arXiv:2606.14640v1 Announce Type: new Abstract: We study Online Convex Optimization (OCO) over a convex set $K\subseteq \mathbb R^d$, where in each round $t$ the learner selects $x_t\in K$ and then observes a convex loss $f_t:K\to[0,1]$, with the goal of minimizing regret to the best fixed decision in hindsight. We introduce a unified probing model that generalizes two recent lines of work: sublinear best-expert queries in the experts setting, and pairwise (comparison-based) feedback available every round in OCO. In our framework, the learner has a budget of $k\le T$ pairwise probes; on a prob
The paper represents a continued push for more efficient and robust machine learning algorithms, particularly in online learning settings where real-time adaptability and limited feedback are common.
Improved online convex optimization, especially with sublinear noisy probes, could lead to more resource-efficient and practically deployable AI systems, enhancing adaptability in dynamic environments.
This research provides a new theoretical framework for online learning under constrained feedback, potentially influencing the design of adaptive algorithms for AI agents and real-time decision-making systems.
- · AI algorithm developers
- · Robotics
- · Autonomous systems
- · Online learning platforms
- · Systems requiring extensive labelled data
- · Inefficient AI models
More efficient and robust online machine learning algorithms will emerge from this theoretical advancement.
This could accelerate the development of AI agents capable of learning and adapting with minimal and noisy feedback in complex environments.
These advancements might contribute to the broader adoption of AI in applications where data collection is expensive or real-time adaptation is critical, such as certain forms of autonomous control or resource optimization.
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Read at arXiv cs.LG