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Found 14 papers, 7 papers with code
Rotting Infinitely Many-armed Bandits beyond the Worst-case Rotting: An Adaptive Approach
In this study, we consider the infinitely many armed bandit problems in rotting environments, where the mean reward of an arm may decrease with each pull, while otherwise, it remains unchanged.
On the convergence of loss and uncertainty-based active learning algorithms
This includes demonstrating convergence rate guarantees for loss-based sampling with various loss functions.
Rotting Infinitely Many-armed Bandits
We consider the infinitely many-armed bandit problem with rotting rewards, where the mean reward of an arm decreases at each pull of the arm according to an arbitrary trend with maximum rotting rate $\varrho=o(1)$.
Scheduling Servers with Stochastic Bilinear Rewards
In this paper, we study scheduling in multi-class, multi-server queueing systems with stochastic rewards of job-server assignments following a bilinear model in feature vectors characterizing jobs and servers.
Pure Exploration and Regret Minimization in Matching Bandits
Finding an optimal matching in a weighted graph is a standard combinatorial problem.
Scheduling Jobs with Stochastic Holding Costs
Our numerical results demonstrate the efficacy of our algorithms and show that our regret analysis is nearly tight.
Test Score Algorithms for Budgeted Stochastic Utility Maximization
Motivated by recent developments in designing algorithms based on individual item scores for solving utility maximization problems, we study the framework of using test scores, defined as a statistic of observed individual item performance data, for solving the budgeted stochastic utility maximization problem.
Accelerated MM Algorithms for Ranking Scores Inference from Comparison Data
In this paper, we study a popular method for inference of the Bradley-Terry model parameters, namely the MM algorithm, for maximum likelihood estimation and maximum a posteriori probability estimation.
KONG: Kernels for ordered-neighborhood graphs
We present novel graph kernels for graphs with node and edge labels that have ordered neighborhoods, i. e. when neighbor nodes follow an order.
Adaptive Matching for Expert Systems with Uncertain Task Types
To address this challenge, we develop a model of a task-expert matching system where a task is matched to an expert using not only the prior information about the task but also the feedback obtained from the past matches.
QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding
In this paper, we propose Quantized SGD (QSGD), a family of compression schemes which allow the compression of gradient updates at each node, while guaranteeing convergence under standard assumptions.
Streaming Min-max Hypergraph Partitioning
In many applications, the data is of rich structure that can be represented by a hypergraph, where the data items are represented by vertices and the associations among items are represented by hyperedges.
SerialRank: Spectral Ranking using Seriation
Intuitively, the algorithm assigns similar rankings to items that compare similarly with all others.
Spectral Ranking using Seriation
We first show that this spectral seriation algorithm recovers the true ranking when all pairwise comparisons are observed and consistent with a total order.
