arXiv Machine Learning
Jul 15
A Geometric Approach to Constrained Online Learning
arXiv:2605. 21107v2 Announce Type: replace Abstract: We study constrained online convex optimization with adversarial time-varying constraints.
Tracking a Small Set of Experts in Online Convex Optimization
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arXiv Statistics ML
Oct 26
Strongly Adaptive Online Learning with Time-Varying Movement Costs
arXiv:2610.32740v1 Announce Type: cross Abstract: We study strongly adaptive regret for Online Convex Optimization (OCO) with time-varying movement costs, a setting that evaluates a learner's perform...
arXiv Machine Learning
Sep 24
Tight Regret Bound for Online Inverse Linear Optimization via Multiscale Matrix Weights
arXiv:2609. 26978v1 Announce Type: cross Abstract: We study online inverse linear optimization with a fixed unknown linear utility: in each round, an environment presents a compact action set, the learner recommends an action from it, and the environment returns an action that maximizes the utility over the same set.
arXiv Statistics ML
Oct 1
Lower Bounds for Linear-Oracle Online Learning
arXiv:2609.38375v1 Announce Type: new Abstract: Can a constant number of linear minimizations per round improve on the $T^{3/4}$ regret rate of online Frank-Wolfe on general convex sets? Weibel et al...
arXiv Machine Learning
Aug 27
Minimax Alternating Regret for the Experts Problem and Online Convex Optimization
arXiv:2608. 25182v1 Announce Type: cross Abstract: In this paper, we study alternating regret in online convex optimization (OCO), motivated by the success of alternating learning dynamics in two-player games.
Hugging Face Trending Papers
Oct 6
Optimal and Efficient Online Inverse Optimization
In online inverse linear optimization, a learner recommends an action and then observes the choice of an expert who maximizes a fixed, unknown linear objective on $\mathbb{R}^{d}$; the goal is to lear...
arXiv Machine Learning
Sep 24
Prediction with Expert Advice: Anytime Regret with Many Experts Matches the Fixed-Time Constant
arXiv:2609.27206v1 Announce Type: cross Abstract: Prediction with expert advice is a fundamental problem in online learning. When the time horizon $T$ is known in advance, the minimax cumulative regr...
arXiv Machine Learning
Oct 7
Optimal and Efficient Online Inverse Optimization
arXiv:2610. 08735v1 Announce Type: new Abstract: In online inverse linear optimization, a learner recommends an action and then observes the choice of an expert who maximizes a fixed, unknown linear objective on $\mathbb{R}^{d}$; the goal is to learn to optimize this objective without observing it.
arXiv Machine Learning
Aug 31
Online Learning-to-Defer with Varying Experts
arXiv:2605.12340v5 Announce Type: replace-cross Abstract: Learning-to-Defer (L2D) methods route each query either to a predictive model or to external experts. Real-world deployments require handling...
arXiv Machine Learning
Oct 7
On the Computational Tractability of Robust Bandits
The paper studies robust bandits, a framework for learning when the true environment lies outside the learner’s hypothesis class. It presents a polynomial‑time algorithm achieving ≠O(√T) regret for a specific tractable case, while showing that slight extensions of this case are NP‑hard. The work highlights the computational limits of robust bandits and suggests their relevance to AI alignment.
arXiv Machine Learning
Sep 18
The First-Order Oracle Complexity of Lipschitz Convex Optimization in Nondual Settings
arXiv:2609. 20687v1 Announce Type: cross Abstract: We study first-order black-box convex optimization over an $\ell_p$-ball for objectives Lipschitz in the $\ell_q$-norm, solving in the affirmative the nonsmooth version of the COLT open question (Guz15b) on whether the geometry of a smaller feasible set ($p < q$) can improve convergence rates in convex optimization, and matching prior lower bounds up to logarithmic factors.
arXiv Machine Learning
Aug 18
Online Convex Optimization with Dueling Feedback
arXiv:2608. 15050v1 Announce Type: new Abstract: We study online convex optimization with dueling (pairwise comparison) feedback, where the learner observes only a binary preference between two queried points.