Universal Guidance for Diffusion Models - Arpit Bansal; Hong-Min Chu; Avi Schwarzschild; Soumyadip Sengupta; Micah Goldblum; Jonas Geiping; Tom Goldstein (The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024, 2024)

Transformers Can Do Arithmetic with the Right Embeddings - Sean McLeish; Arpit Bansal; Alex Stein; Neel Jain; John Kirchenbauer; Brian R. Bartoldson; Bhavya Kailkhura; Abhinav Bhatele; Jonas Geiping; Avi Schwarzschild; Tom Goldstein (Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, NeurIPS 2024, Vancouver, BC, Canada, December 10 - 15, 2024, 2024)

Generating Potent Poisons and Backdoors from Scratch with Guided Diffusion - Hossein Souri; Arpit Bansal; Hamid Kazemi; Liam Fowl; Aniruddha Saha; Jonas Geiping; Andrew Gordon Wilson; Rama Chellappa; Tom Goldstein; Micah Goldblum (CoRR, 2024)

Transformers Can Do Arithmetic with the Right Embeddings - Sean McLeish; Arpit Bansal; Alex Stein; Neel Jain; John Kirchenbauer; Brian R. Bartoldson; Bhavya Kailkhura; Abhinav Bhatele; Jonas Geiping; Avi Schwarzschild; Tom Goldstein (CoRR, 2024)

Just How Flexible are Neural Networks in Practice? - Ravid Shwartz-Ziv; Micah Goldblum; Arpit Bansal; C. Bayan Bruss; Yann LeCun; Andrew Gordon Wilson (CoRR, 2024)

Universal Guidance for Diffusion Models - Arpit Bansal; Hong-Min Chu; Avi Schwarzschild; Soumyadip Sengupta; Micah Goldblum; Jonas Geiping; Tom Goldstein (IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023 - Workshops, Vancouver, BC, Canada, June 17-24, 2023, 2023)

Loss Landscapes are All You Need: Neural Network Generalization Can Be Explained Without the Implicit Bias of Gradient Descent - Ping-yeh Chiang; Renkun Ni; David Yu Miller; Arpit Bansal; Jonas Geiping; Micah Goldblum; Tom Goldstein (The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023, 2023)

Transfer Learning with Deep Tabular Models - Roman Levin; Valeriia Cherepanova; Avi Schwarzschild; Arpit Bansal; C. Bayan Bruss; Tom Goldstein; Andrew Gordon Wilson; Micah Goldblum (The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023, 2023)

Canary in a Coalmine: Better Membership Inference with Ensembled Adversarial Queries - Yuxin Wen; Arpit Bansal; Hamid Kazemi; Eitan Borgnia; Micah Goldblum; Jonas Geiping; Tom Goldstein (The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023, 2023)

Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise - Arpit Bansal; Eitan Borgnia; Hong-Min Chu; Jie Li; Hamid Kazemi; Furong Huang; Micah Goldblum; Jonas Geiping; Tom Goldstein (Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023, 2023)

Universal Guidance for Diffusion Models - Arpit Bansal; Hong-Min Chu; Avi Schwarzschild; Soumyadip Sengupta; Micah Goldblum; Jonas Geiping; Tom Goldstein (CoRR, 2023)

Can Neural Nets Learn the Same Model Twice? Investigating Reproducibility and Double Descent from the Decision Boundary Perspective - Gowthami Somepalli; Liam Fowl; Arpit Bansal; Ping-Yeh Chiang; Yehuda Dar; Richard G. Baraniuk; Micah Goldblum; Tom Goldstein (IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022, New Orleans, LA, USA, June 18-24, 2022, 2022)

Certified Neural Network Watermarks with Randomized Smoothing - Arpit Bansal; Ping-Yeh Chiang; Michael J. Curry; Rajiv Jain; Curtis Wigington; Varun Manjunatha; John P. Dickerson; Tom Goldstein (International Conference on Machine Learning, ICML 2022, 17-23 July 2022, Baltimore, Maryland, USA, 2022)

End-to-end Algorithm Synthesis with Recurrent Networks: Extrapolation without Overthinking - Arpit Bansal; Avi Schwarzschild; Eitan Borgnia; Zeyad Emam; Furong Huang; Micah Goldblum; Tom Goldstein (Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, NeurIPS 2022, New Orleans, LA, USA, November 28 - December 9, 2022, 2022)

End-to-end Algorithm Synthesis with Recurrent Networks: Logical Extrapolation Without Overthinking - Arpit Bansal; Avi Schwarzschild; Eitan Borgnia; Zeyad Emam; Furong Huang; Micah Goldblum; Tom Goldstein (CoRR, 2022)

Can Neural Nets Learn the Same Model Twice? Investigating Reproducibility and Double Descent from the Decision Boundary Perspective - Gowthami Somepalli; Liam Fowl; Arpit Bansal; Ping-Yeh Chiang; Yehuda Dar; Richard G. Baraniuk; Micah Goldblum; Tom Goldstein (CoRR, 2022)

Transfer Learning with Deep Tabular Models - Roman Levin; Valeriia Cherepanova; Avi Schwarzschild; Arpit Bansal; C. Bayan Bruss; Tom Goldstein; Andrew Gordon Wilson; Micah Goldblum (CoRR, 2022)

Certified Neural Network Watermarks with Randomized Smoothing - Arpit Bansal; Ping-yeh Chiang; Michael J. Curry; Rajiv Jain; Curtis Wigington; Varun Manjunatha; John P. Dickerson; Tom Goldstein (CoRR, 2022)

Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise - Arpit Bansal; Eitan Borgnia; Hong-Min Chu; Jie S. Li; Hamid Kazemi; Furong Huang; Micah Goldblum; Jonas Geiping; Tom Goldstein (CoRR, 2022)

Canary in a Coalmine: Better Membership Inference with Ensembled Adversarial Queries - Yuxin Wen; Arpit Bansal; Hamid Kazemi; Eitan Borgnia; Micah Goldblum; Jonas Geiping; Tom Goldstein (CoRR, 2022)

Preventing Unauthorized Use of Proprietary Data: Poisoning for Secure Dataset Release - Liam Fowl; Ping-yeh Chiang; Micah Goldblum; Jonas Geiping; Arpit Bansal; Wojtek Czaja; Tom Goldstein (CoRR, 2021)

MetaBalance: High-Performance Neural Networks for Class-Imbalanced Data - Arpit Bansal; Micah Goldblum; Valeriia Cherepanova; Avi Schwarzschild; C. Bayan Bruss; Tom Goldstein (CoRR, 2021)

Datasets for Studying Generalization from Easy to Hard Examples - Avi Schwarzschild; Eitan Borgnia; Arjun Gupta; Arpit Bansal; Zeyad Emam; Furong Huang; Micah Goldblum; Tom Goldstein (CoRR, 2021)

PAG-Net: Progressive Attention Guided Depth Super-resolution Network - Arpit Bansal; Sankaraganesh Jonna; Rajiv R. Sahay (CoRR, 2019)

Product design methodology applied in developing a liquid petroleum gas level indicator using android technology - Vishwas Mahesh; Vinyas Mahesh; Ishwarkumar Teggi; Arpit Bansal; S. Manjesh (Ingénierie des Systèmes d Inf., 2018)

An efficient automatic intensity based method for detection of macula in retinal images - Arpit Bansal; Aashwin Vats; Akshita Jain; Malay Kishore Dutta; Radim Burget; Jiri Prinosil (39th International Conference on Telecommunications and Signal Processing, TSP 2016, Vienna, Austria, June 27-29, 2016, 2016)