385 citations · 2k across the 117 of their papers we have counts for
129 papers
A Performative Control Approach to Day-to-Day Traffic Signal Retiming
Shuchang Jiang, Xinwu Qian, Santiago Segarra +1
Conventional fixed-time traffic signal optimization and recent day-to-day signal retiming generally model route adaptation under prescribed route-choice behavior and origin-destina…
Feasible Flow Matching for Graph Reconstruction via Within-Sampling Primal-Dual Guidance
Haoming Chen, Nicolas Zilberstein, Santiago Paternain +1
Graph reconstruction from partial observations often comes with structural side information, such as degree bounds, triangle counts, or an edge-density band. Prior-Informed Flow Ma…
Biased Backpressure Routing for Multihop Wireless Networks with Heterogeneous Interfaces
Yujun Ming, Zhongyuan Zhao, Fikadu Dagefu +5
Heterogeneous-interface multihop wireless networks (Het-MuNets) are emerging as a promising paradigm for tactical networks and for infrastructure-light applications such as vehicul…
Directed Graph Topology Inference via Graph Filter Identification
Rasoul Shafipour, Andrei Buciulea, Santiago Segarra +2
We address the problem of inferring a directed network from nodal measurements generated by linear diffusion dynamics on the sought graph. Observations are modeled as the outputs o…
Ant Backpressure Routing for Dynamic Wireless Multi-hop Networks with Mixed Traffic Patterns
Negar Erfaniantaghvayi, Zhongyuan Zhao, Kevin Chan +2
Backpressure (BP) routing and its shortest-path biased variant (SP-BP) provide powerful congestion-aware multipath resource allocation for wireless multi-hop networks, but they rel…
Learning Normalized Energy Models for Linear Inverse Problems
Nicolas Zilberstein, Santiago Segarra, Eero Simoncelli +1
Generative diffusion models can provide powerful prior probability models for inverse problems in imaging, but existing implementations suffer from two key limitations: the p…
Prior-Informed Flow Matching for Graph Reconstruction
Harvey Chen, Nicolas Zilberstein, Santiago Segarra
We introduce \textit{Prior-Informed Flow Matching (PIFM)}, a conditional flow model for graph reconstruction. Reconstructing graphs from partial observations remains a key challeng…
A Performance Analyzer for a Public Cloud's ML-Augmented VM Allocator
Roozbeh Bostandoost, Pooria Namyar, Siva Kesava Reddy Kakarla +8
Cloud operators increasingly deploy multiple ML models in their VM allocation pipelines. In such settings, individually benign predictions can shift and compound, severely degradin…
Deep Unfolding: Recent Developments, Theory, and Design Guidelines
Nir Shlezinger, Santiago Segarra, Yi Zhang +4
Optimization methods play a central role in signal processing, serving as the mathematical foundation for inference, estimation, and control. While classical iterative optimization…
From Moments to Models: Graphon-Mixture Learning for Mixup and Contrastive Learning
Ali Azizpour, Reza Ramezanpour, Santiago Segarra
Real-world graph datasets often arise from mixtures of populations, where graphs are generated by multiple distinct underlying distributions. In this work, we propose a unified fra…
Adaptive Node Feature Selection For Graph Neural Networks
Madeline Navarro, Ali Azizpour, Santiago Segarra
We propose an adaptive node feature selection approach for graph neural networks (GNNs) that identifies and removes unnecessary features during training. The ability to measure how…
Biased Backpressure Routing Using Link Features and Graph Neural Networks
Zhongyuan Zhao, Bojan Radojičić, Gunjan Verma +2
To reduce the latency of Backpressure (BP) routing in wireless multi-hop networks, we propose to enhance the existing shortest path-biased BP (SP-BP) and sojourn time-based backlog…
Efficient Path Planning with Soft Homology Constraints
Carlos A. Taveras, Santiago Segarra, César A. Uribe
We study the problem of path planning with soft homology constraints on a surface topologically equivalent to a disk with punctures. Specifically, we propose an algorithm, named $\…
Fair GLASSO: Estimating Fair Graphical Models with Unbiased Statistical Behavior
Madeline Navarro, Samuel Rey, Andrei Buciulea +2
We propose estimating Gaussian graphical models (GGMs) that are fair with respect to sensitive nodal attributes. Many real-world models exhibit unfair discriminatory behavior due t…
Mitigating Subpopulation Bias for Fair Network Topology Inference
Madeline Navarro, Samuel Rey, Andrei Buciulea +2
We consider fair network topology inference from nodal observations. Real-world networks often exhibit biased connections based on sensitive nodal attributes. Hence, different subp…
Estimation of partially known Gaussian graphical models with score-based structural priors
Martín Sevilla, Antonio García Marques, Santiago Segarra
We propose a novel algorithm for the support estimation of partially known Gaussian graphical models that incorporates prior information about the underlying graph. In contrast to…
GraSSRep: Graph-Based Self-Supervised Learning for Repeat Detection in Metagenomic Assembly
Ali Azizpour, Advait Balaji, Todd J. Treangen +1
Repetitive DNA (repeats) poses significant challenges for accurate and efficient genome assembly and sequence alignment. This is particularly true for metagenomic data, where genom…
Congestion-aware Distributed Task Offloading in Wireless Multi-hop Networks Using Graph Neural Networks
Zhongyuan Zhao, Jake Perazzone, Gunjan Verma +1
Computational offloading has become an enabling component for edge intelligence in mobile and smart devices. Existing offloading schemes mainly focus on mobile devices and servers,…
Learning Non-myopic Power Allocation in Constrained Scenarios
Arindam Chowdhury, Santiago Paternain, Gunjan Verma +2
We propose a learning-based framework for efficient power allocation in ad hoc interference networks under episodic constraints. The problem of optimal power allocation -- for maxi…
Joint channel estimation and data detection in massive MIMO systems based on diffusion models
Nicolas Zilberstein, Ananthram Swami, Santiago Segarra
We propose a joint channel estimation and data detection algorithm for massive multilple-input multiple-output systems based on diffusion models. Our proposed method solves the bli…
ML-Based Feedback-Free Adaptive MCS Selection for Massive Multi-User MIMO
Qing An, Mehdi Zafari, Chris Dick +3
As wireless communication systems strive to improve spectral efficiency, there has been a growing interest in employing machine learning (ML)-based approaches for adaptive modulati…
Solving Max-Min Fair Resource Allocations Quickly on Large Graphs
Pooria Namyar, Behnaz Arzani, Srikanth Kandula +5
We consider the max-min fair resource allocation problem. The best-known solutions use either a sequence of optimizations or waterfilling, which only applies to a narrow set of cas…
Enhanced Backpressure Routing Using Wireless Link Features
Zhongyuan Zhao, Gunjan Verma, Ananthram Swami +1
Backpressure (BP) routing is a well-established framework for distributed routing and scheduling in wireless multi-hop networks. However, the basic BP scheme suffers from poor end-…
Recovering Missing Node Features with Local Structure-based Embeddings
Victor M. Tenorio, Madeline Navarro, Santiago Segarra +1
Node features bolster graph-based learning when exploited jointly with network structure. However, a lack of nodal attributes is prevalent in graph data. We present a framework to…