Study compares static vs adaptive strategies in two trading models.
problem Optimal execution in static vs adaptive strategies.
method Discrete and continuous time models with permanent and temporary impact.
result Static strategies are nearly as optimal as adaptive ones in the studied models.
Optimal trade execution strategies show adaptive methods reduce costs.
problem Optimal trade execution with short-term price predictive signals.
method Comparison of static and adaptive strategies with transient and instantaneous market impacts.
result Adaptive strategies significantly reduce transaction costs compared to static strategies.
Simple model uses time series momentum to outperform benchmarks in equity and bond markets.
problem Finding systematic excess returns in various markets.
method Time series momentum applied to multiple investable indices without complex parameter estimation.
result Significant outperformance in equity and bond markets, nearly doubling returns.
The paper extends static Systemic Risk Measures to a conditional setting.
problem Investigating how static Systemic Risk Measures can be adapted to a conditional framework.
method Providing a general dual representation result, analyzing Conditional Shortfall Systemic Risk Measures, and providing explicit formulas for exponential preferences.
result Explicit formulas for Conditional Shortfall Systemic Risk Measures and a time consistency property.
SPLICE generates accurate time-series imputations with reliable prediction intervals.
problem Lack of reliability guarantees in time-series imputation models.
method Modular framework combining latent generative imputation with distribution-free prediction intervals.
result SPLICE achieves lowest mean Load-only MSE and best CRPS on various datasets.
This paper reviews methods for discovering patient subgroups from EHR data.
problem Discovering subgroups of patients and co-occurring medical conditions from EHR data.
method Low-rank data approximation methods like matrix and tensor decompositions.
result These methods provide transparent and interpretable insights into patient phenotypes.
Adapting Israel's proof of static black hole uniqueness, we show that the Schwarzschild spacetime is the only static vacuum asymptotically flat spacetime that possesses a suitably defined photon sphere.
Boosting models improve pediatric ICU transfer prediction.
problem Predicting transfer of pediatric patients to ICU.
method Adaptive and gradient boosting classifiers combined into an ensemble model.
result Improved accuracy, sensitivity, specificity, and AUROC over baseline.
Adaptive PCA algorithms for changing environments.
problem Static adversarial regret is not suitable for changing environments.
method Online adaptive algorithms for PCA and variance minimization with sub-linear adaptive regret guarantees.
result The proposed algorithms adapt to changing environments.
In a recent paper, the authors established the uniqueness of photon spheres in static vacuum asymptotically flat spacetimes by adapting Bunting and Masood-ul-Alam's proof of static vacuum black hole uniqueness. Here, we establish uniqueness of suitably defined sub-extremal photon spheres in static electro-vacuum asympt…
New testing method quantifies ML vs AV robustness against evasion.
problem Benchmarking ML vs AV for malware detection.
method Developed new testing methodology to evaluate performance changes on modified files.
result ML systems show greater robustness against evasion than AV products.
In a recent paper the first author established the uniqueness of photon spheres, suitably defined, in static vacuum asymptotically flat spacetimes by adapting Israel's proof of static black hole uniqueness. In this note we establish uniqueness of photon spheres by adapting the argument of Bunting and Masood-ul-Alam, wh…
Prototype for adaptive electron microscopy scans reduces dose and time.
problem Reduce electron microscopy scan time and dose with minimal loss.
method Adaptive partial scanning with reinforcement learning.
result Reinforcement learning trained neural network optimizes scan paths.
Adapts data analysis for growing data, improving generalization guarantees.
problem Challenges of overfitting and statistical validity in adaptive workflows with growing data.
method Generalizes adaptive analysis on dynamic data, incorporating time-varying empirical accuracy bounds and mechanisms.
result First generalization bounds for adaptive analysis on dynamic data, matching prior works' improvement over data splitting.
New algorithm detects and adapts to changes in real-time data streams.
problem Adapting to fast-changing data in real-time systems.
method Concept drift detection followed by prototype-based adaptation.
result Stable and quick adjustments during model adaptation.
In many practical applications of clustering, the objects to be clustered evolve over time, and a clustering result is desired at each time step. In such applications, evolutionary clustering typically outperforms traditional static clustering by producing clustering results that reflect long-term trends while being ro…
The paper defines mass for certain spacetimes with positive cosmological constant.
problem Defining mass for specific spacetimes with positive cosmological constant.
method Proposes and discusses a mass definition for compact static metrics with positive cosmological constant.
result Characterizes de Sitter solution as the only static vacuum metric with zero mass.
AdS uniqueness and black hole energy bounds proven.
problem Proving uniqueness of Anti-de Sitter spacetime and energy bounds for AdS black holes.
method Adapted Wang's proof to static asymptotically locally hyperbolic vacuum metrics and higher-genus horizons.
result Negativity of free energy E − T S E-TS E − T S for AdS black holes with higher-genus horizons. We consider a multi-armed bandit problem in a setting where each arm produces a noisy reward realization which depends on an observable random covariate. As opposed to the traditional static multi-armed bandit problem, this setting allows for dynamically changing rewards that better describe applications where side inf…
Proposes Nash averaging to improve evaluation in machine learning.
problem Overwhelming choices in evaluation suites and attacks have diluted the basic model.
method Detailed analysis of evaluation scenarios leads to Nash averaging, which adapts to data redundancies.
result Nash averaging encourages maximally inclusive evaluation, reducing bias.
Adaptive social media event detection improves accuracy by 350%.
problem Concept drift in event signals over time.
method Continuous concept drift adaptation using machine learning classifiers.
result 350% improvement in landslide detection accuracy.
Conformal Bayes under label shift: post-hoc calibration vs. in-training adaptation
problem Bayesian prediction sets under label shift
method Post-hoc calibration vs. In-training adaptation
result Both strategies achieve valid coverage equally in an unbiased training regime
New research shows fixed-budget best-arm identification cannot match static oracle performance.
problem Fixed-budget best-arm identification's performance limitations.
method Analysis of various adaptive and static algorithms for best-arm identification.
result For any algorithm, there exists at least one instance where the error decay rate is at most \((1 + \frac{\log(K)}{8})^{-1}\) times that of the static oracle.
Investigates model risk and semi-static hedging for martingale constrained models.
problem Model risk distributionally robust sensitivities for functionals on the Wasserstein space.
method Introduces distributionally robust problem with semi-static hedging strategies.
result Explicit characterizations of model risk optimal semi-static hedging strategies.
ARO overfits by making constraints dependent on uncertainty, leading to brittleness.
problem ARO's adaptive policies become brittle when realizations fall outside the uncertainty set.
method Assigning constraint-specific uncertainty set sizes with probabilistic guarantees.
result Regularization through specific uncertainty set sizes ensures stability and flexibility.
Proves a Minkowski-like inequality for static flat manifolds.
problem Bounding total mean curvature of convex surfaces.
method Adapting Wei's analysis to static asymptotically flat manifolds.
result Proves a Minkowski-like inequality for general static asymptotically flat manifolds.
Extends DeTEcT framework for token economies with dynamic and probabilistic parameters.
problem Modeling wealth distribution in token economies with dynamic and probabilistic parameters.
method Introduces four parametrization techniques: dynamic vs static, probabilistic vs non-probabilistic.
result Derives existing wealth distribution models from DeTEcT framework with added restrictions.
New algorithm achieves both static and dynamic regret optimally against an oblivious adversary for deterministic losses.
problem Achieving optimal static and dynamic regret simultaneously in adversarial bandits.
method Extends impossibility result to deterministic losses, uses negative static regret and Blackwell approachability.
result First algorithm achieving optimal static and dynamic regret simultaneously against an oblivious adversary.
Based on the daily data of American and Chinese stock markets, the dynamic behavior of a financial network with static and dynamic thresholds is investigated. Compared with the static threshold, the dynamic threshold suppresses the large fluctuation induced by the cross-correlation of individual stock prices, and leads…
Bayesian method achieves static guarantees with subgaussian prior.
problem Adaptive data analysis with statistical guarantees.
method Bayesian approach with Dirichlet prior and subgaussian theorem.
result Posterior mean algorithm matches static case guarantees.
Study improves dynamic PT fleet optimization under noisy demand predictions.
problem Accurately predicting dynamic public transport demand for effective fleet management.
method Experimental case study in Copenhagen, using linear programming to optimize fleets.
result Optimized fleet performance is mainly affected by noise distribution skew and large errors.
Adaptive portfolio outperforms static alternatives by 120% over 5 years.
problem Achieving strong and stable long-term performance in diversified portfolios.
method RL-BHRP: A two-level, learning-based approach that adjusts sector and stock exposures dynamically.
result Adaptive portfolio outperforms static alternatives by 120% over 5 years.
Dynamic pricing improves DeFi lending efficiency by reducing regret to logarithmic levels.
problem Static pricing mechanisms in DeFi lending protocols lead to suboptimal welfare and revenue.
method Online learning model for static and dynamic pricing models in DeFi lending.
result Adaptive supply models achieve logarithmic regret, outperforming static models.
QLBS and RLOP methods improve option pricing and hedging performance.
problem Improving option pricing and hedging performance under market frictions.
method Incorporates risk aversion and trading costs into QLBS, proposes RLOP approach.
result RLOP outperforms in dynamic hedging by reducing shortfall probability.
Dynamic risk measures follow law invariance principles over time.
problem Tackles dynamic risk measurement principles.
method Shows equivalence between adapted law invariance and recursive one-step conditional-law representation for time-consistent risk measures.
result Identifies adapted law invariance as the dynamic counterpart of ordinary law invariance.
Adaptive pooling operators improve sound event detection with weak labels.
problem Efficiently label audio recordings with weakly annotated sound sources.
method Developed adaptive pooling operators for multiple instance learning.
result Adaptive pooling operators outperform non-adaptive methods on static predictions and nearly match strong annotations.
Adaptive ensemble improves flu forecasts with minimal data.
problem Accurate flu forecasts to help public health.
method Adaptive stacking of ensembles, changing model weights weekly.
result Adaptive ensemble outperforms static ensembles in flu forecasts.
Universal algorithm minimizes adaptive regret for various convex functions.
problem Minimizing adaptive regret in changing environments for multiple convex functions.
method Borrowing MetaGrad's idea of multiple learning rates and using sleeping experts.
result First universal algorithm for minimizing adaptive regret of convex functions.
The α \alpha α -Alternator adapts to varying noise levels in sequences, improving robustness and performance.
problem Current models assume uniform noise levels, limiting performance on noisy temporal data.
method Introduces α \alpha α -Alternator using Vendi Score to dynamically adjust noise sensitivity. result Outperforms Alternators and state-of-the-art models in trajectory prediction, imputation, and forecasting.
Adaptive Misinformation defends against model stealing attacks by sending incorrect predictions for OOD queries.
problem Model stealing attacks clone target models using black-box query access and a surrogate dataset.
method Selective sending of incorrect predictions for Out-Of-Distribution (OOD) queries to degrade attacker's clone model accuracy.
result Our defense reduces attacker's clone model accuracy by up to 40% while maintaining benign user accuracy under 0.5%.
Adaptive Monte Carlo schemes developed over the last years usually seek to ensure ergodicity of the sampling process in line with MCMC tradition. This poses constraints on what is possible in terms of adaptation. In the general case ergodicity can only be guaranteed if adaptation is diminished at a certain rate. Import…
Physics-informed methods infer spatial dynamics from static snapshots, but limits exist.
problem Inferring spatial dynamics from static molecular patterns.
method Combining flexible representations with mechanistic constraints, analyzing structural identifiability, and adapting physics-informed schemes.
result Static spatial patterns can identify spatially varying dynamics, but limits exist due to modeling choices.
Existence of strong randomized equilibria in mean-field games with common noise.
problem Existence of strong solutions in mean-field games of optimal stopping.
method Connection with Bank-El Karoui's representation problem and continuity assumptions.
result Existence of strong randomized mean-field equilibrium under certain conditions.
RG-TTA adapts neural forecasters to streaming time series shifts by modulating adaptation intensity.
problem Adapting neural forecasters to distribution shifts in streaming time series data.
method RG-TTA uses a meta-controller that continuously modulates adaptation intensity based on distributional similarity.
result RG-TTA achieves the lowest MSE in 156 of 224 seed-averaged experiments, reducing MSE by 5.7% vs TTA.
DynBRO learns robustly from dynamic Byzantine workers.
problem Fault-tolerant distributed learning with dynamic Byzantine workers.
method Multi-level Monte Carlo (MLMC) gradient estimation and adaptive learning rate.
result DynaBRO nearly matches static setting's convergence rate with O ( T ) \mathcal{O}(\sqrt{T}) O ( T ) Byzantine worker changes. Study compares adaptive vs fixed query learning methods.
problem Comparing adaptive and fixed query learning methods for task approximation.
method Examined in-context and agentic learning in two settings: unrestricted and realizable.
result Adaptivity does not hinder performance in unrestricted setting but can in realizable setting.
This paper proposes a continuous timing strategy for growth vs. defensive style allocation.
problem Dynamic allocation of growth and defensive ETF baskets using macro-market timing signals.
method Continuous smooth score combining multiple factors, mapped to G/D weights, smoothed with EWMA.
result Continuous style timing strategy outperforms static benchmarks in risk-adjusted returns.
In this paper, we explore and detail our experiments in a high-dimensionality, multi-class image classification problem often found in the automatic recognition of Sign Languages. Here, our efforts are directed towards comparing the characteristics, advantages and drawbacks of creating and training Support Vector Machi…