Oguzhan Akcin — accepted AI-conference papers

2026

DiffVax: Optimization-Free Image Immunization Against Diffusion-Based Editing

Tarik Can Ozden, Ozgur Kara, Oguzhan Akcin, Kerem Zaman, Shashank Srivastava, Sandeep P. Chinchali +1

ICLR 2026poster

Current image immunization defense techniques against diffusion-based editing embed imperceptible noise into target images to disrupt editing models. However, these methods face scalability challenges, as they require time-consuming optimization for each image separately, taking hours for small batc…

Cited by 0 Source ↗code ↗Scholar ↗

2025

Distributed Upload and Active Labeling for Resource-Constrained Fleet Learning

Oguzhan Akcin, Harsh Goel, Ruihan Zhao, Sandeep P. Chinchali

CoRL 2025poster

In multi-robot systems, fleets are often deployed to collect data that improves the performance of machine learning models for downstream perception and planning. However, real-world robotic deployments generate vast amounts of data across diverse conditions, while only a small portion can be transm…

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2024

Fleet Supervisor Allocation: A Submodular Maximization Approach

Oguzhan Akcin, Ahmet Ege Tanriverdi, Kaan Kale, Sandeep P. Chinchali

CoRL 2024poster

In real-world scenarios, the data collected by robots in diverse and unpredictable environments is crucial for enhancing their perception and decision-making models. This data is predominantly collected under human supervision, particularly through imitation learning (IL), where robots learn complex…

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2024

Time Weaver: A Conditional Time Series Generation Model

Sai Shankar Narasimhan, Shubhankar Agarwal, Oguzhan Akcin, sujay sanghavi, Sandeep P. Chinchali

ICML 2024spotlight

Imagine generating a city’s electricity demand pattern based on weather, the presence of an electric vehicle, and location, which could be used for capacity planning during a winter freeze. Such real-world time series are often enriched with paired heterogeneous contextual metadata (e.g., weather an…

Cited by 17 Source ↗PDF ↗Scholar ↗

2023

Fleet Active Learning: A Submodular Maximization Approach

Oguzhan Akcin, Orhan Unuvar, Onat Ure, Sandeep P. Chinchali

CoRL 2023poster

In multi-robot systems, robots often gather data to improve the performance of their deep neural networks (DNNs) for perception and planning. Ideally, these robots should select the most informative samples from their local data distributions by employing active learning approaches. However, when th…

Cited by 5 Source ↗Scholar ↗

2022

Decentralized Data Collection for Robotic Fleet Learning: A Game-Theoretic Approach

Oguzhan Akcin, Po-han Li, Shubhankar Agarwal, Sandeep P. Chinchali

CoRL 2022poster

Fleets of networked autonomous vehicles (AVs) collect terabytes of sensory data, which is often transmitted to central servers (the ``cloud'') for training machine learning (ML) models. Ideally, these fleets should upload all their data, especially from rare operating contexts, in order to train rob…

Cited by 6 Source ↗Scholar ↗