Augmenters at SemEval-2023 Task 1: Enhancing CLIP in Handling Compositionality and Ambiguity for Zero-Shot Visual WSD through Prompt Augmentation and Text-To-Image Diffusion.

[BibT_eX]

[DOI]

Jie S. Li

Yow-Ting Shiue

Yong-Siang Shih

Jonas Geiping

CoRR, 2023

Bring Your Own Data! Self-Supervised Evaluation for Large Language Models.

[BibT_eX]

[DOI]

CoRR, 2023

Tree-Ring Watermarks: Fingerprints for Diffusion Images that are Invisible and Robust.

[BibT_eX]

[DOI]

CoRR, 2023

A Cookbook of Self-Supervised Learning.

[BibT_eX]

[DOI]

CoRR, 2023

JPEG Compressed Images Can Bypass Protections Against AI Editing.

[BibT_eX]

[DOI]

Pedro Sandoval Segura

Jonas Geiping

Tom Goldstein

CoRR, 2023

Augmenters at SemEval-2023 Task 1: Enhancing CLIP in Handling Compositionality and Ambiguity for Zero-Shot Visual WSD through Prompt Augmentation and Text-To-Image Diffusion.

[BibT_eX]

[DOI]

Proceedings of the The 17th International Workshop on Semantic Evaluation, 2023

Tree-Rings Watermarks: Invisible Fingerprints for Diffusion Images.

[BibT_eX]

[DOI]

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

Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery.

[BibT_eX]

[DOI]

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

Understanding and Mitigating Copying in Diffusion Models.

[BibT_eX]

[DOI]

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

On the Exploitability of Instruction Tuning.

[BibT_eX]

[DOI]

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

What Can We Learn from Unlearnable Datasets?

[BibT_eX]

[DOI]

Pedro Sandoval Segura

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

A Performance-Driven Benchmark for Feature Selection in Tabular Deep Learning.

[BibT_eX]

[DOI]

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

Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise.

[BibT_eX]

[DOI]

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

A Watermark for Large Language Models.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2023

Cramming: Training a Language Model on a single GPU in one day.

[BibT_eX]

[DOI]

Jonas Geiping

Tom Goldstein

Proceedings of the International Conference on Machine Learning, 2023

Canary in a Coalmine: Better Membership Inference with Ensembled Adversarial Queries.

[BibT_eX]

[DOI]

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Seeing in Words: Learning to Classify through Language Bottlenecks.

[BibT_eX]

[DOI]

Proceedings of the First Tiny Papers Track at ICLR 2023, 2023

How Much Data Are Augmentations Worth? An Investigation into Scaling Laws, Invariance, and Implicit Regularization.

[BibT_eX]

[DOI]

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models.

[BibT_eX]

[DOI]

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Panning for Gold in Federated Learning: Targeted Text Extraction under Arbitrarily Large-Scale Aggregation.

[BibT_eX]

[DOI]

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Loss Landscapes are All You Need: Neural Network Generalization Can Be Explained Without the Implicit Bias of Gradient Descent.

[BibT_eX]

[DOI]

Proceedings of the Eleventh International Conference on Learning Representations, 2023

STYX: Adaptive Poisoning Attacks Against Byzantine-Robust Defenses in Federated Learning.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Conference on Acoustics, 2023

Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Models.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

Universal Guidance for Diffusion Models.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022

K-SAM: Sharpness-Aware Minimization at the Speed of SGD.

[BibT_eX]

[DOI]

CoRR, 2022

Thinking Two Moves Ahead: Anticipating Other Users Improves Backdoor Attacks in Federated Learning.

[BibT_eX]

[DOI]

CoRR, 2022

Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise.

[BibT_eX]

[DOI]

CoRR, 2022

Autoregressive Perturbations for Data Poisoning.

[BibT_eX]

[DOI]

Pedro Sandoval Segura

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2022

Stochastic Training is Not Necessary for Generalization.

[BibT_eX]

[DOI]

Proceedings of the Tenth International Conference on Learning Representations, 2022

Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models.

[BibT_eX]

[DOI]

Proceedings of the Tenth International Conference on Learning Representations, 2022

Poisons that are learned faster are more effective.

[BibT_eX]

[DOI]

Pedro Sandoval Segura

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2022

A Simple Strategy to Provable Invariance via Orbit Mapping.

[BibT_eX]

[DOI]

Kanchana Vaishnavi Gandikota

Proceedings of the Computer Vision - ACCV 2022, 2022

2021

Modern optimization techniques in computer vision: from variational models to machine learning security.

[BibT_eX]

[DOI]

Jonas Geiping

PhD thesis, 2021

DARTS for Inverse Problems: a Study on Hyperparameter Sensitivity.

[BibT_eX]

[DOI]

CoRR, 2021

Training or Architecture? How to Incorporate Invariance in Neural Networks.

[BibT_eX]

[DOI]

Kanchana Vaishnavi Gandikota

CoRR, 2021

Preventing Unauthorized Use of Proprietary Data: Poisoning for Secure Dataset Release.

[BibT_eX]

[DOI]

CoRR, 2021

DP-InstaHide: Provably Defusing Poisoning and Backdoor Attacks with Differentially Private Data Augmentations.

[BibT_eX]

[DOI]

CoRR, 2021

What Doesn't Kill You Makes You Robust(er): Adversarial Training against Poisons and Backdoors.

[BibT_eX]

[DOI]

CoRR, 2021

Adversarial Examples Make Strong Poisons.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Witches' Brew: Industrial Scale Data Poisoning via Gradient Matching.

[BibT_eX]

[DOI]

Proceedings of the 9th International Conference on Learning Representations, 2021

Strong Data Augmentation Sanitizes Poisoning and Backdoor Attacks Without an Accuracy Tradeoff.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Conference on Acoustics, 2021

2020

MetaPoison: Practical General-purpose Clean-label Data Poisoning.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Inverting Gradients - How easy is it to break privacy in federated learning?

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Truth or backpropaganda? An empirical investigation of deep learning theory.

[BibT_eX]

[DOI]

Proceedings of the 8th International Conference on Learning Representations, 2020

Witchcraft: Efficient PGD Attacks with Random Step Size.

[BibT_eX]

[DOI]

Proceedings of the 2020 IEEE International Conference on Acoustics, 2020

Fast Convex Relaxations using Graph Discretizations.

[BibT_eX]

[DOI]

Proceedings of the 31st British Machine Vision Conference 2020, 2020

2019

Piecewise Rigid Scene Flow with Implicit Motion Segmentation.

[BibT_eX]

[DOI]

Andreas Görlitz

Jonas Geiping

Andreas Kolb

Proceedings of the 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2019

Parametric Majorization for Data-Driven Energy Minimization Methods.

[BibT_eX]

[DOI]

Jonas Geiping

Michael Moeller

Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, 2019

2018

Composite Optimization by Nonconvex Majorization-Minimization.

[BibT_eX]

[DOI]

Jonas Geiping

Michael Möller

SIAM J. Imaging Sci., 2018

2017

Multiframe Motion Coupling for Video Super Resolution.

[BibT_eX]

[DOI]

Proceedings of the Energy Minimization Methods in Computer Vision and Pattern Recognition, 2017

2016

Multiframe Motion Coupling via Infimal Convolution Regularization for Video Super Resolution.

[BibT_eX]

[DOI]

CoRR, 2016

Jonas Geiping

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