The paper investigates higher-dimensional confidence sequences for monitoring multiple data streams. It introduces three extensions of one-dimensional betting-based CSs—a weighted Bonferroni region, a max‑wealth form, and a portfolio region—highlighting that the portfolio is tighter but computationally challenging. To make it practical, the authors propose tractable outer approximations (bounding box, π‑ellipsoid, and their intersection) that preserve validity and outperform existing methods in experiments, enabling applications such as multi‑metric A/B testing and model comparison.
arXiv:2608.28646v1 Announce Type: new Abstract: Large language models (LLMs) can generate plausible-sounding ETF portfolios while silently violating basic KYC-style constraints on risk, fees, and div...
arXiv:2605. 27887v2 Announce Type: replace Abstract: Large language models (LLMs) have shown strong performance across diverse financial tasks, yet portfolio management (PM), a critical financial decision-making task, remains poorly benchmarked.
The paper introduces adaptive policy portfolios for robust Markov decision processes, where a finite set of memoryless randomized policies is synthesized offline and paired with an online selector. It defines robust regret as a measure of portfolio quality, comparing each portfolio member to the optimal policy for each plausible environment. The authors provide a complexity-theoretic analysis of portfolio certification and synthesis, showing that even deterministic portfolios in simple settings are highly complex, and present an offline construction method that can be specialized at runtime.
The paper studies high‑dimensional linear contextual bandits with knapsack constraints (CBwK), aiming to exploit sparsity for tighter regret bounds. It introduces an online hard‑thresholding estimator integrated into a primal‑dual framework, achieving sub‑linear regret that grows only logarithmically with the feature dimension. Under either a diverse‑covariate or margin condition, the regret improves to τ‑dependent rates, and when both hold simultaneously, a dual resolving scheme yields an even tighter bound. The approach also recovers optimal rates for high‑dimensional contextual bandits without knapsacks, and experiments demonstrate its practical effectiveness.
We study ridge regression from exactly $s$ distinct rows of a fixed design. Responses are fixed, and only the subset is random. The determinant law and selected ridge fit share one positive definite p...
arXiv:2610.07721v1 Announce Type: cross Abstract: We study ridge regression from exactly $s$ distinct rows of a fixed design. Responses are fixed, and only the subset is random. The determinant law a...
The paper investigates adaptive policy portfolios for Robust Markov Decision Processes (RMDPs), proposing finite sets of memoryless randomized policies generated offline and selected online. It introduces robust regret as a metric for portfolio quality, comparing each portfolio member’s performance to the optimal policy for each plausible environment. The authors provide complexity-theoretic results showing that certifying and synthesizing such portfolios is highly intractable, and they present an offline construction method that can be specialized at runtime.
arXiv:2502. 18834v3 Announce Type: replace-cross Abstract: Financial time series (FinTS) record the behavior of human-brain-augmented decision-making, capturing valuable historical information that can be leveraged for profitable investment strategies.
arXiv:2512. 14991v2 Announce Type: replace Abstract: We study reinforcement learning for controlled diffusion processes with unbounded continuous state spaces, bounded continuous actions, and polynomially growing rewards: settings that arise naturally in finance, economics, and operations research.
arXiv:2307. 03587v4 Announce Type: replace Abstract: In non-stationary linear contextual bandits, existing efficient algorithms typically rely on the Weighted Regularized Least-Squares (WRLS) estimator.
arXiv:2604. 05859v2 Announce Type: replace Abstract: We study Contextual Multi-Armed Bandits (CMABs) for non-episodic decision-making problems where the context includes both textual and numerical information (e.
arXiv:2512. 09850v2 Announce Type: replace Abstract: We introduce Conformal Bandits, a novel framework integrating Conformal Prediction (CP) into bandit problems, a classic paradigm for sequential decision-making under uncertainty.