Mathematics of Operations Research
Optimization flow control—I: basic algorithm and convergence
IEEE/ACM Transactions on Networking (TON)
Approximation in stochastic scheduling: the power of LP-based priority policies
Journal of the ACM (JACM)
Scheduling precedence-constrained jobs with stochastic processing times on parallel machines
SODA '01 Proceedings of the twelfth annual ACM-SIAM symposium on Discrete algorithms
Dynamic Programming and Optimal Control
Dynamic Programming and Optimal Control
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Allocating Bandwidth for Bursty Connections
SIAM Journal on Computing
The Sample Average Approximation Method for Stochastic Discrete Optimization
SIAM Journal on Optimization
Q2: Memory-Based Active Learning for Optimizing Noisy Continuous Functions
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Stochastic Load Balancing and Related Problems
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
Approximating the Stochastic Knapsack Problem: The Benefit of Adaptivity
FOCS '04 Proceedings of the 45th Annual IEEE Symposium on Foundations of Computer Science
Stochastic Optimization is (Almost) as easy as Deterministic Optimization
FOCS '04 Proceedings of the 45th Annual IEEE Symposium on Foundations of Computer Science
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Adaptivity and approximation for stochastic packing problems
SODA '05 Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms
Approximation algorithms for stochastic scheduling problems
Approximation algorithms for stochastic scheduling problems
Asking the right questions: model-driven optimization using probes
Proceedings of the twenty-fifth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Model-driven optimization using adaptive probes
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
Definition and complexity of some basic metareasoning problems
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Sampling bounds for stochastic optimization
APPROX'05/RANDOM'05 Proceedings of the 8th international workshop on Approximation, Randomization and Combinatorial Optimization Problems, and Proceedings of the 9th international conference on Randamization and Computation: algorithms and techniques
The ratio index for budgeted learning, with applications
SODA '09 Proceedings of the twentieth Annual ACM-SIAM Symposium on Discrete Algorithms
Approximation algorithms for restless bandit problems
SODA '09 Proceedings of the twentieth Annual ACM-SIAM Symposium on Discrete Algorithms
Large-scale uncertainty management systems: learning and exploiting your data
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Approximation algorithms for restless bandit problems
Journal of the ACM (JACM)
SODA '10 Proceedings of the twenty-first annual ACM-SIAM symposium on Discrete Algorithms
Approximation algorithms for optimal decision trees and adaptive TSP problems
ICALP'10 Proceedings of the 37th international colloquium conference on Automata, languages and programming
Paradoxes in Learning and the Marginal Value of Information
Decision Analysis
The Irrevocable Multiarmed Bandit Problem
Operations Research
Interactive learning for efficiently detecting errors in insurance claims
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient Learning with Partially Observed Attributes
The Journal of Machine Learning Research
Approximation algorithms for stochastic orienteering
Proceedings of the twenty-third annual ACM-SIAM symposium on Discrete Algorithms
New algorithms for budgeted learning
Machine Learning
A stochastic probing problem with applications
IPCO'13 Proceedings of the 16th international conference on Integer Programming and Combinatorial Optimization
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We present the first approximation algorithms for a large class of budgeted learning problems. One classicexample of the above is the budgeted multi-armed bandit problem. In this problem each arm of the bandithas an unknown reward distribution on which a prior isspecified as input. The knowledge about the underlying distribution can be refined in the exploration phase by playing the arm and observing the rewards. However, there is a budget on the total number of plays allowed during exploration. After this exploration phase,the arm with the highest (posterior) expected reward is hosen for exploitation. The goal is to design the adaptive exploration phase subject to a budget constraint on the number of plays, in order to maximize the expected reward of the arm chosen for exploitation. While this problem is reasonably well understood in the infinite horizon discounted reward setting, the budgeted version of the problem is NP-Hard. For this problem and several generalizations, we provide approximate policies that achieve a reward within constant factor of the reward optimal policy. Our algorithms use a novel linear program rounding technique based on stochastic packing.