8 citations · 36 across the 25 of their papers we have counts for
32 papers
Colored Graph Signal Diffusion Models
Yiğit Berkay Uslu, Samar Hadou, Shirin Saeedi Bidokhti +1
Standard diffusion processes with isotropic noise corruption are not spectrally neutral. They inherit a coarse-to-fine generation hierarchy from the nonuniform data spectrum, becau…
Tetris3D: 3D Scene Generation With Objects That Fit Together
Jaeyeong Kim, Jinhyuk Jang, Jongmin Lee +2
We propose Tetris3D, a generative framework for single-image 3D scene reconstruction that recovers objects which are physically and geometrically coherent as a scene. Existing meth…
Generative Diffusion Models of Stochastic Graph Signals
Yiğit Berkay Uslu, Samar Hadou, Sergio Rozada +2
Sampling stochastic signals supported on a graph underlies many graph machine learning tasks, including recommender systems, forecasting in financial markets, and wireless network…
Active Sampling for Ultra-Low-Bit-Rate Video Compression via Conditional Controlled Diffusion
Amirhosein Javadi, Shirin Saeedi Bidokhti, Tara Javidi
Diffusion models provide a powerful generative prior for perceptual reconstruction at ultra-low bitrates, but effective video compression requires controlling the generative proces…
Graph Signal Diffusion Models for Wireless Resource Allocation
Yigit Berkay Uslu, Samar Hadou, Shirin Saeedi Bidokhti +1
We consider constrained ergodic resource optimization in wireless networks with graph-structured interference. We train a diffusion model policy to match expert conditional distrib…
Decentralized Learning Strategies for Estimation Error Minimization with Graph Neural Networks
Xingran Chen, Navid NaderiAlizadeh, Alejandro Ribeiro +1
We address real-time sampling and estimation of autoregressive Markovian sources in dynamic yet structurally similar multi-hop wireless networks. Each node caches samples from othe…
Graph Signal Generative Diffusion Models
Yigit Berkay Uslu, Samar Hadou, Sergio Rozada +2
We introduce U-shaped encoder-decoder graph neural networks (U-GNNs) for stochastic graph signal generation using denoising diffusion processes. The architecture learns node featur…
Generative Diffusion Models for Resource Allocation in Wireless Networks
Yigit Berkay Uslu, Samar Hadou, Shirin Saeedi Bidokhti +1
This paper proposes a supervised training algorithm for learning stochastic resource allocation policies with generative diffusion models (GDMs). We formulate the allocation proble…
Steering the Herd: A Framework for LLM-based Control of Social Learning
Raghu Arghal, Kevin He, Shirin Saeedi Bidokhti +1
Algorithms increasingly serve as information mediators--from social media feeds and targeted advertising to the increasing ubiquity of LLMs. This engenders a joint process where ag…
Optimal Neural Compressors for the Rate-Distortion-Perception Tradeoff
Eric Lei, Hamed Hassani, Shirin Saeedi Bidokhti
Recent efforts in neural compression have focused on the rate-distortion-perception (RDP) tradeoff, where the perception constraint ensures the source and reconstruction distributi…
Group Testing with General Correlation Using Hypergraphs
Hesam Nikpey, Saswati Sarkar, Shirin Saeedi Bidokhti
Group testing, a problem with diverse applications across multiple disciplines, traditionally assumes independence across nodes' states. Recent research, however, focuses on real-w…
Transferable Graphical MARL for Real-Time Estimation in Dynamic Wireless Networks
Xingran Chen, Navid NaderiAlizadeh, Alejandro Ribeiro +1
We study real-time sampling and estimation of autoregressive Markovian sources in decentralized and dynamic multi-hop networks that share similar structures. Nodes cache neighborin…
Approaching Rate-Distortion Limits in Neural Compression with Lattice Transform Coding
Eric Lei, Hamed Hassani, Shirin Saeedi Bidokhti
Neural compression has brought tremendous progress in designing lossy compressors with good rate-distortion (RD) performance at low complexity. Thus far, neural compression design…
Text + Sketch: Image Compression at Ultra Low Rates
Eric Lei, Yiğit Berkay Uslu, Hamed Hassani +1
Recent advances in text-to-image generative models provide the ability to generate high-quality images from short text descriptions. These foundation models, when pre-trained on bi…
On a Relation Between the Rate-Distortion Function and Optimal Transport
Eric Lei, Hamed Hassani, Shirin Saeedi Bidokhti
We discuss a relationship between rate-distortion and optimal transport (OT) theory, even though they seem to be unrelated at first glance. In particular, we show that a function d…
Federated Neural Compression Under Heterogeneous Data
Eric Lei, Hamed Hassani, Shirin Saeedi Bidokhti
We discuss a federated learned compression problem, where the goal is to learn a compressor from real-world data which is scattered across clients and may be statistically heteroge…
Containing a spread through sequential learning: to exploit or to explore?
Xingran Chen, Hesam Nikpey, Jungyeol Kim +2
The spread of an undesirable contact process, such as an infectious disease (e.g. COVID-19), is contained through testing and isolation of infected nodes. The temporal and spatial…
Bounds on the Capacity of the Multiple Access Diamond Channel with Cooperating Base-Stations
Michael Dikshtein, Shirin Saeedi Bidokhti, Shlomo Shamai
A diamond network is considered in which the central processor is connected, via backhaul noiseless links, to multiple conferencing base stations, which communicate with a single u…
Group Testing with Correlation under Edge-Faulty Graphs
Hesam Nikpey, Jungyeol Kim, Xingran Chen +2
In applications of group testing in networks, e.g. identifying individuals who are infected by a disease spread over a network, exploiting correlation among network nodes provides…
Robust Graph Neural Networks via Probabilistic Lipschitz Constraints
Raghu Arghal, Eric Lei, Shirin Saeedi Bidokhti
Graph neural networks (GNNs) have recently been demonstrated to perform well on a variety of network-based tasks such as decentralized control and resource allocation, and provide…
Out-of-Distribution Robustness in Deep Learning Compression
Eric Lei, Hamed Hassani, Shirin Saeedi Bidokhti
In recent years, deep neural network (DNN) compression systems have proved to be highly effective for designing source codes for many natural sources. However, like many other mach…
Timely Broadcasting in Erasure Networks: Age-Rate Tradeoffs
Xingran Chen, Renpu Liu, Shaochong Wang +1
The interplay between timeliness and rate efficiency is investigated in packet erasure broadcast channels with feedback. A scheduling framework is proposed in which coding actions,…
Real-time Sampling and Estimation on Random Access Channels: Age of Information and Beyond
Xingran Chen, Xinyu Liao, Shirin Saeedi Bidokhti
Efficient sampling and remote estimation are critical for a plethora of wireless-empowered applications in the Internet of Things and cyber-physical systems. Motivated by such appl…
Age of Information in Random Access Channels
Xingran Chen, Konstantinos Gatsis, Hamed Hassani +1
In applications of remote sensing, estimation, and control, timely communication is not always ensured by high-rate communication. This work proposes distributed age-efficient tran…