel Hsu, Kiran Vodrahalli

Proceedings of the Algorithmic Learning Theory, 2019

2018

Mapping between fMRI responses to movies and their natural language annotations.

Kiran Vodrahalli, Po-Hsuan Chen, Yingyu Liang, Christopher Baldassano, Janice Chen, Esther Yong, Christopher J. Honey, Uri Hasson, Peter J. Ramadge, Kenneth A. Norman, Sanjeev Arora

NeuroImage, 2018

A Large Self-Annotated Corpus for Sarcasm.

Mikhail Khodak, Nikunj Saunshi, Kiran Vodrahalli

Proceedings of the Eleventh International Conference on Language Resources and Evaluation, 2018

A Compressed Sensing View of Unsupervised Text Embeddings, Bag-of-n-Grams, and LSTMs.

Sanjeev Arora, Mikhail Khodak, Nikunj Saunshi, Kiran Vodrahalli

Proceedings of the 6th International Conference on Learning Representations, 2018

2016

Mapping Between Natural Movie fMRI Responses and Word-Sequence Representations.

Kiran Vodrahalli, Po-Hsuan Chen, Yingyu Liang, Janice Chen, Esther Yong, Christopher J. Honey, Peter J. Ramadge, Kenneth A. Norman, Sanjeev Arora

CoRR, 2016