Designs an online selective sampling approach for choosing which model to use.
problem Active model selection for pre-trained classifiers in unlabeled data streams.
method Online selective sampling approach to query and label examples.
result High probability of outputting the best model with minimal label queries.
Online selection of dynamic features has attracted intensive interest in recent years. However, existing online feature selection methods evaluate features individually and ignore the underlying structure of feature stream. For instance, in image analysis, features are generated in groups which represent color, texture…
OOMP selects features online for sparse linear regression.
problem Feature selection in high-dimensional sparse linear models.
method Online algorithm that alternates between feature selection and coefficient estimation.
result Theoretical guarantees and computational complexity analysis of OOMP.
FIRES framework selects stable features from online data.
problem Efficiently selecting features in online settings with limited data.
method FIRES framework uses model parameter importance for feature selection.
result FIRES selects stable feature sets with minimal model complexity.
Automated method selects best model from many for production systems.
problem Selecting the best model for production systems from a large pool.
method Automated online experimentation mechanism using Bayesian surrogate models.
result Efficiently identifies the best model with small online experiments.
Online method selects candidates from data streams, ensuring irreversible decisions.
problem Conformal selection's incompatibility with irreversible decisions in online scenarios.
method Online Conformal Selection with Accept-to-Reject Changes (OCS-ARC) incorporating online Benjamini-Hochberg procedure.
result OCS-ARC controls FDR at or below nominal level, improving selection power.
The paper addresses errors in online selective conformal prediction and proposes new strategies to ensure valid inference.
problem Online selective conformal prediction's exchangeability issues and false coverage rate control problems.
method Evaluation and correction of existing calibration selection strategies, proposing new ones that preserve exchangeability.
result Novel calibration selection strategies ensure both selection-conditional coverage and FCR control.
Proposes a new algorithm for efficient online model selection of LLMs considering the increasing-then-converging trend.
problem Balancing cost and performance in choosing the best LLM among a diverse set of models.
method Introduces a time-increasing bandit algorithm (TI-UCB) that predicts model performance increases and balances exploration and exploitation.
result Achieves a logarithmic regret upper bound, indicating a fast convergence rate in model selection.
ALEXP improves model selection in linear bandits with exponential regret improvement.
problem Model selection in linear bandits is challenging due to balancing exploration and exploitation.
method ALEXP uses online learning with favorable bias-variance trade-off to emulate full-information feedback.
result ALEXP achieves an exponentially improved ( log M \log M log M ) regret dependence on the number of models M M M . This work tackles online memory selection in continual learning using information theory.
problem Online selection of a representative replay memory from data streams.
method Information-theoretic criteria (surprise, learnability) and Bayesian model for efficient computation.
result InfoRS improves robustness against data imbalance compared to reservoir sampling.
Improved online penalty selection for time series models.
problem Efficiently selecting penalty parameters for lasso in time series models.
method Enhanced autoregressive model with online penalty selection.
result Significantly improved computational performance and forecast accuracy.
New online feature selection method handles streaming data with concept drift.
problem Handling streaming data with concept drift and sparsity.
method Online feature screening method with model adaptation.
result Online screening methods with model adaptation outperform without model adaptation on data streams with concept drift.
As an emerging research direction, online streaming feature selection deals with sequentially added dimensions in a feature space while the number of data instances is fixed. Online streaming feature selection provides a new, complementary algorithmic methodology to enrich online feature selection, especially targets t…
Revisits online Laplace methods for neural networks, showing they are sound under certain conditions.
problem Online Laplace methods violate the Laplace approximation's critical assumption.
method Re-derives online Laplace methods, showing they target a variational bound on a mode-corrected variant of the Laplace evidence.
result Online Laplace and its mode-corrected counterpart share stationary points that satisfy the Laplace method's assumption.
Proposes PEMI for online selective conformal prediction with asymmetric rules.
problem Challenges of handling asymmetric selection mechanisms in online selective conformal prediction.
method PEMI: permutation-based framework for selective conformal prediction with arbitrary asymmetric selection rules.
result Achieves exact selection-conditional coverage for any asymmetric selection mechanism and any prediction model.
CAP algorithm controls FCR in online selective prediction.
problem Online predictive tasks with temporal multiplicity and FCR control.
method CAP framework with adaptive pick rule and calibration set construction.
result CAP achieves exact selection-conditional coverage guarantee and FCR control.
We introduce an efficient algorithmic framework for model selection in online learning, also known as parameter-free online learning. Departing from previous work, which has focused on highly structured function classes such as nested balls in Hilbert space, we propose a generic meta-algorithm framework that achieves o…
Both the human brain and artificial learning agents operating in real-world or comparably complex environments are faced with the challenge of online model selection. In principle this challenge can be overcome: hierarchical Bayesian inference provides a principled method for model selection and it converges on the sam…
Paper addresses selection bias in online advertising auctions.
problem Selection bias affects auction truthfulness and advertiser profits.
method Theoretical analysis combined with multi-task learning.
result Selection bias can be significantly reduced using multi-task learning.
Online feature selection has been an active research area in recent years. We propose a novel diverse online feature selection method based on Determinantal Point Processes (DPP). Our model aims to provide diverse features which can be composed in either a supervised or unsupervised framework. The framework aims to pro…
New algorithm reduces adaptation lag in online model selection.
problem Adaptation lag in online model selection for non-stationary environments.
method Optimistic online mirror descent with safeguarded large learning rates.
result Reduces adaptation lag from hundreds of rounds to a few rounds.
We consider the problem of online collaborative filtering in the online setting, where items are recommended to the users over time. At each time step, the user (selected by the environment) consumes an item (selected by the agent) and provides a rating of the selected item. In this paper, we propose a novel algorithm …
Feature Selection (FS) plays an important role in learning and classification tasks. The object of FS is to select the relevant and non-redundant features. Considering the huge amount number of features in real-world applications, FS methods using batch learning technique can't resolve big data problem especially when …
New bounds for online portfolio selection without smoothness assumptions.
problem Online portfolio selection with non-Lipschitz, non-smooth losses.
method Data-dependent bounds using novel smoothness characterizations and FTRL with self-concordant regularizers.
result Achieves logarithmic regrets when data is 'easy' and sublinear worst-case regrets.
MPS selects models for nonstationary time series in real-time.
problem Model selection under nonstationary time series.
method Combines conformal inference with model confidence sets.
result Reliably identifies optimal models under nonstationarity.
Proposes a max-utility arm selection strategy for reducing cumulative regret in sequential query recommendations.
problem Reduces cumulative regret in sequential query recommendations for closed loop interactive learning settings.
method Proposes a max-utility arm selection strategy based on the maximum utility of arms.
result Improves cumulative regret substantially compared to baseline algorithms and random selection.
Proposes OBS, a method to adaptively combine Bayesian models online.
problem Learning optimal combinations of Bayesian models in online learning.
method Empirical Bayes lens, Online Bayesian Stacking (OBS).
result Establishes a novel connection between OBS and portfolio selection.
Proposes an online model for LLM cascading with adaptive API selection.
problem Adaptive querying and selection of LLM APIs in a context-dependent environment.
method Develops a learning approach combining GMM estimation and UCB-style bounds.
result Achieves cumulative regret of O ~ ( T ) \widetilde O(\sqrt T) O ( T ) over T T T periods. Current online learning methods suffer issues such as lower convergence rates and limited capability to select important features compared to their offline counterparts. In this paper, a novel framework for online learning based on running averages is proposed. Many popular offline regularized methods such as Lasso, El…
New algorithm solves online resource allocation problems efficiently.
problem Dynamic resource allocation in operations research.
method Minimal Selection Principle and MSoE algorithm.
result Ensures optimal cumulative regret bounds in dynamic resource allocation.
GAIF enhances online multiple testing with feedback, improving statistical power.
problem Sequential online multiple testing with delayed feedback.
method GAIF framework using dynamic threshold adjustment and feedback-driven model selection.
result Improves statistical power through feedback-driven model selection.
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.
Study selective classification with limited feedback in online learning.
problem Resource-limited and safety-critical domains where feedback is only received on abstentions.
method Versioning-based schemes for minimizing mistakes and abstentions.
result Constructed schemes that make few mistakes and minimal excess abstentions.
Efficiently selects seed nodes to maximize content influence in unknown social networks.
problem Maximizing content spread in social networks with unknown network model.
method Formulated as an infinite-horizon discounted MDP, uses model-based reinforcement learning to select seed users adaptively.
result Established a regret bound of O ~ ( T ) \widetilde O(\sqrt{T}) O ( T ) for the algorithm. Paper tackles online facility location with user requests and provides a quasi-linear time algorithm.
problem Online facility location problem with user requests and site selection costs.
method Design of a novel quasi-linear time algorithm based on the Hedge algorithm.
result Good theoretical guarantees on the performance of the novel algorithm.
Paper tackles policy selection with logged data and limited online interactions.
problem Safe evaluation and deployment of offline reinforcement learning policies.
method Active offline policy selection combining logged data with online interaction.
result Improves upon state-of-the-art OPE estimates and pure online policy evaluation.
New method for efficient personalized learning in mobile health.
problem Efficient and personalized learning in mobile health.
method Proposes a novel generative process on kernel composition for online Gaussian Process regression.
result Trajectories of kernel evolutions can be transferred between users to improve learning and kernels are meaningful for mHealth prediction.
Transfer learning significantly accelerates the reinforcement learning process by exploiting relevant knowledge from previous experiences. The problem of optimally selecting source policies during the learning process is of great importance yet challenging. There has been little theoretical analysis of this problem. In…
Adaptive robust strategy improves online portfolio selection by managing market trends and costs.
problem Optimizing sequential investment decisions in volatile markets.
method Robust optimization with adaptive parameter adjustment.
result Adaptive scheme outperforms existing strategies in cumulative returns and Sharpe ratios.
OnlineSCI extends ACI for adaptive selective inference with improved coverage and IER control.
problem Adaptive selective inference in online settings with improved coverage and IER control.
method Adaptive selective inference with extended ACI algorithm.
result OnlineSCI controls average missed coverage and instantaneous error rate at selected times, up to a non-asymptotic remainder term.
New algorithm for online portfolio selection with reduced runtime.
problem Maximizing total return in online portfolio selection.
method Minimizes current logarithmic loss regularized by log-determinant of Hessian.
result Achieves regret guarantee similar to Universal Portfolios with reduced runtime.
Unified analysis of online optimization with self-concordant barriers, improving regret bounds.
problem Online convex optimization with specific loss functions.
method Online mirror descent with self-concordant barriers and logarithmic loss.
result Improved regret bounds for online portfolio selection and quantum state learning.
COMA combines prediction sets from multiple models for online, adaptive prediction.
problem Combining multiple prediction models with uncertainty guarantees.
method Online model aggregation using weighted voting of conformal prediction sets.
result COMA retains coverage guarantees under negative correlation assumptions.
A/B testing improves marketing decisions by selecting effective stratification variables.
problem Improving the sensitivity of A/B testing through stratified sampling.
method Designing an algorithm to select a subset of stratification variables for variance reduction.
result The subset selection method outperforms other variance reduction techniques in A/B testing.
Optimizes recommender selection online with D-optimal design.
problem Finding the optimal recommender in online exploration-exploitation.
method Leverages D-optimal design from statistics to maximize information gain.
result Achieves maximum information gain during online exploration.
Paper proposes an online sparse linear regression method for streaming data.
problem Sparse regression for variable selection and prediction accuracy.
method Online sparse linear regression framework with memory efficiency and relaxed assumptions.
result The ℓ 2 \ell_2 ℓ 2 -norm statistical error of the estimator diminishes to zero with optimal order. BOMS enhances offline MBRL by improving model selection with Bayesian optimization.
problem Inaccurate model selection in offline MBRL due to distribution shift.
method Proposes BOMS, an active model selection framework using Bayesian optimization.
result Improves model selection with only a small amount of online interaction.
Paper corrects bias in online learning algorithms with endogenous data.
problem Dynamic selection problems in online learning algorithms with endogenous data.
method Instrumental-variable-based algorithm to correct bias, proving central limit theorem.
result Obtains true parameter values and low regret levels.