Search Results for author:
Found 23 papers, 3 papers with code
Laplace-HDC: Understanding the geometry of binary hyperdimensional computing
This paper studies the geometry of binary hyperdimensional computing (HDC), a computational scheme in which data are encoded using high-dimensional binary vectors.
Fully Zeroth-Order Bilevel Programming via Gaussian Smoothing
In this paper, we study and analyze zeroth-order stochastic approximation algorithms for solving bilvel problems, when neither the upper/lower objective values, nor their unbiased gradient estimates are available.
Certain and Approximately Certain Models for Statistical Learning
Real-world data is often incomplete and contains missing values.
Survey of Distributed Algorithms for Resource Allocation over Multi-Agent Systems
This survey paper provides a comprehensive analysis of distributed algorithms for addressing the distributed resource allocation (DRA) problem over multi-agent systems.
Accelerated Distributed Allocation
The proposed algorithm is all-time feasible, implying that at any termination time of the algorithm, the resource-demand feasibility holds.
Robust-to-Noise Algorithms for Distributed Resource Allocation and Scheduling
Efficient resource allocation and scheduling algorithms are essential for various distributed applications, ranging from wireless networks and cloud computing platforms to autonomous multi-agent systems and swarm robotic networks.
Discretized Distributed Optimization over Dynamic Digraphs
This work, particularly, is an improvement over existing stochastic-weight undirected networks in case of link removal or packet drops.
Distributed Delay-Tolerant Strategies for Equality-Constraint Sum-Preserving Resource Allocation
This paper proposes two nonlinear dynamics to solve constrained distributed optimization problem for resource allocation over a multi-agent network.
D-SVM over Networked Systems with Non-Ideal Linking Conditions
The agents solve a consensus-constraint distributed optimization cooperatively via continuous-time dynamics, while the links are subject to strongly sign-preserving odd nonlinear conditions.
Distributed Constraint-Coupled Optimization over Lossy Networks
This paper considers distributed resource allocation and sum-preserving constrained optimization over lossy networks, where the links are unreliable and subject to packet drops.
Distributed CPU Scheduling Subject to Nonlinear Constraints
This paper considers a network of collaborating agents for local resource allocation subject to nonlinear model constraints.
RIGID: Robust Linear Regression with Missing Data
With the significant increase in using robust optimization techniques to train machine learning models, this paper presents a novel robust regression framework that operates by minimizing the uncertainty associated with missing data.
Distributed Finite-Sum Constrained Optimization subject to Nonlinearity on the Node Dynamics
Motivated by recent development in networking and parallel data-processing, we consider a distributed and localized finite-sum (or fixed-sum) allocation technique to solve resource-constrained convex optimization problems over multi-agent networks (MANs).
1st-Order Dynamics on Nonlinear Agents for Resource Allocation over Uniformly-Connected Networks
A general nonlinear $1$st-order consensus-based solution for distributed constrained convex optimization is proposed with network resource allocation applications.
Distributed support-vector-machine over dynamic balanced directed networks
In this paper, we consider the binary classification problem via distributed Support-Vector-Machines (SVM), where the idea is to train a network of agents, with limited share of data, to cooperatively learn the SVM classifier for the global database.
Constrained Reinforcement Learning With Learned Constraints
In this work, given a reward function and a set of demonstrations from an expert that maximizes this reward function while respecting \textit{unknown} constraints, we propose a framework to learn the most likely constraints that the expert respects.
Fast-Convergent Dynamics for Distributed Allocation of Resources Over Switching Sparse Networks with Quantized Communication Links
The idea is to optimally allocate the resources among the group of agents by minimizing the overall cost function subject to fixed sum of resources.
Inverse Constrained Reinforcement Learning
However, for the real world deployment of reinforcement learning (RL), it is critical that RL agents are aware of these constraints, so that they can act safely.
Learning To Solve Differential Equations Across Initial Conditions
Recently, there has been a lot of interest in using neural networks for solving partial differential equations.
Fast Convex Pruning of Deep Neural Networks
We develop a fast, tractable technique called Net-Trim for simplifying a trained neural network.
Net-Trim: Convex Pruning of Deep Neural Networks with Performance Guarantee
This program seeks a sparse set of weights at each layer that keeps the layer inputs and outputs consistent with the originally trained model.
Sweep Distortion Removal from THz Images via Blind Demodulation
Heavy sweep distortion induced by alignments and inter-reflections of layers of a sample is a major burden in recovering 2D and 3D information in time resolved spectral imaging.
Learning Shapes by Convex Composition
We present a mathematical and algorithmic scheme for learning the principal geometric elements in an image or 3D object.
