Predicting Sequences with Unreliable Oracles
Key takeaways
- Sequential prediction can be challenged by unreliable "lying oracles."
- Algorithms are proposed for both stochastic and adversarial environments.
- Logarithmic regret bounds are established for these algorithms.
- The research addresses prediction complexity with deceptive information.
Who benefits
Summary
This paper addresses sequential prediction of m-ary sequences where a learner incurs costs based on predictions made via comparative queries to a "lying oracle," proposing algorithms for both stochastic and adversarial environments with logarithmic regret bounds.
Why it matters
Professionals dealing with prediction systems that rely on potentially unreliable or noisy information sources can benefit from algorithms designed to maintain performance and minimize regret under such challenging conditions.
How to implement this in your domain
- 1Assess the reliability of information sources used in current prediction models, identifying potential "lying oracle" scenarios.
- 2Explore integrating robust prediction algorithms that account for noisy or deceptive feedback.
- 3Develop strategies for managing uncertainty and minimizing regret in decision-making processes based on imperfect information.
- 4Apply the principles of this research to areas like fraud detection or adversarial machine learning where inputs might be intentionally misleading.
Original post by Puspabeethi Samanta, Nikhil Karamchandani, Jayakrishnan Nair
"arXiv:2608.14102v1 Announce Type: new Abstract: We consider the problem of sequential prediction of an $m$-ary sequence, where at each epoch, (i) the environment selects an outcome from an $m$-ary alphabet, (ii) the learner selects a probability distribution over the same alphabe…"
View on XOriginally posted by Puspabeethi Samanta, Nikhil Karamchandani, Jayakrishnan Nair on X · view source
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