New hybrid rules improve lasso optimization efficiency.
problem Efficiently solving lasso problems with ultrahigh-dimensional data.
method Hybrid safe-strong rules (HSSR) incorporating safe screening into sequential strong rules.
result HSSR outperforms existing rules in synthetic and real data sets.
A new stopping rule based on E-values helps efficiently use sampling in Bayesian Deep Ensembles.
problem How long should sampling continue in Bayesian Deep Ensembles to yield significant improvements?
method Formulated as a sequential anytime-valid hypothesis test, using E-values to decide when to stop sampling.
result Only a fraction of the full-chain budget is often required for significant improvements.
We propose a probabilistic formulation that enables sequential detection of multiple change points in a network setting. We present a class of sequential detection rules for certain functionals of change points (minimum among a subset), and prove their asymptotic optimality properties in terms of expected detection del…
New rules reduce SLOPE model fitting time by screening out irrelevant variables.
problem Expensive tuning of regularization parameter in penalized regression models.
method Strong screening rules for group-based SLOPE models.
result Significant acceleration of fitting process for Group SLOPE and sparse-group SLOPE.
Study aggregation of statistical evidence under unknown dependence using group-invariance.
problem Aggregating statistical evidence under unknown and complex dependence structures.
method Develops a framework using group-invariance and permutation-based constructions to aggregate evidence across transformed datasets.
result Shows uniform improvement in critical values for single-batch aggregation over deterministic calibrations, adapting to unknown dependence structures.
GuideR learns rules guided by user preferences for classification, regression, and survival analysis.
problem Lack of user preferences in rule learning algorithms.
method Guided sequential covering approach.
result User preferences improve rule quality in classification, regression, and survival analysis.
Study efficient sequential evaluation of large language models using historical data.
problem Sequentially evaluate a new large language model (LLM) on a fixed question set.
method Construct a confidence sequence (CS) and design active querying rules to shrink CS width.
result Simple uniform sampling can sometimes outperform adaptive querying rules.
We consider rules for discarding predictors in lasso regression and related problems, for computational efficiency. El Ghaoui et al (2010) propose "SAFE" rules that guarantee that a coefficient will be zero in the solution, based on the inner products of each predictor with the outcome. In this paper we propose strong …
Regularized greedy policies outperform classical greedy in finite-horizon bandit problems.
problem Optimizing decision-making in sequential experiments with finite time constraints.
method Developed regularized greedy algorithms for multi-armed Bernoulli bandits.
result Calibrated regularized greedy policies consistently match or outperform state-of-the-art algorithms.
Adaptive Bayesian learning aggregates experts to improve performance.
problem Bayesian online learning's performance depends on inferential choices.
method Treat Bayesian update rules as experts and aggregate them based on sequential predictive losses.
result The aggregate competes with the best expert in hindsight at a low aggregation cost.
RuleKit aids in creating interpretable models for various data types.
problem Creating interpretable models for different data types.
method Sequential covering induction algorithm for classification, regression, and survival problems.
result Facilitates verification of hypotheses about data dependencies.
Optimized parallel algorithms for identifying strong ties in data.
problem Identifying strong ties in data with varying distances and community sizes.
method Design and analysis of sequential and parallel algorithms for partitioned local depths.
result Optimized algorithms achieve up to 19.4x speedup in parallel execution.
New methods optimize experiment selection for sequential data, improving model accuracy.
problem Optimizing experiment selection for sequential data in multidimensional cases.
method Adopting greedy experiment selection methods for maximum likelihood estimation.
result Proposed methods produce consistent and asymptotically normal estimators.
New method controls false discoveries in real-time data streams.
problem Online testing of hypotheses with strict error constraints and no future data.
method Structure-adaptive sequential testing (SAST) with alpha-investment algorithm.
result Substantial power gain over existing online testing rules.
We consider the setting of sequential prediction of arbitrary sequences based on specialized experts. We first provide a review of the relevant literature and present two theoretical contributions: a general analysis of the specialist aggregation rule of Freund et al. (1997) and an adaptation of fixed-share rules of He…
Generative networks minimize predictive scoring rules for probabilistic forecasting.
problem Evaluating and improving probabilistic forecasts using generative models.
method Training generative networks to minimize predictive-sequential scoring rules on temporal sequences.
result Our method outperforms adversarial approaches in probabilistic calibration.
Paper explores folding patterns of curved creases preserving their geometric properties.
problem Investigating rigid-ruling folding motions of curved crease-rule patterns.
method Deriving conditions for rigid-ruling foldability and analyzing combinations of creases.
result Constant fold-angle creases are only compatible with other constant fold-angle creases.
A new screening rule 'dynamic Sasvi' improves sparse optimization speed.
problem Sparse optimization problem identification.
method Flexible framework based on Fenchel-Rockafellar duality for norm-regularized least squares.
result Dynamic Sasvi can eliminate more features and increase solver speed.
PF-RNNs use particle filtering to model uncertainty in RNNs for better sequential data prediction.
problem Highly variable and noisy sequential data.
method PF-RNNs maintain a latent state distribution as a set of particles, updating with Bayes rule.
result PF-RNNs outperform standard RNNs on various sequence prediction tasks.
This paper studies statistical estimation in optional regression models.
problem Estimating parameters in regression models with optional semimartingale processes.
method Structural least squares (LS) estimates and their sequential versions.
result Strong consistency of LS-estimates and fixed accuracy of sequential LS-estimates.
A new screening rule improves SLOPE efficiency for high-dimensional data.
problem Efficiently selecting relevant predictors in high-dimensional data.
method Developed a screening rule for SLOPE based on its subdifferential.
result The screening rule improves SLOPE's performance significantly in high-dimensional settings.
Bayesian RL tackles uncertainty with deep generative models and sequential samplers.
problem Optimal decision-making in uncertain environments with limited data.
method Bayesian approach using deep generative models and prequential scoring rule for posterior inference. Policy learning via expected Thompson sampling.
result Improves policy learning in high-dimensional parameter spaces and continuous action spaces.
The paper develops methods to estimate optimal treatment sequences under policy constraints.
problem Estimating the best sequence of treatments over multiple stages for individuals.
method Empirical welfare maximization approach, solving treatment assignment sequentially or simultaneously.
result Established convergence rates and upper bounds for estimation methods.
R-SQAIR adds relational bias to sequential object attention models for better object interactions.
problem Traditional sequential multi-object attention models struggle with relational inferences.
method Proposes R-SQAIR, a relational extension of SQAIR with a parallel pairwise interaction module.
result Demonstrates gains in object relations and combinatorial generalization over sequential mechanisms.
E-valuator converts verifier scores into reliable decision rules.
problem Ensuring the correctness of agent trajectories based on heuristic scores.
method Sequential hypothesis testing framework for online monitoring of agent trajectories.
result E-valuator provides better false alarm rate control and statistical power than other strategies.
Signature kernel handles sequential data with theoretical and practical advantages.
problem Handling sequential data efficiently and accurately.
method Positive definite kernel based on stochastic analysis with efficient computation.
result Strong empirical performance and theoretical guarantees.
New private algorithm for sequential hypothesis testing with privacy and error rate guarantees.
problem Privacy protection in sequential hypothesis testing for sensitive data.
method Renyi differential privacy, Wald's Sequential Probability Ratio Test (SPRT).
result Private algorithm with strong privacy guarantees and theoretical performance analysis.
Develops a method to learn optimal timing of treatments from observational data.
problem Choosing the right time to start treatments in dynamic decision-making problems.
method Advantage Doubly Robust Estimator for dynamic treatment rules under sequential ignorability.
result Proves welfare regret bounds and shows promising empirical performance.
The paper extends a learning heuristic to high-dimensional contexts, reducing the risk of unusual actions.
problem Sequential learning problems in high dimensions, especially in dynamic pricing and auctions.
method Introducing a conservative εt-greedy rule that limits the adoption of new actions to a focused set of promising actions. result Reasonable bounds for cumulative regret and improved regret bound for conservative version compared to non-conservative.
Efficiently samples latent functions in complex data models with sequential structure.
problem Inference of latent functions in probabilistic models with complex data likelihoods.
method Extends Markov chain Monte Carlo techniques to handle sequential structure, enabling efficient sampling of latent variables and parameters.
result Strong performance in growing-data settings, demonstrating scalability.
New approach optimizes decisions based on uncertainty in predictions.
problem Mismatch between prediction accuracy and decision loss in sequential design.
method Directional uncertainty-guided approach to sequential experimental design.
result Directional uncertainty-based design stops earlier and performs better.
Symmetric CNNs improve sequential recommendation and protein structure prediction.
problem Improving prediction accuracy in sequential recommendation and protein structure inference.
method Developed a CNN architecture that preserves symmetry in convolutional layers, using parameterized convolutional kernels.
result Symmetric structured CNNs achieve better performance with fewer parameters.
Develops a framework for distributional Granger causality
problem Identifying predictive dependence in time series beyond Gaussian settings
method Using finite collection of channel-specific restrictions
result Identifies distributional Granger non-causality through testable hypotheses
New method recalibrates VaR for option books, reducing forecast errors.
problem Inaccurate VaR forecasts due to missing operational choices.
method Marking-aware sequential VaR recalibration targeting normalized book-level loss.
result Sequential VaR recalibration improves VaR performance across different markets and options.
Paper introduces a method to control early classification accuracy gaps.
problem Maintaining accuracy in early classification without full input processing.
method Statistical framework for a calibrated stopping rule.
result Reduces up to 94% of timesteps while controlling accuracy gaps.
New voting rules protect against strategic voting by robust statistics.
problem Strategic voting can skew election outcomes.
method Revisit Mallows model, develop robust estimator.
result Efficient estimator achieves nearly optimal robustness.
CRPS improves GP-based sequential design for chemical space.
problem Finding molecules with specific properties in synthetic chemistry.
method Threshold-weighted CRPS as acquisition function for GP models in sequential design.
result Improved performance in molecule research with CRPS-based strategies.
Near-optimal tests and confidence sequences for non-parametric data.
problem Flexible statistical inference and decision-making with non-parametric data.
method Classic delayed-start normal-mixture sequential probability ratio tests with asymptotic guarantees.
result Asymptotically optimal type-I error and expected rejection time guarantees.
Efficient algorithm reduces control system regret to sqrt(t).
problem Control of linear quadratic systems with guaranteed anytime performance.
method Combines SDP-based framework with regularization and perturbation mechanisms.
result Achieves anytime regret of order sqrt(t) with explicit dependence on system dimensions.
This work tackles lifelong unsupervised generative modeling.
problem Learning multiple tasks sequentially with knowledge retention.
method Student-Teacher Variational Autoencoder architecture with cross-model regularizer.
result Model mitigates catastrophic interference in sequential learning.
A Python package for GPU-accelerated signature kernel computation.
problem Efficient computation of signature kernels for sequential data.
method GPU-accelerated algorithms and tensor sketches.
result New algorithm outperforms existing methods.
Reduces change detection to estimation using confidence sequences.
problem Detecting changes in data streams with minimal delay and false alarms.
method Reduction from sequential change detection to sequential estimation using confidence sequences.
result Change detection scheme with minimal structural assumptions and strong guarantees.
Product models of low dimensional experts are a powerful way to avoid the curse of dimensionality. We present the ``under-complete product of experts' (UPoE), where each expert models a one dimensional projection of the data. The UPoE is fully tractable and may be interpreted as a parametric probabilistic model for pro…
Combines control variates and adaptive importance sampling for Monte Carlo integration.
problem Improving Monte Carlo integration accuracy with control variates and adaptive sampling.
method A quadrature rule combining control variates and adaptive importance sampling.
result Non-asymptotic bound on the probabilistic error of the procedure.
Bayesian model predicts sequences better than LSTMs by identifying underlying rules.
problem Current RNNs struggle to generalize from limited training data and identify underlying rules in sequences.
method Bayesian model that learns underlying concepts from sequences and generalizes to new data.
result Bayesian model predicts sequences better than traditional LSTMs.
New deviation inequalities for multi-armed bandit models.
problem Analyzing adaptive sampling in multi-armed bandit models.
method Constructing mixture martingales based on hierarchical priors.
result Deviation inequalities valid uniformly in time for adaptive sampling.
Two bandit frameworks for learning the distribution with highest mean.
problem Learning which distribution has the highest mean among a set of distributions.
method Asymptotically optimal algorithms for regret minimization and best arm identification.
result Comparison of sampling rules and complexity terms for both problems.
New methods prune unpromising rules from KGs, improving scalability and runtime.
problem Scalability issues in walk-based rule learning from KGs.
method Rule Hierarchy Framework (RHF) and Hierarchical Pruning (HPMs).
result Significant reductions in runtime and number of learned rules without compromising predictive performance.