arXiv research

GAIF enhances online multiple testing with feedback, improving statistical power.

New algorithms control FDX while achieving more power in online multiple testing.

Private online FDR control for adaptive testing under differential privacy.

A new metric, Weighted Regret, unifies FDR and power evaluation in online multiple testing.

e-LOND algorithm controls FDR in online testing with arbitrary dependencies.

Biological research often involves testing a growing number of null hypotheses as new data is accumulated over time. We study the problem of online control of the familywise error rate (FWER), that is testing an apriori unbounded sequence of hypotheses (p-values) one by one over time without knowing the future, such th…

We consider the problem of asynchronous online testing, aimed at providing control of the false discovery rate (FDR) during a continual stream of data collection and testing, where each test may be a sequential test that can start and stop at arbitrary times. This setting increasingly characterizes real-world applicati…

Multiple hypothesis testing, a situation when we wish to consider many hypotheses, is a core problem in statistical inference that arises in almost every scientific field. In this setting, controlling the false discovery rate (FDR), which is the expected proportion of type I error, is an important challenge for making …

Online surveillance detects systemic risk in financial markets.

We propose an alternative framework to existing setups for controlling false alarms when multiple A/B tests are run over time. This setup arises in many practical applications, e.g. when pharmaceutical companies test new treatment options against control pills for different diseases, or when internet companies test the…

One important partition of algorithms for controlling the false discovery rate (FDR) in multiple testing is into offline and online algorithms. The first generally achieve significantly higher power of discovery, while the latter allow making decisions sequentially as well as adaptively formulating hypotheses based on …

A method for efficient statistical inference from online algorithms.

aLTT selects hyperparameters efficiently with statistical guarantees.

Framework for online hypothesis testing across various data types.

Multiple hypothesis testing is a core problem in statistical inference and arises in almost every scientific field. Given a set of null hypotheses $\mathcal{H}(n) = (H_1,\dotsc, H_n)$, Benjamini and Hochberg introduced the false discovery rate (FDR), which is the expected proportion of false positives among rejected nu…

A new family of conformal test martingales based on Legendre polynomials for online exchangeability testing.

A/B testing improves marketing decisions by selecting effective stratification variables.

DOMKL learns functions from IoT data with minimal regret and consensus constraints.

Decentralized Gaussian processes for multi-agent systems.

In the analysis of sequential data, the detection of abrupt changes is important in predicting future changes. In this paper, we propose statistical hypothesis tests for detecting covariance structure changes in locally smooth time series modeled by Gaussian Processes (GPs). We provide theoretically justified threshold…

Online change-point detection (OCPD) is important for application in various areas such as finance, biology, and the Internet of Things (IoT). However, OCPD faces major challenges due to high-dimensionality, and it is still rarely studied in literature. In this paper, we propose a novel, online, graph-based, change-poi…

New algorithm detects changes in high-dimensional data with mean and variance.

Proposes PEMI for online selective conformal prediction with asymmetric rules.

A probabilistic framework for online test-time adaptation

Paper adapts Bayesian Hui-Walter method for unlabeled data.

New method controls false discoveries in online testing with deadlines.

We propose an adaptive sampling approach for multiple testing which aims to maximize statistical power while ensuring anytime false discovery control. We consider $n$ distributions whose means are partitioned by whether they are below or equal to a baseline (nulls), versus above the baseline (actual positives). In addi…

We propose a nonparametric sequential test that aims to address two practical problems pertinent to online randomized experiments: (i) how to do a hypothesis test for complex metrics; (ii) how to prevent type $1$ error inflation under continuous monitoring. The proposed test does not require knowledge of the underlying…

Study uses multiple online media to predict crude oil prices.

New method controls false discoveries in real-time data streams.

We introduce a local volatility model for the valuation of options on commodity futures by using European vanilla option prices. The corresponding calibration problem is addressed within an online framework, allowing the use of multiple price surfaces. Since uncertainty in the observation of the underlying future price…

New methods improve LLM preference optimization by intelligently weighting multiple reference models.

New active learning method for kernel selection improves efficiency and accuracy.

Paper proposes methods to handle missing data in online RL, improving efficiency and uncertainty capture.

New findings control FDR for online testing methods under positive dependence.

New algorithm for online optimization over symmetric cones, unifying previous methods.

In the online multiple testing problem, p-values corresponding to different null hypotheses are observed one by one, and the decision of whether or not to reject the current hypothesis must be made immediately, after which the next p-value is observed. Alpha-investing algorithms to control the false discovery rate (FDR…

TAME learns new tasks without knowing them, outperforming existing methods.

Comparison Lift uses bandit algorithms to optimize online ad testing.

Unified framework for online LLM watermark detection using e-processes.

It is well known that the historical logs are used for evaluating and learning policies in interactive systems, e.g. recommendation, search, and online advertising. Since direct online policy learning usually harms user experiences, it is more crucial to apply off-policy learning in real-world applications instead. Tho…

Develops a method to detect changes in linear systems with temporal correlations.

A new approach for test-time adaptation detects and reacts to distribution shifts.

Model-X test detects conditional independence in streaming data.

Framework for online resource allocation using social welfare functions.

Online change detection algorithm using random Fourier features.

This paper introduces a reinforcement learning framework for A/B testing with dynamic causal effects.

New algorithms detect and react to multiple change points in online learning.