Jonas Geiping

According to our database1, Jonas Geiping authored at least 60 papers between 2016 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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Article 
PhD thesis 
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Links

On csauthors.net:

Bibliography

2024
Coercing LLMs to do and reveal (almost) anything.
CoRR, 2024

Spotting LLMs With Binoculars: Zero-Shot Detection of Machine-Generated Text.
CoRR, 2024

2023
Object Recognition as Next Token Prediction.
CoRR, 2023

A Simple and Efficient Baseline for Data Attribution on Images.
CoRR, 2023

Towards Possibilities & Impossibilities of AI-generated Text Detection: A Survey.
CoRR, 2023

NEFTune: Noisy Embeddings Improve Instruction Finetuning.
CoRR, 2023

Baseline Defenses for Adversarial Attacks Against Aligned Language Models.
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.
CoRR, 2023

Bring Your Own Data! Self-Supervised Evaluation for Large Language Models.
CoRR, 2023

On the Reliability of Watermarks for Large Language Models.
CoRR, 2023

Tree-Ring Watermarks: Fingerprints for Diffusion Images that are Invisible and Robust.
CoRR, 2023

A Cookbook of Self-Supervised Learning.
CoRR, 2023

JPEG Compressed Images Can Bypass Protections Against AI Editing.
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.
Proceedings of the The 17th International Workshop on Semantic Evaluation, 2023

Tree-Rings Watermarks: Invisible Fingerprints for Diffusion Images.
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.
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.
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.
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?
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.
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.
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.
Proceedings of the International Conference on Machine Learning, 2023

Cramming: Training a Language Model on a single GPU in one day.
Proceedings of the International Conference on Machine Learning, 2023

Canary in a Coalmine: Better Membership Inference with Ensembled Adversarial Queries.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Seeing in Words: Learning to Classify through Language Bottlenecks.
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.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Panning for Gold in Federated Learning: Targeted Text Extraction under Arbitrarily Large-Scale Aggregation.
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.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

STYX: Adaptive Poisoning Attacks Against Byzantine-Robust Defenses in Federated Learning.
Proceedings of the IEEE International Conference on Acoustics, 2023

Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Models.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

Universal Guidance for Diffusion Models.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022
K-SAM: Sharpness-Aware Minimization at the Speed of SGD.
CoRR, 2022

Thinking Two Moves Ahead: Anticipating Other Users Improves Backdoor Attacks in Federated Learning.
CoRR, 2022

Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise.
CoRR, 2022

Autoregressive Perturbations for Data Poisoning.
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.
Proceedings of the International Conference on Machine Learning, 2022

Stochastic Training is Not Necessary for Generalization.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Poisons that are learned faster are more effective.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2022

A Simple Strategy to Provable Invariance via Orbit Mapping.
Proceedings of the Computer Vision - ACCV 2022, 2022

2021
Modern optimization techniques in computer vision: from variational models to machine learning security.
PhD thesis, 2021

DARTS for Inverse Problems: a Study on Hyperparameter Sensitivity.
CoRR, 2021

Training or Architecture? How to Incorporate Invariance in Neural Networks.
CoRR, 2021

Preventing Unauthorized Use of Proprietary Data: Poisoning for Secure Dataset Release.
CoRR, 2021

DP-InstaHide: Provably Defusing Poisoning and Backdoor Attacks with Differentially Private Data Augmentations.
CoRR, 2021

What Doesn't Kill You Makes You Robust(er): Adversarial Training against Poisons and Backdoors.
CoRR, 2021

Adversarial Examples Make Strong Poisons.
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.
Proceedings of the 9th International Conference on Learning Representations, 2021

Strong Data Augmentation Sanitizes Poisoning and Backdoor Attacks Without an Accuracy Tradeoff.
Proceedings of the IEEE International Conference on Acoustics, 2021

2020
MetaPoison: Practical General-purpose Clean-label Data Poisoning.
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?
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.
Proceedings of the 8th International Conference on Learning Representations, 2020

Witchcraft: Efficient PGD Attacks with Random Step Size.
Proceedings of the 2020 IEEE International Conference on Acoustics, 2020

Fast Convex Relaxations using Graph Discretizations.
Proceedings of the 31st British Machine Vision Conference 2020, 2020

2019
Piecewise Rigid Scene Flow with Implicit Motion Segmentation.
Proceedings of the 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2019

Parametric Majorization for Data-Driven Energy Minimization Methods.
Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, 2019

2018
Composite Optimization by Nonconvex Majorization-Minimization.
SIAM J. Imaging Sci., 2018

2017
Multiframe Motion Coupling for Video Super Resolution.
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.
CoRR, 2016


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