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Efficient Parallel Algorithms for Hypergraph Matching
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When and why randomised exploration works (in linear bandits)
arXiv:2502.08870v2 Announce Type: replace Abstract: We provide an approach for the analysis of randomised exploration algorithms like Thompson sampling that does not rely on forced optimism or posterior inflation. With this, we demonstrate that in the $d$-dimensional linear bandit setting, when the action space is smooth and strongly convex, randomised exploration algorithms enjoy an $n$-step regret bound of the order $O(d\sqrt{n} \log(n))$. Notably, this shows for the first time that there...
One-Shot Klein Cutting Planes for Lipschitz Geodesically Convex Optimization in Hyperbolic Space
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Coherent Swap Regret and Channel-Proof Learning
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Rectangular Matrix Multiplication in the Low-Bandwidth Model
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Module Lattice Security (Part II): Module Lattice Reduction via Optimal Sign Selection
arXiv:2604.22900v2 Announce Type: replace Abstract: We extend the CDPR's quantum attack from ideal lattices to module lattices over $2^k$-th cyclotomic rings. Using trace orthogonality of the power basis, we decompose a rank-$d$ module into mutually orthogonal rank-$1$ submodules, and apply CDPR's analysis to each independently and return the shortest candidate. The Hermite factor $\exp(\tilde{O}(\sqrt{n}))$ matches the ideal case, with a module reduction factor $\alpha_d=O(1)$ independent...
Batched Stochastic Linear Bandits with 1-Bit Communication Constraints
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Dimension Reduction via Sum-of-Squares and Improved Clustering Algorithms for Non-Spherical Mixtures
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An Improved Algorithm for Adversarial Linear Contextual Bandits via Reduction
arXiv:2508.11931v3 Announce Type: replace Abstract: We present an oracle-efficient, near-optimal algorithm for linear contextual bandits with adversarial losses and stochastic action sets, only requiring a linear optimization oracle for the action sets in each round. Our approach reduces this setting to misspecification-robust adversarial linear bandits with fixed action sets. Without knowledge of the context distribution or access to a context simulator, the algorithm achieves...
Worst-Case Update Complexity of the Preisach Extremum Stack
new Abstract: The Preisach extremum stack $\Pi_n$ is the minimal sufficient statistic for the class $\mathcal{R}$ of computable rate-independent functionals in the Kolmogorov complexity sense [1]. Its standard update algorithm runs in amortised $O(1)$ time, but adversarial inputs can force $\Theta(k)$ operations per step (where $k$ is the current depth). We establish a three-level complexity picture: (i) any compact exact $\mathcal{R}$-minimal representation incurs $\Theta(k)$ output changes...