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1. Co-Dream: Collaborative Dream Synthesis over Decentralized Models

   https://doi.org/10.1609/aaai.v39i19.34258  

   Singh, Abhishek; Gupta, Gauri; Shi, Yichuan; Dang, Alex; Kapila, Ritvik; Shankar, Sheshank; Ehab, Mohammed; Raskar, Ramesh (April 2025, Proceedings of the AAAI Conference on Artificial Intelligence)

   Federated Learning (FL) has pioneered the idea of share wisdom not raw data to enable collaborative learning over decentralized data. FL achieves this goal by averaging model parameters instead of centralizing data. However, representing wisdom in the form of model parameters has its own limitations including the requirement for uniform model architectures across clients and communication overhead proportional to model size.In this work we introduce Co-Dream a framework for representing wisdom in data space instead of model parameters. Here, clients collaboratively optimize random inputs based on their locally trained models and aggregate gradients of their inputs. Our proposed approach overcomes the aforementioned limitations and comes with additional benefits such as adaptive optimization and interpretable representation of knowledge. We empirically demonstrate the effectiveness of Co-Dream and compare its performance with existing techniques. 

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2. Hypergraph co-optimal transport: metric and categorical properties

   https://doi.org/10.1007/s41468-023-00142-9  

   Chowdhury, Samir; Needham, Tom; Semrad, Ethan; Wang, Bei; Zhou, Youjia (September 2023, Journal of Applied and Computational Topology)

   Hypergraphs capture multi-way relationships in data, and they have consequently seen a number of applications in higher-order network analysis, computer vision, geometry processing, and machine learning. In this paper, we develop theoretical foundations for studying the space of hypergraphs using ingredients from optimal transport. By enriching a hypergraph with probability measures on its nodes and hyperedges, as well as relational information capturing local and global structures, we obtain a general and robust framework for studying the collection of all hypergraphs. First, we introduce a hypergraph distance based on the co-optimal transport framework of Redko et al. and study its theoretical properties. Second, we formalize common methods for transforming a hypergraph into a graph as maps between the space of hypergraphs and the space of graphs, and study their functorial properties and Lipschitz bounds. Finally, we demonstrate the versatility of our Hypergraph Co-Optimal Transport (HyperCOT) framework through various examples. 

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3. Co-manifold learning with missing data

   Mishne, Gal; Chi, Eric C; Coifman, Ronald R. (January 2019, Proceedings of the 36th International Conference on Machine Learning)

   Representation learning is typically applied to only one mode of a data matrix, either its rows or columns. Yet in many applications, there is an underlying geometry to both the rows and the columns. We propose utilizing this coupled structure to perform co-manifold learning: uncovering the underlying geometry of both the rows and the columns of a given matrix, where we focus on a missing data setting. Our unsupervised approach consists of three components. We first solve a family of optimization problems to estimate a complete matrix at multiple scales of smoothness. We then use this collection of smooth matrix estimates to compute pairwise distances on the rows and columns based on a new multi-scale metric that implicitly introduces a coupling between the rows and the columns. Finally, we construct row and column representations from these multi- scale metrics. We demonstrate that our approach outperforms competing methods in both data visualization and clustering. 

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4. Advancing Neuro-Inspired Lifelong Learning for Edge with Co-Design

   https://doi.org/10.1609/aaaiss.v3i1.31226  

   Soures, Nicholas; Karia, Vedant; Kudithipudi, Dhireesha (May 2024, Proceedings of the AAAI Symposium Series)

   Lifelong learning, which refers to an agent's ability to continuously learn and enhance its performance over its lifespan, is a significant challenge in artificial intelligence (AI), that biological systems tackle efficiently. This challenge is further exacerbated when AI is deployed in untethered environments with strict energy and latency constraints. We take inspiration from neural plasticity and investigate how to leverage and build energy-efficient lifelong learning machines. Specifically, we study how a combination of neural plasticity mechanisms, namely neuromodulation, synaptic consolidation, and metaplasticity, enhance the continual learning capabilities of AI models. We further co-design architectures that leverage compute-in-memory topologies and sparse spike-based communication with quantization for the edge. Aspects of this co-design can be transferred to federated lifelong learning scenarios. 

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5. Provable Convex Co-clustering of Tensors

   Chi, Eric C; Gaines, Brian J; Sun, Will Wei; Zhou, Hua; Yang, Jian (October 2020, Journal of machine learning research)

   null (Ed.)

   Cluster analysis is a fundamental tool for pattern discovery of complex heterogeneous data. Prevalent clustering methods mainly focus on vector or matrix-variate data and are not applicable to general-order tensors, which arise frequently in modern scientific and business applications. Moreover, there is a gap between statistical guarantees and computational efficiency for existing tensor clustering solutions due to the nature of their non-convex formulations. In this work, we bridge this gap by developing a provable convex formulation of tensor co-clustering. Our convex co-clustering (CoCo) estimator enjoys stability guarantees and its computational and storage costs are polynomial in the size of the data. We further establish a non-asymptotic error bound for the CoCo estimator, which reveals a surprising ``blessing of dimensionality" phenomenon that does not exist in vector or matrix-variate cluster analysis. Our theoretical findings are supported by extensive simulated studies. Finally, we apply the CoCo estimator to the cluster analysis of advertisement click tensor data from a major online company. Our clustering results provide meaningful business insights to improve advertising effectiveness. 

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