Publications by Shuqing Teresa Yeo

ViPer: Visual Personalization of Generative Models via Individual Preference Learning

Amir Roshan Zamir, Roman Christian Bachmann, Shuqing Teresa Yeo

Different users find different images generated for the same prompt desirable. This gives rise to personalized image generation which involves creating images aligned with an individual’s visual preference. Current generative models are, however, tuned to ...

Springer Science and Business Media Deutschland GmbH2025

Controlled Training Data Generation with Diffusion Models

Andrei Atanov, Shuqing Teresa Yeo

We present a method to control a text-to-image generative model to produce training data useful for supervised learning. Unlike previous works that employ an open-loop approach via pre-defined prompts to generate new data using either a language model or h ...

2025

An Analysis of Model Robustness across Concurrent Distribution Shifts

Shuqing Teresa Yeo

Machine learning models, meticulously optimized for source data, often fail to predict target data when faced with distribution shifts (DSs). Previous benchmarking studies, though extensive, have mainly focused on simple DSs. Recognizing that DSs often occ ...

2025

4M: Massively Multimodal Masked Modeling

Amir Roshan Zamir, Oguzhan Fatih Kar, Roman Christian Bachmann, Shuqing Teresa Yeo, David Mizrahi

Current machine learning models for vision are often highly specialized and limited to a single modality and task. In contrast, recent large language models exhibit a wide range of capabilities, hinting at a possibility for similarly versatile models in co ...

Neural Information Processing Systems (Nips)2023

Rapid Network Adaptation: Learning to Adapt Neural Networks Using Test-Time Feedback

Amir Roshan Zamir, Oguzhan Fatih Kar, Shuqing Teresa Yeo, Zahra Sodagar

We propose a method for adapting neural networks to distribution shifts at test-time. In contrast to training-time robustness mechanisms that attempt to anticipate and counter the shift, we create a closed-loop system and make use of test-time feedback sig ...

Ieee Computer Soc2023

Making Computer Vision Models Robust and Adaptive

Shuqing Teresa Yeo

Visual perception is indispensable for many real-world applications. However, perception models deployed in the real world will encounter numerous and unpredictable distribution shifts, for example, changes in geographic locations, motion blur, and adverse ...

EPFL2023

3D Common Corruptions and Data Augmentation

Amir Roshan Zamir, Oguzhan Fatih Kar, Andrei Atanov, Shuqing Teresa Yeo

We introduce a set of image transformations that can be used as corruptions to evaluate the robustness of models as well as data augmentation mechanisms for training neural networks. The primary distinction of the proposed transformations is that, unlike e ...

IEEE COMPUTER SOC2022

Robustness via Cross-Domain Ensembles

Amir Roshan Zamir, Oguzhan Fatih Kar, Shuqing Teresa Yeo

We present a method for making neural network predictions robust to shifts from the training data distribution. The proposed method is based on making predictions via a diverse set of cues (called 'middle domains') and ensembling them into one strong predi ...

IEEE2021

Iterative Classroom Teaching

Pierre Dillenbourg, Volkan Cevher, Thibault Lucien Christian Asselborn, Louis Pierre Faucon, Shuqing Teresa Yeo, Kamalaruban Parameswaran

We consider the machine teaching problem in a classroom-like setting wherein the teacher has to deliver the same examples to a diverse group of students. Their diversity stems from differences in their initial internal states as well as their learning rate ...

ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE2019

Applying IDC theory to education in the Alps region: A response to Chan et al.’s contribution

Pierre Dillenbourg, Jennifer Kaitlyn Olsen, Jauwairia Nasir, Kevin Gonyop Kim, Shuqing Teresa Yeo

In this paper, we present a response to the Interest-Driven Creator (IDC) Theory from a European perspective. Specifically, we raise six questions intended to start a dialogue with respect to IDC Theory’s placement in existing learning theories, its adopti ...

2019