arXiv subjects

Annie Chen

Publications and source records attributed to Annie Chen.

6 recordsLinked to original sources

arXiv2026

CHORUS: Decentralized Multi-Embodiment Collaboration with One VLA Policy

Multi-robot collaboration allows robots to efficiently take on a wide range of tasks, from moving a couch through a doorway to assembling structures on a construction site. However, achieving such coordination in mobile multi-robot settings remains challenging: centralized methods conditioned on the combined observations of a team scale poorly with team size, and decentralized methods that train one policy per robot often require explicit alignment procedures or information sharing at inference time to overcome partial observability. Our key insight is that the visuomotor priors of pretrained vision-language-action (VLA) models should enable reactive, decentralized collaboration from each robot's local observations alone, without these inference-time assumptions. We propose CHORUS, a framework that adapts a single VLA backbone to control diverse, multi-robot teams. At inference time, each robot runs an independent copy of CHORUS, conditioned only on its own observations and a robot-identifying prompt. In real-world experiments including mobile tape measurement, library book handovers, and laundry basket lifting, CHORUS achieves a 64% point improvement over decentralized, from-scratch models, improves reactivity to teammate behavior by 40% points, and outperforms centralized baselines. Together, these results show that a shared VLA backbone is capable of achieving decentralized multi-robot collaboration, without per-robot policies or inter-robot communication at inference.

cs.RO↗

arXiv2025

Improving Forecasts of Suicide Attempts for Patients with Little Data

Ecological Momentary Assessment provides real-time data on suicidal thoughts and behaviors, but predicting suicide attempts remains challenging due to their rarity and patient heterogeneity. We show that single models fit to all patients perform poorly, while individualized models improve performance but still overfit to patients with limited data. To address this, we introduce Latent Similarity Gaussian Processes (LSGPs) to capture patient heterogeneity, enabling those with little data to leverage similar patients' trends. Preliminary results show promise: even without kernel-design, we outperform all but one baseline while offering a new understanding of patient similarity.

stat.ML↗

arXiv2025

Safety Representations for Safer Policy Learning

Reinforcement learning algorithms typically necessitate extensive exploration of the state space to find optimal policies. However, in safety-critical applications, the risks associated with such exploration can lead to catastrophic consequences. Existing safe exploration methods attempt to mitigate this by imposing constraints, which often result in overly conservative behaviours and inefficient learning. Heavy penalties for early constraint violations can trap agents in local optima, deterring exploration of risky yet high-reward regions of the state space. To address this, we introduce a method that explicitly learns state-conditioned safety representations. By augmenting the state features with these safety representations, our approach naturally encourages safer exploration without being excessively cautious, resulting in more efficient and safer policy learning in safety-critical scenarios. Empirical evaluations across diverse environments show that our method significantly improves task performance while reducing constraint violations during training, underscoring its effectiveness in balancing exploration with safety.

cs.LG↗

arXiv2024

AutoFT: Learning an Objective for Robust Fine-Tuning

Foundation models encode rich representations that can be adapted to downstream tasks by fine-tuning. However, fine-tuning a model on one data distribution often degrades performance under distribution shifts. Current approaches to robust fine-tuning use hand-crafted regularization techniques to constrain the fine-tuning process towards the pretrained model. Yet, it is hard to specify how to adapt relevant characteristics of the foundation model during fine-tuning, as this depends on how the pre-training, fine-tuning, and test data distributions relate to each other. We propose AutoFT, a data-driven approach for robust fine-tuning. Given a task, AutoFT searches for a fine-tuning procedure that enhances out-of-distribution (OOD) generalization. Specifically, AutoFT uses bi-level optimization to search for an objective function and hyperparameters that maximize post-adaptation performance on a small OOD validation set. We evaluate AutoFT on nine natural distribution shifts. Our experiments show that AutoFT significantly improves generalization to OOD inputs, outperforming existing robust fine-tuning methods. Notably, AutoFT achieves a new state-of-the-art on the WILDS iWildCam and FMoW benchmarks, outperforming the previous best methods by $6.0\%$ and $1.5\%$, respectively.

cs.LG↗

arXiv2022

On the Opportunities and Risks of Foundation Models

AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks. We call these models foundation models to underscore their critically central yet incomplete character. This report provides a thorough account of the opportunities and risks of foundation models, ranging from their capabilities (e.g., language, vision, robotics, reasoning, human interaction) and technical principles(e.g., model architectures, training procedures, data, systems, security, evaluation, theory) to their applications (e.g., law, healthcare, education) and societal impact (e.g., inequity, misuse, economic and environmental impact, legal and ethical considerations). Though foundation models are based on standard deep learning and transfer learning, their scale results in new emergent capabilities,and their effectiveness across so many tasks incentivizes homogenization. Homogenization provides powerful leverage but demands caution, as the defects of the foundation model are inherited by all the adapted models downstream. Despite the impending widespread deployment of foundation models, we currently lack a clear understanding of how they work, when they fail, and what they are even capable of due to their emergent properties. To tackle these questions, we believe much of the critical research on foundation models will require deep interdisciplinary collaboration commensurate with their fundamentally sociotechnical nature.

cs.LG↗

arXiv2019

Limit Theorems for Descents in Permutations and Arithmetic Progressions in $\mathbb{Z}/p\mathbb{Z}$

We prove a quantitative local limit theorem for the number of descents in a random permutation. Our proof uses a conditioning argument and is based on bounding the characteristic function $ϕ(t)$ of the number of descents. We also establish a central limit theorem for the number of 3-term arithmetic progressions (3-APs) in a random subset of $\mathbb{Z}/p\mathbb{Z}$. We conjecture that there is no local limit theorem for 3-APs, but a proof of this remains elusive. A promising avenue of proof is to condition on the size of the subset and show that the resulting distributions are too far apart for different sizes. This has proven difficult because the distances between these conditioned distributions on are the same order as their standard deviations such that the constant multiple between them is not very large.

math.PR↗