9 citations · 16 across the 13 of their papers we have counts for
14 papers
Making Embodied AI Reliable: A Community Agenda from Testing to Formal Verification
Xi Zheng, Dulanga Weerakoon, Yintong Huo +8
Embodied AI systems are increasingly deployed in open-world environments, yet ensuring their reliability remains a fundamental challenge. Drawing on discussions from the AAAI'26 Br…
An Enigma of Artificial Reason: Investigating the Production-Evaluation Gap in Large Reasoning Models
Mingzhong Sun, Teresa Yeo, Armando Solar-Lezama +1
Studies of human reasoning have shown that people are typically stronger at evaluating reasoning than producing it from scratch. In contrast, large reasoning models (LRMs) are trai…
Adaptive Problem Generation via Symbolic Representations
Teresa Yeo, Myeongho Jeon, Dulaj Weerakoon +4
We present a method for generating training data for reinforcement learning with verifiable rewards to improve small open-weights language models on mathematical tasks. Existing da…
Towards Adaptive Environment Generation for Training Embodied Agents
Teresa Yeo, Dulaj Weerakoon, Dulanga Weerakoon +1
Embodied agents struggle to generalize to new environments, even when those environments share similar underlying structures to their training settings. Most current approaches to…
An Analysis of Model Robustness across Concurrent Distribution Shifts
Myeongho Jeon, Suhwan Choi, Hyoje Lee +1
Machine learning models, meticulously optimized for source data, often fail to predict target data when faced with distribution shifts (DSs). Previous benchmarking studies, though…
ViPer: Visual Personalization of Generative Models via Individual Preference Learning
Sogand Salehi, Mahdi Shafiei, Teresa Yeo +2
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 indiv…
Controlled Training Data Generation with Diffusion Models
Teresa Yeo, Andrei Atanov, Harold Benoit +4
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 and…
4M: Massively Multimodal Masked Modeling
David Mizrahi, Roman Bachmann, Oğuzhan Fatih Kar +4
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…
Rapid Network Adaptation: Learning to Adapt Neural Networks Using Test-Time Feedback
Teresa Yeo, Oğuzhan Fatih Kar, Zahra Sodagar +1
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…
Task Discovery: Finding the Tasks that Neural Networks Generalize on
Andrei Atanov, Andrei Filatov, Teresa Yeo +2
When developing deep learning models, we usually decide what task we want to solve then search for a model that generalizes well on the task. An intriguing question would be: what…
3D Common Corruptions and Data Augmentation
Oğuzhan Fatih Kar, Teresa Yeo, Andrei Atanov +1
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 network…
Robustness via Cross-Domain Ensembles
Teresa Yeo, Oğuzhan Fatih Kar, Alexander Sax +1
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…
Robust Learning Through Cross-Task Consistency
Amir Zamir, Alexander Sax, Teresa Yeo +6
Visual perception entails solving a wide set of tasks, e.g., object detection, depth estimation, etc. The predictions made for multiple tasks from the same image are not independen…
Iterative Classroom Teaching
Teresa Yeo, Parameswaran Kamalaruban, Adish Singla +5
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…