Learning in Prophet Inequalities with Noisy Observations
Fuente:
arXiv
Saved in:
| Main Authors: | Kim, Jung-hun, Perchet, Vianney |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Lookback Prophet Inequalities
by: Benomar, Ziyad, et al.
Published: (2024)
by: Benomar, Ziyad, et al.
Published: (2024)
Instance-dependent Stochastic Lipschitz bandit
by: Potfer, Marius, et al.
Published: (2026)
by: Potfer, Marius, et al.
Published: (2026)
On Tradeoffs in Learning-Augmented Algorithms
by: Benomar, Ziyad, et al.
Published: (2025)
by: Benomar, Ziyad, et al.
Published: (2025)
Online Packet Scheduling with Deadlines and Learning
by: Genalti, Gianmarco, et al.
Published: (2026)
by: Genalti, Gianmarco, et al.
Published: (2026)
The Value of Reward Lookahead in Reinforcement Learning
by: Merlis, Nadav, et al.
Published: (2024)
by: Merlis, Nadav, et al.
Published: (2024)
Comparing Uniform Price and Discriminatory Multi-Unit Auctions through Regret Minimization
by: Potfer, Marius, et al.
Published: (2025)
by: Potfer, Marius, et al.
Published: (2025)
A survey on multi-player bandits
by: Boursier, Etienne, et al.
Published: (2022)
by: Boursier, Etienne, et al.
Published: (2022)
Prophet Inequalities: Competing with the Top $\ell$ Items is Easy
by: Molina, Mathieu, et al.
Published: (2024)
by: Molina, Mathieu, et al.
Published: (2024)
Non-clairvoyant Scheduling with Partial Predictions
by: Benomar, Ziyad, et al.
Published: (2024)
by: Benomar, Ziyad, et al.
Published: (2024)
Covariance-adapting algorithm for semi-bandits with application to sparse rewards
by: Perrault, Pierre, et al.
Published: (2026)
by: Perrault, Pierre, et al.
Published: (2026)
Tracking Most Significant Shifts in Infinite-Armed Bandits
by: Suk, Joe, et al.
Published: (2025)
by: Suk, Joe, et al.
Published: (2025)
Learning to Schedule in Parallel-Server Queues with Stochastic Bilinear Rewards
by: Kim, Jung-hun, et al.
Published: (2021)
by: Kim, Jung-hun, et al.
Published: (2021)
Stable Matching with Ties: Approximation Ratios and Learning
by: Lin, Shiyun, et al.
Published: (2024)
by: Lin, Shiyun, et al.
Published: (2024)
On the Hardness of Reinforcement Learning with Transition Look-Ahead
by: Pla, Corentin, et al.
Published: (2025)
by: Pla, Corentin, et al.
Published: (2025)
The Harder Path: Last Iterate Convergence for Uncoupled Learning in Zero-Sum Games with Bandit Feedback
by: Fiegel, Côme, et al.
Published: (2026)
by: Fiegel, Côme, et al.
Published: (2026)
Queueing Matching Bandits with Preference Feedback
by: Kim, Jung-hun, et al.
Published: (2024)
by: Kim, Jung-hun, et al.
Published: (2024)
Stochastic Matching Bandits with Rare Optimization Updates
by: Kim, Jung-hun, et al.
Published: (2025)
by: Kim, Jung-hun, et al.
Published: (2025)
Dynamic Assortment Selection and Pricing with Censored Preference Feedback
by: Kim, Jung-hun, et al.
Published: (2025)
by: Kim, Jung-hun, et al.
Published: (2025)
Adversarial Bandits against Arbitrary Strategies
by: Kim, Jung-hun, et al.
Published: (2022)
by: Kim, Jung-hun, et al.
Published: (2022)
Strategic Multi-Armed Bandit Problems Under Debt-Free Reporting
by: Yahmed, Ahmed Ben, et al.
Published: (2025)
by: Yahmed, Ahmed Ben, et al.
Published: (2025)
Learning to Allocate Resources with Censored Feedback
by: Montanari, Giovanni, et al.
Published: (2026)
by: Montanari, Giovanni, et al.
Published: (2026)
Adaptive Bandit Algorithms for Contextual Matching Markets
by: Lin, Shiyun, et al.
Published: (2026)
by: Lin, Shiyun, et al.
Published: (2026)
Optimal last-iterate convergence in matrix games with bandit feedback using the log-barrier
by: Fiegel, Come, et al.
Published: (2026)
by: Fiegel, Come, et al.
Published: (2026)
Contextual Linear Bandits under Noisy Features: Towards Bayesian Oracles
by: Kim, Jung-hun, et al.
Published: (2017)
by: Kim, Jung-hun, et al.
Published: (2017)
Oracle-Efficient Combinatorial Semi-Bandits
by: Kim, Jung-hun, et al.
Published: (2025)
by: Kim, Jung-hun, et al.
Published: (2025)
An Adaptive Approach for Infinitely Many-armed Bandits under Generalized Rotting Constraints
by: Kim, Jung-hun, et al.
Published: (2024)
by: Kim, Jung-hun, et al.
Published: (2024)
Multi-Armed Bandits with Minimum Aggregated Revenue Constraints
by: Yahmed, Ahmed Ben, et al.
Published: (2025)
by: Yahmed, Ahmed Ben, et al.
Published: (2025)
Mode Estimation with Partial Feedback
by: Arnal, Charles, et al.
Published: (2024)
by: Arnal, Charles, et al.
Published: (2024)
Improved Algorithms for Contextual Dynamic Pricing
by: Tullii, Matilde, et al.
Published: (2024)
by: Tullii, Matilde, et al.
Published: (2024)
The Competition Complexity of Prophet Inequalities with Correlations
by: Ezra, Tomer, et al.
Published: (2024)
by: Ezra, Tomer, et al.
Published: (2024)
Improved learning rates in multi-unit uniform price auctions
by: Potfer, Marius, et al.
Published: (2025)
by: Potfer, Marius, et al.
Published: (2025)
DU-Shapley: A Shapley Value Proxy for Efficient Dataset Valuation
by: Garrido-Lucero, Felipe, et al.
Published: (2023)
by: Garrido-Lucero, Felipe, et al.
Published: (2023)
Improved Regret and Contextual Linear Extension for Pandora's Box and Prophet Inequality
by: Liu, Junyan, et al.
Published: (2025)
by: Liu, Junyan, et al.
Published: (2025)
TopicProphet: Prophesies on Temporal Topic Trends and Stocks
by: Kim, Olivia
Published: (2025)
by: Kim, Olivia
Published: (2025)
Adaptive Measurement Allocation for Learning Kernelized SVMs Under Noisy Observations
by: Miroszewski, Artur
Published: (2026)
by: Miroszewski, Artur
Published: (2026)
LLM-as-a-Prophet: Understanding Predictive Intelligence with Prophet Arena
by: Yang, Qingchuan, et al.
Published: (2025)
by: Yang, Qingchuan, et al.
Published: (2025)
Calibrated Forecasting and Persuasion
by: Jain, Atulya, et al.
Published: (2024)
by: Jain, Atulya, et al.
Published: (2024)
Probabilistic Machine Learning for Noisy Labels in Earth Observation
by: Kondylatos, Spyros, et al.
Published: (2025)
by: Kondylatos, Spyros, et al.
Published: (2025)
Semi-Supervised Learning with Noisy Proxy Covariates: Generalization Bounds and Distribution Regression
by: Kim, Kwangho, et al.
Published: (2026)
by: Kim, Kwangho, et al.
Published: (2026)
Learning Discriminative Dynamics with Label Corruption for Noisy Label Detection
by: Kim, Suyeon, et al.
Published: (2024)
by: Kim, Suyeon, et al.
Published: (2024)
Similar Items
-
Lookback Prophet Inequalities
by: Benomar, Ziyad, et al.
Published: (2024) -
Instance-dependent Stochastic Lipschitz bandit
by: Potfer, Marius, et al.
Published: (2026) -
On Tradeoffs in Learning-Augmented Algorithms
by: Benomar, Ziyad, et al.
Published: (2025) -
Online Packet Scheduling with Deadlines and Learning
by: Genalti, Gianmarco, et al.
Published: (2026) -
The Value of Reward Lookahead in Reinforcement Learning
by: Merlis, Nadav, et al.
Published: (2024)