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09/01/2023
Baseline Defenses for Adversarial Attacks Against Aligned Language Models
As Large Language Models quickly become ubiquitous, it becomes critical ...
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07/09/2023
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
This paper describes our zero-shot approaches for the Visual Word Sense ...
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06/29/2023
Seeing in Words: Learning to Classify through Language Bottlenecks
Neural networks for computer vision extract uninterpretable features des...
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06/28/2023
On the Exploitability of Instruction Tuning
Instruction tuning is an effective technique to align large language mod...
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06/23/2023
Bring Your Own Data! Self-Supervised Evaluation for Large Language Models
With the rise of Large Language Models (LLMs) and their ubiquitous deplo...
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06/07/2023
On the Reliability of Watermarks for Large Language Models
As LLMs become commonplace, machine-generated text has the potential to ...
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05/31/2023
Understanding and Mitigating Copying in Diffusion Models
Images generated by diffusion models like Stable Diffusion are increasin...
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05/31/2023
Tree-Ring Watermarks: Fingerprints for Diffusion Images that are Invisible and Robust
Watermarking the outputs of generative models is a crucial technique for...
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05/30/2023
What Can We Learn from Unlearnable Datasets?
In an era of widespread web scraping, unlearnable dataset methods have t...
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04/24/2023
A Cookbook of Self-Supervised Learning
Self-supervised learning, dubbed the dark matter of intelligence, is a p...
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04/05/2023
JPEG Compressed Images Can Bypass Protections Against AI Editing
Recently developed text-to-image diffusion models make it easy to edit o...
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02/14/2023
Universal Guidance for Diffusion Models
Typical diffusion models are trained to accept a particular form of cond...
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02/07/2023
Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery
The strength of modern generative models lies in their ability to be con...
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01/24/2023
A Watermark for Large Language Models
Potential harms of large language models can be mitigated by watermarkin...
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12/28/2022
Cramming: Training a Language Model on a Single GPU in One Day
Recent trends in language modeling have focused on increasing performanc...
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12/07/2022
Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Models
Cutting-edge diffusion models produce images with high quality and custo...
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10/23/2022
K-SAM: Sharpness-Aware Minimization at the Speed of SGD
Sharpness-Aware Minimization (SAM) has recently emerged as a robust tech...
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10/19/2022
Canary in a Coalmine: Better Membership Inference with Ensembled Adversarial Queries
As industrial applications are increasingly automated by machine learnin...
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10/17/2022
Thinking Two Moves Ahead: Anticipating Other Users Improves Backdoor Attacks in Federated Learning
Federated learning is particularly susceptible to model poisoning and ba...
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10/12/2022
How Much Data Are Augmentations Worth? An Investigation into Scaling Laws, Invariance, and Implicit Regularization
Despite the clear performance benefits of data augmentations, little is ...
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09/24/2022
A Simple Strategy to Provable Invariance via Orbit Mapping
Many applications require robustness, or ideally invariance, of neural n...
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08/19/2022
Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise
Standard diffusion models involve an image transform – adding Gaussian n...
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06/08/2022
Autoregressive Perturbations for Data Poisoning
The prevalence of data scraping from social media as a means to obtain d...
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04/19/2022
Poisons that are learned faster are more effective
Imperceptible poisoning attacks on entire datasets have recently been to...
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02/01/2022
Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification
Federated learning (FL) has rapidly risen in popularity due to its promi...
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01/29/2022
Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models
A central tenet of Federated learning (FL), which trains models without ...
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10/25/2021
Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models
Federated learning has quickly gained popularity with its promises of in...
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09/29/2021
Stochastic Training is Not Necessary for Generalization
It is widely believed that the implicit regularization of stochastic gra...
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08/12/2021
DARTS for Inverse Problems: a Study on Hyperparameter Sensitivity
Differentiable architecture search (DARTS) is a widely researched tool f...
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06/21/2021
Adversarial Examples Make Strong Poisons
The adversarial machine learning literature is largely partitioned into ...
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06/18/2021
Training or Architecture? How to Incorporate Invariance in Neural Networks
Many applications require the robustness, or ideally the invariance, of ...
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03/02/2021
DP-InstaHide: Provably Defusing Poisoning and Backdoor Attacks with Differentially Private Data Augmentations
Data poisoning and backdoor attacks manipulate training data to induce s...
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02/26/2021
What Doesn't Kill You Makes You Robust(er): Adversarial Training against Poisons and Backdoors
Data poisoning is a threat model in which a malicious actor tampers with...
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02/16/2021
Preventing Unauthorized Use of Proprietary Data: Poisoning for Secure Dataset Release
Large organizations such as social media companies continually release d...
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11/18/2020
Strong Data Augmentation Sanitizes Poisoning and Backdoor Attacks Without an Accuracy Tradeoff
Data poisoning and backdoor attacks manipulate victim models by maliciou...
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09/04/2020
Witches' Brew: Industrial Scale Data Poisoning via Gradient Matching
Data Poisoning attacks involve an attacker modifying training data to ma...
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04/23/2020
Fast Convex Relaxations using Graph Discretizations
Matching and partitioning problems are fundamentals of computer vision a...
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04/01/2020
MetaPoison: Practical General-purpose Clean-label Data Poisoning
Data poisoning–the process by which an attacker takes control of a model...
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03/31/2020
Inverting Gradients – How easy is it to break privacy in federated learning?
The idea of federated learning is to collaboratively train a neural netw...
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11/18/2019
WITCHcraft: Efficient PGD attacks with random step size
State-of-the-art adversarial attacks on neural networks use expensive it...
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10/01/2019
Truth or Backpropaganda? An Empirical Investigation of Deep Learning Theory
We empirically evaluate common assumptions about neural networks that ar...
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08/17/2019
Parametric Majorization for Data-Driven Energy Minimization Methods
Energy minimization methods are a classical tool in a multitude of compu...
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02/20/2018
Composite Optimization by Nonconvex Majorization-Minimization
Many tasks in imaging can be modeled via the minimization of a nonconvex...
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11/23/2016
