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Found 15 papers, 4 papers with code
MAPL: Model Agnostic Peer-to-peer Learning
Effective collaboration among heterogeneous clients in a decentralized setting is a rather unexplored avenue in the literature.
Constrained Hierarchical Clustering via Graph Coarsening and Optimal Cuts
Motivated by extracting and summarizing relevant information in short sentence settings, such as satisfaction questionnaires, hotel reviews, and X/Twitter, we study the problem of clustering words in a hierarchical fashion.
Renewable-Based Charging in Green Ride-Sharing
We propose a new mechanism to promote EV charging during hours of high renewable generation, and we introduce the concept of charge request, which is issued by a power utility company.
Incentives and co-evolution: Steering linear dynamical systems with noncooperative agents
Modern socio-technical systems typically consist of many interconnected users and competing service providers, where notions like market equilibrium are tightly connected to the ``evolution'' of the network of users.
Achievement and Fragility of Long-term Equitability
In this paper, we investigate how to allocate limited resources to {locally interacting} communities in a way to maximize a pertinent notion of equitability.
Personalized incentives as feedback design in generalized Nash equilibrium problems
We investigate both stationary and time-varying, nonmonotone generalized Nash equilibrium problems that exhibit symmetric interactions among the agents, which are known to be potential.
Learning equilibria with personalized incentives in a class of nonmonotone games
We consider quadratic, nonmonotone generalized Nash equilibrium problems with symmetric interactions among the agents.
OpReg-Boost: Learning to Accelerate Online Algorithms with Operator Regression
This paper presents a new regularization approach -- termed OpReg-Boost -- to boost the convergence and lessen the asymptotic error of online optimization and learning algorithms.
Distributed Personalized Gradient Tracking with Convex Parametric Models
We present a distributed optimization algorithm for solving online personalized optimization problems over a network of computing and communicating nodes, each of which linked to a specific user.
Extrapolation-based Prediction-Correction Methods for Time-varying Convex Optimization
In this paper, we focus on the solution of online optimization problems that arise often in signal processing and machine learning, in which we have access to streaming sources of data.
Multi-block ADMM Heuristics for Mixed-Binary Optimization on Classical and Quantum Computers
Solving combinatorial optimization problems on current noisy quantum devices is currently being advocated for (and restricted to) binary polynomial optimization with equality constraints via quantum heuristic approaches.
Combinatorial Optimization
Quantum Physics
Optimization and Control
Optimization and Learning with Information Streams: Time-varying Algorithms and Applications
Approaches for the design of time-varying or online first-order optimization methods are discussed, with emphasis on algorithms that can handle errors in the gradient, as may arise when the gradient is estimated.
Inexact Online Proximal-gradient Method for Time-varying Convex Optimization
The online proximal-gradient method is inexact, in the sense that: (i) it relies on an approximate first-order information of the smooth component of the cost; and, (ii) the proximal operator (with respect to the non-smooth term) may be computed only up to a certain precision.
Pursuit of Low-Rank Models of Time-Varying Matrices Robust to Sparse and Measurement Noise
In tracking of time-varying low-rank models of time-varying matrices, we present a method robust to both uniformly-distributed measurement noise and arbitrarily-distributed ``sparse'' noise.
Autoregressive Moving Average Graph Filtering
We design a family of autoregressive moving average (ARMA) recursions, which (i) are able to approximate any desired graph frequency response, and (ii) give exact solutions for tasks such as graph signal denoising and interpolation.
