Abstract
The slow adoption of Artificial Intelligence (AI)-enabled agents that operate in dynamic real-world environments is in many instances due to the lack of model resilience and robustness to previously unseen real-world data, often leading to unpredictable behavior after deployment. This is true, for example, in commercial applications such as autonomous driving or human-robot interaction, and in military applications such as object recognition. It is therefore critical in many applications that the AI models generalize to new environment variations and scenarios. There are several approaches that one could consider, ranging from synthetic data augmentation approaches such as Sim2Real for robotics or incremental and continuous learning where an AI model dynamically adapts without forgetting what it has learned in the past. In this workshop, we seek novel advances in the development of AI models that are robust to distributional shifts in the data used to train AI models when deployed in dynamic environments. Emphasis will be placed on AI- models operating on multivariate time-series signals which are of relevance in signal processing.
Keywords: Artificial Intelligence, distributional shifts, continuous learning, decision support, autonomous systems, physics-informed models, Sim2RealSchedule
Date: 7th April 2025, Monday
9:30 AM - 9:35 AM : Opening Remarks - Vishal Patel
9:35 AM - 10:35 AM : Invited Talk 1 - Rama Chellappa (45 min + 15 min Q&A)
10:35 AM - 11:00 AM : Paper Presentation 1 (20 min + 5 min Q&A) - Computation Overhead Optimization Dual-Domain Network with Multi-Level Cross-Domain Connections for Sparse-view CT Reconstruction (Zihan Deng, Zhisheng Wang, Yuanlin Shan, Guohang He, Demin Jiang, Shunli Wang)
11:00 AM - 11:30 AM : Tea/Coffee Break
11:30 AM - 12:30 PM : Invited Talk 2 - Dinesh Manocha (45 min + 15 Q&A)
12:35 PM - 1:00 PM : Paper Presentation 2 (20 min + 5 min Q&A per presenter) - Multi-Armed Bandit with Sparse and Noisy Feedback (Djallel Bouneffouf, Raphael Feraud, Baihan Lin)
Organizers
Dr. Joel Goodman, Office of Naval Research (ONR), Arlingon, VA USA,
E: joel.i.goodman.civ@us.navy.mil
Dr. Shuowen Hu, DEVCOM Army Research Laboratory (ARL) Adelphi, MD USA,
E: shuowen.hu.civ@army.mil
Dr. Raghuveer Rao, DEVCOM Army Research Laboratory (ARL) Adelphi, MD USA,
E: raghuveer.m.rao.civ@army.mil
Prof. Vikram Krishnamurthy, Cornell University, Ithaca NY USA,
E: vikramk@cornell.edu
Prof. Vishal Patel, Johns Hopkins University, Baltimore, MD USA,
E: vpatel36@jhu.edu
WebChair: Jay N. Paranjape, Johns Hopkins University, Baltimore, MD USA,
E: jparanj1@jhu.edu
Diversity Inclusion and Ethical Issues
This workshop will promote an inclusive and equitable culture that welcomes, engages, and rewards all who contribute to the field, without regard to race, religion, gender, disability, age, national origin, sexual orientation, gender identity, or gender expression. We will work to ensure that any data or open-source material that our attendees have access to as a part of this workshop is free of explicit or implicit bias.