Jonas Geiping
40 papers · 2019–2026 · 9 conferences · across top CS/AI conferences
Achievements
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Cross-Pollinator
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Taxonomy Completionist
(60)
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Keyword Pioneer
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Dynamic Duo
(32)
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Grand Slam
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Triple Crown
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Deep Specialist
(10)
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Century Club
(39)
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Prolific Year
(17)
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Unstoppable
(7)
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The Questioner
(6)
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Keyword Collector
(129)
Conferences
NIPS (15)
ICLR (12)
ICML (6)
CVPR (2)
AAAI (1)
ACL (1)
ICCV (1)
NAACL (1)
SEMEVAL (1)
Top co-authors
Research topics
Keywords
large language model
(4)
data poisoning
(4)
generative model
(3)
neural network
(3)
diffusion model
(3)
vision-language model
(3)
prompt augmentation
(2)
training datum
(2)
visual word sense disambiguation
(2)
in-context learning
(2)
privacy attack
(2)
privacy protection
(2)
text-to-image diffusion
(2)
adversarial attack
(2)
text generation
(2)
zero-shot learning
(2)
instruction tuning
(2)
poisoning attack
(2)
copyright protection
(2)
data replication
(2)
Papers
MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation
AAAI 2026
LLM-Generated Passphrases That Are Secure and Easy to Remember
NAACL 2025
An Interpretable N-gram Perplexity Threat Model for Large Language Model Jailbreaks
ICML 2025
When, Where and Why to Average Weights?
ICML 2025
Great Models Think Alike and this Undermines AI Oversight
ICML 2025
Spotting LLMs With Binoculars: Zero-Shot Detection of Machine-Generated Text
ICML 2024
Be like a Goldfish, Don't Memorize! Mitigating Memorization in Generative LLMs
NIPS 2024
Privacy Backdoors: Enhancing Membership Inference through Poisoning Pre-trained Models
NIPS 2024
CALVIN: Improved Contextual Video Captioning via Instruction Tuning
NIPS 2024
Transformers Can Do Arithmetic with the Right Embeddings
NIPS 2024
Object Recognition as Next Token Prediction
CVPR 2024
NEFTune: Noisy Embeddings Improve Instruction Finetuning
ICLR 2024
Universal Guidance for Diffusion Models
ICLR 2024
On the Reliability of Watermarks for Large Language Models
ICLR 2024
Cramming: Training a Language Model on a single GPU in one day.
ICML 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
ACL 2023
Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Models
CVPR 2023
Canary in a Coalmine: Better Membership Inference with Ensembled Adversarial Queries
ICLR 2023
Loss Landscapes are All You Need: Neural Network Generalization Can Be Explained Without the Implicit Bias of Gradient Descent
ICLR 2023
A Watermark for Large Language Models
ICML 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
SEMEVAL 2023
A Performance-Driven Benchmark for Feature Selection in Tabular Deep Learning
NIPS 2023
Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise
NIPS 2023
Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models
ICLR 2023
How Much Data Are Augmentations Worth? An Investigation into Scaling Laws, Invariance, and Implicit Regularization
ICLR 2023
Panning for Gold in Federated Learning: Targeted Text Extraction under Arbitrarily Large-Scale Aggregation
ICLR 2023
Understanding and Mitigating Copying in Diffusion Models
NIPS 2023
Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery
NIPS 2023
Tree-Rings Watermarks: Invisible Fingerprints for Diffusion Images
NIPS 2023
On the Exploitability of Instruction Tuning
NIPS 2023
What Can We Learn from Unlearnable Datasets?
NIPS 2023
Autoregressive Perturbations for Data Poisoning
NIPS 2022
Stochastic Training is Not Necessary for Generalization
ICLR 2022
Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models
ICLR 2022
Witches' Brew: Industrial Scale Data Poisoning via Gradient Matching
ICLR 2021
Adversarial Examples Make Strong Poisons
NIPS 2021
MetaPoison: Practical General-purpose Clean-label Data Poisoning
NIPS 2020
Truth or backpropaganda? An empirical investigation of deep learning theory
ICLR 2020
Inverting Gradients - How easy is it to break privacy in federated learning?
NIPS 2020
Parametric Majorization for Data-Driven Energy Minimization Methods
ICCV 2019