Rahul Vaze
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View article: Multi-Objective $\textit{min-max}$ Online Convex Optimization
Multi-Objective $\textit{min-max}$ Online Convex Optimization Open
In online convex optimization (OCO), a single loss function sequence is revealed over a time horizon of $T$, and an online algorithm has to choose its action at time $t$, before the loss function at time $t$ is revealed. The goal of the on…
View article: Online Convex Optimization with Switching Cost with Only One Single Gradient Evaluation
Online Convex Optimization with Switching Cost with Only One Single Gradient Evaluation Open
Online convex optimization with switching cost is considered under the frugal information setting where at time $t$, before action $x_t$ is taken, only a single function evaluation and a single gradient is available at the previously chose…
View article: Online Bidding Algorithms with Strict Return on Spend (ROS) Constraint
Online Bidding Algorithms with Strict Return on Spend (ROS) Constraint Open
Auto-bidding problem under a strict return-on-spend constraint (ROSC) is considered, where an algorithm has to make decisions about how much to bid for an ad slot depending on the revealed value, and the hidden allocation and payment funct…
View article: $O(\sqrt{T})$ Static Regret and Instance Dependent Constraint Violation for Constrained Online Convex Optimization
$O(\sqrt{T})$ Static Regret and Instance Dependent Constraint Violation for Constrained Online Convex Optimization Open
The constrained version of the standard online convex optimization (OCO) framework, called COCO is considered, where on every round, a convex cost function and a convex constraint function are revealed to the learner after it chooses the a…
View article: Multipartite Entanglement Measure : Genuine to Absolutely Maximally Entangled
Multipartite Entanglement Measure : Genuine to Absolutely Maximally Entangled Open
Multipartite entanglement is a fundamental aspect of quantum mechanics, crucial to advancements in quantum information processing and quantum computation. Within this field, Genuinely Multipartite Entanglement (GME), being entangled in all…
View article: Tight Bounds for Online Convex Optimization with Adversarial Constraints
Tight Bounds for Online Convex Optimization with Adversarial Constraints Open
A well-studied generalization of the standard online convex optimization (OCO) is constrained online convex optimization (COCO). In COCO, on every round, a convex cost function and a convex constraint function are revealed to the learner a…
View article: Scheduling Multi-Server Jobs is Not Easy
Scheduling Multi-Server Jobs is Not Easy Open
The problem of online scheduling of multi-server jobs is considered, where there are a total of $K$ servers, and each job requires concurrent service from multiple servers for it to be processed. Each job on its arrival reveals its process…
View article: Secured and Privacy in Exam Cell Management System Using Blockchain Mechanism with Advanced Encryption Standard (AES) Algorithm
Secured and Privacy in Exam Cell Management System Using Blockchain Mechanism with Advanced Encryption Standard (AES) Algorithm Open
This study proposes a novel approach to enhance the security and privacy of Exam Cell Management Systems (ECMS) by integrating blockchain technology with the Advanced Encryption Standard (AES) algorithm. Traditional systems face challenges…
View article: Capacity Provisioning Motivated Online Non-Convex Optimization Problem with Memory and Switching Cost
Capacity Provisioning Motivated Online Non-Convex Optimization Problem with Memory and Switching Cost Open
An online non-convex optimization problem is considered where the goal is to minimize the flow time (total delay) of a set of jobs by modulating the number of active servers, but with a switching cost associated with changing the number of…
View article: Optimal Algorithms for Online Convex Optimization with Adversarial Constraints
Optimal Algorithms for Online Convex Optimization with Adversarial Constraints Open
A well-studied generalization of the standard online convex optimization (OCO) framework is constrained online convex optimization (COCO). In COCO, on every round, a convex cost function and a convex constraint function are revealed to the…
View article: Online convex optimization with switching cost and delayed gradients
Online convex optimization with switching cost and delayed gradients Open
View article: On Dynamic Regret and Constraint Violations in Constrained Online Convex Optimization
On Dynamic Regret and Constraint Violations in Constrained Online Convex Optimization Open
A constrained version of the online convex optimization (OCO) problem is considered. With slotted time, for each slot, first an action is chosen. Subsequently the loss function and the constraint violation penalty evaluated at the chosen a…
View article: Online Facility Location with Weights and Congestion
Online Facility Location with Weights and Congestion Open
The classic online facility location problem deals with finding the optimal set of facilities in an online fashion when demand requests arrive one at a time and facilities need to be opened to service these requests. In this work, we study…
View article: A Rare Case of Non HFE Haemochromatosis
A Rare Case of Non HFE Haemochromatosis Open
View article: Non-asymptotic near optimal algorithms for two sided matchings
Non-asymptotic near optimal algorithms for two sided matchings Open
A two-sided matching system is considered, where servers are assumed to arrive at a fixed rate, while the arrival rate of customers is modulated via a price-control mechanism. We analyse a loss model, wherein customers who are not served i…
View article: Minimizing Age of Information under Arbitrary Arrival Model with Arbitrary Packet Size
Minimizing Age of Information under Arbitrary Arrival Model with Arbitrary Packet Size Open
We consider a single source-destination pair, where information updates arrive at the source at arbitrary time instants. For each update, its size, i.e. the service time required for complete transmission to the destination, is also arbitr…
View article: Scheduling for Multi-Phase Parallelizable Jobs
Scheduling for Multi-Phase Parallelizable Jobs Open
With multiple identical unit speed servers, the online problem of scheduling jobs that migrate between two phases, limitedly parallelizable or completely sequential, and choosing their respective speeds to minimize the total flow time is c…
View article: Scheduling to Minimize Age of Information with Multiple Sources
Scheduling to Minimize Age of Information with Multiple Sources Open
We consider a G/G/1 queueing system with a single server, where updates arrive from different sources stochastically with possibly different update inter-generation time distributions. The server can transmit/serve at most one update at an…
View article: Speed Scaling on Parallel Servers With MapReduce Type Precedence Constraints
Speed Scaling on Parallel Servers With MapReduce Type Precedence Constraints Open
A multiple server setting is considered, where each server has tunable speed, and increasing the speed incurs an energy cost. Jobs arrive to a single queue, and each job has two types of sub-tasks, map and reduce, and a {\bf precedence} co…
View article: Mental health outcome among psychiatric patients due to COVID 19 lockdown induced disruption of access to psychiatric services
Mental health outcome among psychiatric patients due to COVID 19 lockdown induced disruption of access to psychiatric services Open
Background: COVID 19 lockdown has an impact on the mental health of the general population, COVID patients, and health professionals. However, knowledge about its impact on psychiatric patients is limited. Objectives: To assess the mental …
View article: Scheduling to Learn In An Unsupervised Online Streaming Model
Scheduling to Learn In An Unsupervised Online Streaming Model Open
An unsupervised online streaming model is considered where samples arrive in an online fashion over $T$ slots. There are $M$ classifiers, whose confusion matrices are unknown a priori. In each slot, at most one sample can be labeled by any…
View article: Speed Scaling with Multiple Servers Under A Sum Power Constraint
Speed Scaling with Multiple Servers Under A Sum Power Constraint Open
The problem of scheduling jobs and choosing their respective speeds with multiple servers under a sum power constraint to minimize the flow time + energy is considered. This problem is a generalization of the flow time minimization problem…
View article: On the Age of Information of a Queuing System with Heterogeneous Servers
On the Age of Information of a Queuing System with Heterogeneous Servers Open
An optimal control problem with heterogeneous servers to minimize the average age of information (AoI) is considered. Each server maintains a separate queue, and each packet arriving to the system is randomly routed to one of the servers. …
View article: Continuous Time Bandits With Sampling Costs
Continuous Time Bandits With Sampling Costs Open
We consider a continuous-time multi-arm bandit problem (CTMAB), where the learner can sample arms any number of times in a given interval and obtain a random reward from each sample, however, increasing the frequency of sampling incurs an …
View article: Speed Scaling On Parallel Servers with MapReduce Type Precedence\n Constraints
Speed Scaling On Parallel Servers with MapReduce Type Precedence\n Constraints Open
A multiple server setting is considered, where each server has tunable speed,\nand increasing the speed incurs an energy cost. Jobs arrive to a single queue,\nand each job has two types of sub-tasks, map and reduce, and a {\\bf precedence}…
View article: Minimizing the Sum of Age of Information and Transmission Cost under Stochastic Arrival Model
Minimizing the Sum of Age of Information and Transmission Cost under Stochastic Arrival Model Open
We consider a node-monitor pair, where updates are generated stochastically (according to a known distribution) at the node that it wishes to send to the monitor. The node is assumed to incur a fixed cost for each transmission, and the obj…
View article: Online Energy Minimization Under A Peak Age of Information Constraint
Online Energy Minimization Under A Peak Age of Information Constraint Open
We consider a node where packets of fixed size (in bits) are generated at arbitrary intervals. The node is required to maintain the peak age of information (AoI) at the monitor below a threshold by transmitting potentially a subset of the …
View article: Not Just Age but Age and Quality of Information
Not Just Age but Age and Quality of Information Open
A versatile scheduling problem to model a three-way tradeoff between delay/age, distortion, and energy is considered. The considered problem called the age and quality of information (AQI) is to select which packets to transmit at each tim…
View article: Breaking the Unit Throughput Barrier in Distributed Systems
Breaking the Unit Throughput Barrier in Distributed Systems Open
A multi-level random power transmit strategy that is used in conjunction with a random access protocol (RAP) (e.g. ALOHA, IRSA) is proposed to fundamentally increase the throughput in a distributed communication network. A SIR model is con…
View article: Throughput Maximization With an Average Age of Information Constraint in Fading Channels
Throughput Maximization With an Average Age of Information Constraint in Fading Channels Open
In the emerging fifth generation (5G) technology, communication nodes are expected to support two crucial classes of information traffic, namely, the enhanced mobile broadband (eMBB) traffic with high data rate requirements, and ultra-reli…