New framework converts offline to online estimation using black-box offline estimators.
problem Convert offline estimation algorithms to online estimation algorithms.
method Oracle-Efficient Online Estimation (OEOE) framework.
result Achieves near-optimal online estimation error via black-box offline estimators.
Study online monotone density estimation with expert aggregation and log-optimal calibration.
problem Online monotone density estimation and log-optimal calibration.
method Proposed two online estimators: Grenander estimator and expert aggregation estimator.
result Online estimators achieve O ( n 1 / 3 ) O(n^{1/3}) O ( n 1/3 ) cumulative log-likelihood gap and n log n \sqrt{n\log{n}} n log n pathwise regret bound. New method improves online nonparametric estimators with minimal extra computation.
problem Model selection and hyperparameter tuning for online nonparametric estimators.
method Weighted rolling validation procedure for online cross-validation.
result Improves base estimators to achieve better heuristic performance and adaptive convergence rate.
Paper proposes an online covariance estimator for sketched Newton methods.
problem Estimating the limiting covariance matrix of sketched Newton methods.
method Proposes a fully online covariance matrix estimator from Newton iterates.
result Establishes the consistency and convergence rate of the proposed estimator.
An online decision-making algorithm using stochastic gradient descent for big data.
problem Efficiently updating decision rules in online decision making with big data.
method Stochastic gradient descent for online updates, asymptotic normality of estimators.
result Asymptotic normality of parameter and value estimators, enabling statistical inference.
Robust method estimates state, input, and parameters of linear systems online.
problem Joint estimation of state, input, and parameters in noisy or outlier-prone measurements.
method Combines recursive, alternating, and iteratively-reweighted least squares into a single algorithm.
result Good performance in presence of outliers and compared to state-of-the-art methods.
The paper provides bounds on estimation error in a distributed online learning setting.
problem Estimating an unknown parameter in a distributed and online manner with finite sample guarantees.
method Proposes a distributed online estimation algorithm that improves accuracy through communication, providing non-asymptotic bounds on estimation error.
result Demonstrates a trade-off between estimation error and communication costs, and determines a stopping time for communication based on desired accuracy.
The stochastic gradient descent (SGD) algorithm is widely used for parameter estimation, especially for huge data sets and online learning. While this recursive algorithm is popular for computation and memory efficiency, quantifying variability and randomness of the solutions has been rarely studied. This paper aims at…
A method for efficient statistical inference from online algorithms.
problem Computational constraints in online algorithms make traditional variance estimation difficult.
method HulC method that wraps around online algorithms to produce valid confidence regions.
result The HulC method produces asymptotically valid confidence regions for online algorithms.
OEUVRE estimates online loss with constant time and memory, outperforming other methods.
problem Accurately estimating expected loss in online learning.
method Recursive evaluation of each sample on current and previous models, using algorithmic stability for updates.
result Consistency, convergence rates, and concentration bounds proved for OEUVRE.
Develops a method to estimate optimal policy value in online learning.
problem Challenges in evaluating ongoing policies in online learning environments.
method Doubly Robust Interval Estimation (DREAM) method.
result Valid inference on online conditional mean estimator with asymptotically normal distribution.
Paper proposes real-time VaR estimation using quantile regression forest with conformal calibration.
problem Real-time estimation of Value at Risk (VaR) in rapidly changing markets.
method Quantile regression forest trained offline, real-time VaR estimates via observed risk factors, conformalized estimator for reliability.
result The proposed method provides reliable real-time VaR estimates.
Efficiently estimates online variational learning using importance sampling.
problem Online variational estimation in state-space models.
method Variational approach with Monte Carlo importance sampling.
result Proposed efficient algorithm for streaming data.
New method improves online covariance estimation for SGD.
problem Improving online covariance estimation for SGD.
method Proposes a de-biased covariance estimator that eliminates second-order derivatives.
result Achieves a convergence rate of n ( α − 1 ) / 2 log n n^{(α-1)/2} \sqrt{\log n} n ( α − 1 ) /2 log n , outperforming existing methods. New collaborative algorithm improves personalized mean estimation in online settings.
problem Online estimation of means from multiple, possibly overlapping distributions.
method Novel collaborative strategy for active querying and mean estimation.
result Algorithm improves mean estimates through communication among agents.
Paper proposes online learning for MIMO channel estimation using neural networks.
problem Challenges in MIMO channel estimation due to physical model simplifications and system configuration requirements.
method Unfold a channel estimation algorithm as a neural network, allowing online learning and correction of imperfect models.
result Achieves channel estimation error close to that of a perfectly calibrated system.
New algorithm predicts piecewise regular functions online.
problem Online prediction of piecewise regular functions.
method Modified sleeping experts aggregation algorithm.
result Oracle risk bounds for all local regions.
This paper introduces online algorithms to estimate robust geometric median in large data streams.
problem Detecting outliers in large data sets using robust statistical measures.
method Online stochastic Newton methods for estimating the geometric median.
result Rates of convergence for online estimation of the geometric median.
New algorithm reduces TV-denoising to adaptive online learning.
problem Estimating TV-bounded functions from noisy samples.
method Deep connection to Strongly Adaptive online learning; O ( n log n ) O(n \log n) O ( n log n ) time algorithm. result Near minimax optimal rate of O ( n 1 / 3 C n 2 / 3 ) O(n^{1/3}C_n^{2/3}) O ( n 1/3 C n 2/3 ) under squared error loss. Paper develops an online learning algorithm for functional data models.
problem Recovering slope functions or predictors in functional data models.
method Online regularized learning algorithm in reproducing kernel Hilbert spaces with polynomially decaying step-size.
result Established fast convergence rates for estimation error without capacity assumption.
Paper introduces online tensor inference for real-time data analysis.
problem Real-time processing of high-dimensional tensor data.
method Stochastic Gradient Descent (SGD) for efficient online inference.
result Establishes non-asymptotic convergence and optimal estimation error rate.
Principal component analysis (PCA) has been a prominent tool for high-dimensional data analysis. Online algorithms that estimate the principal component by processing streaming data are of tremendous practical and theoretical interests. Despite its rich applications, theoretical convergence analysis remains largely ope…
Online algorithm for probabilistic forecasting of conditional moments.
problem Probabilistic forecasting needs accurate learning of expected value and conditional heteroskedasticity.
method Combines online LASSO estimation with GAMLSS framework.
result Competitive performance in day-ahead electricity price forecasting.
Paper tackles online learning for DR management with incentives.
problem Estimating baseline consumption in DR programs with consumer incentives.
method Online learning scheme using least-squares with perturbed reward prices.
result Achieves low regret of $\mathcal{O}\left((\log{T})^2
ight)$ compared to optimal.
Bayesian framework estimates label shift for improved classifier performance.
problem Label shift in supervised learning leading to degraded classifier performance.
method Bayesian framework with dynamic Dirichlet priors and online EM algorithms.
result Significant improvements in classifier accuracy over state-of-the-art methods.
Bayesian method improves online NARMAX model identification.
problem Online identification of nonlinear systems with small sample sizes and low noise.
method Variational Bayesian inference using message passing algorithm for polynomial NARMAX models.
result Variational Bayesian estimator outperforms recursive and offline least-squares methods.
New online method estimates OT distances from sample streams.
problem Computing OT distances between arbitrary distributions.
method Online Sinkhorn algorithm using iterative enrichment of non-parametric representation.
result Consistent estimation of true regularized OT distance with nearly-O(1/n) sample complexity.
Paper introduces an online method for estimating the difference between two probability distributions.
problem Estimating the difference between two probability density functions using available data.
method Non-parametric online likelihood-ratio estimation using Pearson-divergence functional minimization.
result The proposed method provides efficient online updates and theoretical guarantees for performance.
Sequential quantile estimation refers to incorporating observations into quantile estimates in an incremental fashion thus furnishing an online estimate of one or more quantiles at any given point in time. Sequential quantile estimation is also known as online quantile estimation. This area is relevant to the analysis …
The paper develops a new algorithm for constructing minimax estimators using online learning techniques.
problem Designing minimax estimators for probability distribution parameters.
method Viewing the problem as a zero-sum game and using online learning with non-convex losses to find a Nash equilibrium.
result The algorithm constructs both a minimax estimator and a least favorable prior.
The paper addresses statistical inference for online decision-making in a contextual bandit setting.
problem Understanding the performance of reward models in online decision-making with contextual information.
method The paper uses the contextual bandit framework with a linear reward model and the ε \varepsilon ε -greedy policy to address the exploration-exploitation dilemma. It employs the martingale central limit theorem and inverse propensity score weighting to establish asymptotic normality of parameter estimators. result The online ordinary least squares estimator and the online weighted least squares estimator are asymptotically normal, providing insights into the performance of the reward model.
In this paper we formulate the nonnegative matrix factorisation (NMF) problem as a maximum likelihood estimation problem for hidden Markov models and propose online expectation-maximisation (EM) algorithms to estimate the NMF and the other unknown static parameters. We also propose a sequential Monte Carlo approximatio…
New method uses online learning to improve AIPW estimators for adaptively collected data.
problem Estimating treatment effects with adaptively collected data.
method Online learning to minimize sequentially weighted estimation error.
result Local minimax lower bound shows optimality of AIPW estimator.
In this paper, we design a nonparametric online algorithm for estimating the triggering functions of multivariate Hawkes processes. Unlike parametric estimation, where evolutionary dynamics can be exploited for fast computation of the gradient, and unlike typical function learning, where representer theorem is readily …
Online DEM improves tracking of latent states in dynamic systems.
problem Tracking latent states in dynamic systems with online updates.
method Specializes DEM for online data assimilation, separating temporal scales.
result ODEM can track latent states of a non-linear generative model.
GCF estimates heterogeneous treatment effects for continuous treatments in online marketplaces.
problem Estimating heterogeneous treatment effects for continuous treatments in online marketplaces.
method Kernel-based doubly robust estimator and distance-based splitting criterion.
result GCF estimates heterogeneous treatment effects for continuous treatments effectively.
Study estimates 163 million online freelancers globally.
problem Estimating the number of online workers globally.
method Combining data from various online labour platforms.
result Headline estimate of 163 million registered profiles.
GLCB uses Gated Linear Networks for online contextual bandits.
problem Online learning in contextual bandits with uncertainty estimation.
method Gated Linear Networks (GLNs) for prediction and uncertainty estimation.
result GLCB outperforms state-of-the-art methods in online contextual bandits.
A new gradient estimator for online optimization with two function evaluations.
problem Online optimization of convex and Lipschitz functions with noisy data.
method L1-randomization approach for gradient estimation.
result Compared or better guarantees than previous methods for canceling noise.
New method for online statistical inference in contextual bandits using SGD.
problem Online decision-making in contextual bandits with statistical inference.
method Weighted stochastic gradient descent for adaptive data collection.
result Asymptotic normality of the parameter estimator with improved efficiency.
Locally private online quantile regression method addresses privacy constraints.
problem Estimating and inferring quantile regression under local differential privacy constraints.
method Developed a finite-alphabet channel where users compute local contributions, apply randomized response, and send reports. A public decoder corrects distortion and reconstructs inputs for averaging.
result Established local privacy, decoder unbiasedness, consistency, asymptotic normality, and inference for scalar contrasts.
Optimal online data collection for semiparametric inference reduces regret.
problem Sequential data collection decisions for efficient estimation under budget constraints.
method Online Moment Selection framework; Explore-then-Commit and Explore-then-Greedy policies.
result Online data collection policies achieve zero regret relative to an oracle policy.
Adaptive truncation improves privacy in online Bayesian estimation.
problem Ensuring privacy in online Bayesian estimation of a static parameter.
method Sequential Monte Carlo, adaptive truncation, Thompson sampling.
result Adaptive truncation reduces privacy-preserving noise, enabling more accurate estimation.
Anchor-TS uses median anchoring to improve online decision-making from offline data with distribution shift.
problem Improving online decision-making from offline data with distribution shift.
method Sample-Mean Anchored Thompson Sampling (Anchor-TS) with median anchoring.
result Anchor-TS safely leverages offline data to accelerate online learning and reduces regret.
Proposes online debiasing estimators for adaptive linear regression.
problem Adaptive data collection leads to non-normal asymptotic behavior in simple methods.
method Online debiasing estimators that correct distributional anomalies.
result Asymptotic normality and minimax lower bound for proposed estimators.
Two methods estimate effect size for online experiments, improving accuracy and efficiency.
problem Determining the correct effect size for online experiment duration.
method Two approaches: hierarchical models and utility theory.
result Proposed methods outperform baseline approaches in accuracy and efficiency.
An algorithm estimates the mean of a process from noisy, online sensor data.
problem Estimating the mean of a process from noisy, online sensor data with some sensors behaving maliciously.
method An efficient online algorithm that produces estimates as data comes in, achieving nearly competitive error bounds.
result The algorithm can compute a good approximation to the true mean with error bounds of O ( δ log ( T ) ) O(δ\log(T)) O ( δ log ( T )) . New method for online inference using SGD with random scaling.
problem Efficient online inference for SGD parameters.
method Asymptotic pivotal statistics via random scaling.
result Robust and efficient online inference without resampling.