About me:

I am a fourth-year PhD student at University of Washington Seattle where I am working with Prof. Jeffery A. Bilmes. I like to work on improving the data efficiency of large-scale models, for both training and inference; with the help of submodular optimization. In Summer'25, I am interning at NVIDIA - Applied Deep Learning Research working with Mohammad Shoeybi and Mostafa Patwary. In summer'24 I did an internship at NVIDIA - Applied Deep Learning Research Team with Rafael Valle and Kevin Shih.  

Before joining PhD program, I completed my undergraduate degree at IIT Delhi, where I worked with Dr Sumeet Agarwal and Dr Prathosh AP.  I'm also an avid photographer (Checkout my Unsplash) and hiker! When not working, I can be found messing around with my camera, google earth, or hiking in North Cascades National Park. 

Submodular Distribution Matching w/ Sparse Features for Multimodal Data Filtering

Arnav Das*, Gantavya Bhatt*, Yiping Wang, Viswa Virinchi, Sahil Verma, Simon Shaolei Du, Jeff Bilmes

Under Review

How Many Van Goghs Does It Take to Van Gogh? Finding the Imitation Threshold

Sahil Verma, Royi Rassin, Arnav Das*, Gantavya Bhatt*, Preethi Seshadri*, Chirag Shah, Jeff Bilmes, Hannaneh Hajishirzi, Yanai Elazar

[Invited Talk] RegML workshop at NeurIPS 2024

[Poster] Accepted at ATTRIB, RegML, and SafeGenAI workshops at NeurIPS 2024 and NLLP Workshop 2024

Under Review

pdf/code

COBRA: COmBinatorial Retrieval Augmentation for Few-Shot Learning

Arnav Das*, Gantavya Bhatt*, Lilly Kumari, Sahil Verma, Jeff Bilmes

[Poster] In Conference on Computer Vision and Pattern Recognition (CVPR'25)

[Poster] In DMLR workshop at ICML'24

arxiv

Comparing Bad Apples to Good Oranges: Aligning Large Language Models via Joint Preference Optimization

Hritik Bansal*, Ashima Suvarna*, Gantavya Bhatt*, Nanyun Peng, Kai-Wei Chang, Aditya Grover

[Poster] Findings of ACL'25. 

[Oral] In DMLR workshop at ICML'24

[Poster] In MHFAI Alignment workshop at ICML'24

arxiv/openreview (DMLR)/openreview (MHFAI)/code

Deep Submodular Peripteral Networks

Gantavya Bhatt*, Arnav Das*, Jeff Bilmes

[Spotlight] In Neural Information Processing Systems (NeurIPS'24)

arxiv

An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models

Gantavya Bhatt*, Yifang Chen*, Arnav Das*, Jifan Zhang*, Sang Truong, Stephen Mussmann, Yinglun Zhu, Jeff Bilmes, Simon Shaolei Du, Kevin Jamieson, Jordan P Ash, Robert D Nowak

[Poster] Accepted at ACL'24 (Findings)

arxiv

Effective Backdoor Mitigation Depends on the Pre-training Objective

Sahil Verma, Gantavya Bhatt, Avi Schwarzschild, Soumye Singhal, Arnav Das, Chirag Shah, John P Dickerson, Jeff Bilmes

In Transactions of Machine Learning Research (Jan'25 edition, TMLR) 

[Best Paper Award 🏆] In BUGS workshop at NeurIPS'23 

Under review

pdf / arxiv 

LabelBench: A Comprehensive Framework for Benchmarking Label-Efficient Learning

Jifan Zhang*, Yifang Chen*, Gregory Canal, Arnav Das†, Gantavya Bhatt†, Stephen Mussmann, Yinglun Zhu, Jeff Bilmes, Simon Shaolei Du, Kevin Jamieson, Robert D Nowak

In Adaptive Experimental Design and Active Learning in the Real World workshop at NeurIPS'23.

Accepted at DMLR'24

pdf / arxiv / code

Accelerating Batch Active Learning Using Continual Learning Techniques

Arnav Das, Gantavya Bhatt*, Megh Manoj Bhalerao, Vianne R. Gao, Rui Yang, Jeff Bilmes

In Transactions of Machine Learning Research (Dec edition, TMLR) 

In DMLR workshop at ICML'23

pdf / arxiv / code

RadarHD: Demonstrating Lidar-like Point Clouds from mmWave Radar

Akarsh Prabhakara, Tao Jin, Arnav Das*, Gantavya Bhatt*, Lilly Kumari, Elahe Soltanaghei, Jeff Bilmes, Swarun Kumar, Anthony Rowe

In Annual International Conference On Mobile Computing And Networking ACM MobiCom '23

pdf / arxiv / code 

High Resolution Point Clouds from mmWave Radar

Akarsh Prabhakara, Tao Jin, Arnav Das*, Gantavya Bhatt*, Lilly Kumari, Elahe Soltanaghei, Jeff Bilmes, Swarun Kumar, Anthony Rowe

In IEEE International Conference on Robotics and Automation (ICRA'23) 

pdf / arxiv / code 

Matryoshka Representation Learning

Aditya Kusupati*, Gantavya Bhatt*, Aniket Rege*, Matthew Wallingford, Aditya Sinha, Vivek Ramanujan, William Howard-Snyder, Kaifeng Chen, Sham Kakade, Prateek Jain, and Ali Farhadi

[Poster] In Neural Information Processing Systems (NeurIPS'22)

pdf / arXiv / code

Tighter m-DPP Coreset Sample Complexity Bounds

Gantavya Bhatt, Jeff Bilmes

[Oral] In SubsetML workshop at International Conference of Machine Learning (ICML'21)

pdf /  arXiv

Systematic Generalization in Neural Networks-based Multivariate Time Series Forecasting Models

Hritik Bansal*, Gantavya Bhatt*, Pankaj Malhotra and Prathosh AP

In International joint Conference on Neural Networks (IJCNN'21)

pdf / arXiv / code

Can RNNs trained on harder subject-verb agreement instances still perform well on easier ones?

Hritik Bansal*, Gantavya Bhatt* and Sumeet Agarwal

In Proceedings of the Society for Computation in Linguistics: Vol. 4 , Article 38. 

pdf / arXiv / code

How much complexity does an RNN architecture need to learn syntax-sensitive dependencies?

Gantavya Bhatt*, Hritik Bansal*, Rishubh Singh* and Sumeet Agarwal

In Proceedings of the Society for Computation in Linguistics: Vol. 4 , Article 38. 

pdf / arXiv / code