13 citations · 27 across the 9 of their papers we have counts for
9 papers
OpenApps: Simulating Environment Variations to Measure UI-Agent Reliability
Karen Ullrich, Jingtong Su, Claudia Shi +7
Reliability is key to realizing the promise of autonomous UI-Agents, multimodal agents that directly interact with apps in the same manner as humans, as users must be able to trust…
A Single Character can Make or Break Your LLM Evals
Jingtong Su, Jianyu Zhang, Karen Ullrich +2
Common Large Language model (LLM) evaluations rely on demonstration examples to steer models' responses to the desired style. While the number of examples used has been studied and…
From Concepts to Components: Concept-Agnostic Attention Module Discovery in Transformers
Jingtong Su, Julia Kempe, Karen Ullrich
Transformers have achieved state-of-the-art performance across language and vision tasks. This success drives the imperative to interpret their internal mechanisms with the dual go…
Mission Impossible: A Statistical Perspective on Jailbreaking LLMs
Jingtong Su, Julia Kempe, Karen Ullrich
Large language models (LLMs) are trained on a deluge of text data with limited quality control. As a result, LLMs can exhibit unintended or even harmful behaviours, such as leaking…
On the Robustness of Neural Collapse and the Neural Collapse of Robustness
Jingtong Su, Ya Shi Zhang, Nikolaos Tsilivis +1
Neural Collapse refers to the curious phenomenon in the end of training of a neural network, where feature vectors and classification weights converge to a very simple geometrical…
Wavelets Beat Monkeys at Adversarial Robustness
Jingtong Su, Julia Kempe
Research on improving the robustness of neural networks to adversarial noise - imperceptible malicious perturbations of the data - has received significant attention. The currently…
On the Connection between Invariant Learning and Adversarial Training for Out-of-Distribution Generalization
Shiji Xin, Yifei Wang, Jingtong Su +1
Despite impressive success in many tasks, deep learning models are shown to rely on spurious features, which will catastrophically fail when generalized to out-of-distribution (OOD…
Can we achieve robustness from data alone?
Nikolaos Tsilivis, Jingtong Su, Julia Kempe
We introduce a meta-learning algorithm for adversarially robust classification. The proposed method tries to be as model agnostic as possible and optimizes a dataset prior to its d…
Sanity-Checking Pruning Methods: Random Tickets can Win the Jackpot
Jingtong Su, Yihang Chen, Tianle Cai +4
Network pruning is a method for reducing test-time computational resource requirements with minimal performance degradation. Conventional wisdom of pruning algorithms suggests that…