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Arpit Bansal
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Cold diffusion: Inverting arbitrary image transforms without noise
A Bansal, E Borgnia, HM Chu, JS Li, H Kazemi, F Huang, M Goldblum, ...
Advances in Neural Information Processing Systems (NeurIPS), 2023
1672023
Universal guidance for diffusion models
A Bansal, HM Chu, A Schwarzschild, S Sengupta, M Goldblum, J Geiping, ...
The Twelfth International Conference on Learning Representations (ICLR) 2024, 2024
67*2024
Universal guidance for diffusion models
A Bansal, HM Chu, A Schwarzschild, S Sengupta, M Goldblum, J Geiping, ...
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023
502023
Transfer learning with deep tabular models
R Levin, V Cherepanova, A Schwarzschild, A Bansal, CB Bruss, ...
arXiv preprint arXiv:2206.15306, 2022
49*2022
Can neural nets learn the same model twice? investigating reproducibility and double descent from the decision boundary perspective
G Somepalli, L Fowl, A Bansal, P Yeh-Chiang, Y Dar, R Baraniuk, ...
Proceedings of the ieee/cvf conference on computer vision and pattern …, 2022
452022
Preventing unauthorized use of proprietary data: Poisoning for secure dataset release
L Fowl, P Chiang, M Goldblum, J Geiping, A Bansal, W Czaja, T Goldstein
arXiv preprint arXiv:2103.02683, 2021
352021
Certified neural network watermarks with randomized smoothing
A Bansal, P Chiang, MJ Curry, R Jain, C Wigington, V Manjunatha, ...
International Conference on Machine Learning, 1450-1465, 2022
322022
End-to-end algorithm synthesis with recurrent networks: Extrapolation without overthinking
A Bansal, A Schwarzschild, E Borgnia, Z Emam, F Huang, M Goldblum, ...
Advances in Neural Information Processing Systems 35, 20232-20242, 2022
31*2022
Canary in a Coalmine: Better Membership Inference with Ensembled Adversarial Queries
Y Wen, A Bansal, H Kazemi, E Borgnia, M Goldblum, J Geiping, ...
International Conference on Learning Representations (ICLR) 2023, 2023
172023
Loss landscapes are all you need: Neural network generalization can be explained without the implicit bias of gradient descent
P Chiang, R Ni, DY Miller, A Bansal, J Geiping, M Goldblum, T Goldstein
The Eleventh International Conference on Learning Representations, 2022
142022
Datasets for studying generalization from easy to hard examples
A Schwarzschild, E Borgnia, A Gupta, A Bansal, Z Emam, F Huang, ...
arXiv preprint arXiv:2108.06011, 2021
62021
MetaBalance: high-performance neural networks for class-imbalanced data
A Bansal, M Goldblum, V Cherepanova, A Schwarzschild, CB Bruss, ...
arXiv preprint arXiv:2106.09643, 2021
52021
Pag-net: Progressive attention guided depth super-resolution network
A Bansal, S Jonna, RR Sahay
arXiv preprint arXiv:1911.09878, 2019
22019
Generating Potent Poisons and Backdoors from Scratch with Guided Diffusion
H Souri, A Bansal, H Kazemi, L Fowl, A Saha, J Geiping, AG Wilson, ...
arXiv preprint arXiv:2403.16365, 2024
2024
Just How Flexible are Neural Networks in Practice?
R Shwartz-Ziv, M Goldblum, A Bansal, CB Bruss, Y LeCun, AG Wilson
2023
Algorithm Design for Learned Algorithms
A Schwarzschild, SM McLeish, A Bansal, G Diaz, A Stein, A Chandnani, ...
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