D2KLab's approach predicts tweet engagement using two stages.
problem Predicting user engagement with tweets.
method Two-stage approach: feature learning and ensemble XGBoost.
result Ranked 22 in the 2020 RecSys Challenge leaderboard.
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A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
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48 results for “two-stage approach”
A Statistical Decision-Theoretical Perspective on the Two-Stage Approach to Parameter Estimationstat.ME
The paper provides a statistical decision-theoretical derivation of the Two-Stage approach for parameter estimation.
problem Theoretical justification for the Two-Stage approach in situations where likelihood is difficult to evaluate.
method Statistical decision-theoretical derivation leading to Bayesian and Minimax estimators.
result The Two-Stage approach is justified theoretically and applied to independent and identically distributed samples.
A new framework integrates classification and regression tasks in multi-task learning.
problem Jointly solving classification and regression tasks in multi-task scenarios.
method Two-Stage Learning-to-Defer (L2D) framework with a unified deferral mechanism.
result Unified deferral mechanism ensures convergence to the Bayes-optimal rejector.
Develops a two-stage approach for robust tensor completion of visual data.
problem Estimating missing values in high-order data with outliers.
method Coarse-to-fine framework and M-estimator-based robust tensor ring recovery.
result Superior performance compared to state-of-the-art robust algorithms.
Improved learning theory for kernel distribution regression with two-stage sampling.
problem Distribution regression problem and two-stage sampling setting.
method Kernel methods, near-unbiased condition, new error bounds, convergence rates.
result Strictly improved convergence rates for three important classes of kernels.
The paper analyzes how generated data improves adversarial training in high-dimensional regression.
problem Improving adversarial training in high-dimensional regression.
method Theoretical analysis of a two-stage training approach with generated data and pseudo-labels.
result Two-stage adversarial training achieves better performance than ridgeless training in high-dimensional linear regression.
SGD improves DR by solving two-stage sampling problems.
problem Improving the learning properties of SGD for distribution regression.
method Applying SGD to two-stage sampling problems in distribution regression.
result Theoretical guarantees for SGD's performance in DR, with optimal bounds.
Two-stage recommender systems show better performance when components interact rather than operate independently.
problem Two-stage recommender systems are often treated as sums of their parts, ignoring interactions between components.
method Used synthetic and real-world data to demonstrate interactions between ranker and nominators. Derived a generalization lower bound and proposed a Mixture-of-Experts approach to learn optimal item pools.
result Independent nominator training can lead to performance on par with random recommendations, highlighting the importance of interactions.
POTEC tackles off-policy learning in large action spaces, improving effectiveness.
problem Existing OPL methods fail in large discrete action spaces due to bias or variance issues.
method Two-stage algorithm: cluster selection via policy-based approach, action selection via regression-based approach.
result POTEC provides substantial improvements in off-policy learning effectiveness, especially in large and structured action spaces.
To integrate strategic, tactical and operational decisions, the two-stage optimization has been widely used to guide dynamic decision making. In this paper, we study the two-stage stochastic programming for complex systems with unknown response estimated by simulation. We introduce the global-local metamodel assisted t…
Two-stage risk control for ranked retrieval systems.
problem Assessing prediction uncertainty and risk control in sequential machine learning systems.
method Developed two-stage risk control methods based on LTT and CRC frameworks, leveraging sequential nature of retrieval and ranking phases.
result The proposed methods provide theoretical guarantees and reduce computational burden compared to prior work.
Paper proposes a two-stage ranking for personalized TV recommendations.
problem Improving TV recommendation accuracy and efficiency.
method First, identifies potential candidates using user viewing patterns. Then, ranks them based on user preferences and program textual information.
result The proposed model outperforms in recommendation accuracy and efficiency.
A new conformal prediction framework for two-stage models identifies stage-wise uncertainty.
problem Limited coverage guarantees and lack of modular structure understanding in existing conformal prediction methods.
method Decomposes prediction residuals into stage-specific components, calibrates parameters using FWER control, and adapts to non-stationary settings.
result Improves coverage and identifies stage-wise error contributions compared to standard conformal methods.
TSCI estimates treatment effects using machine learning and data-adaptive methods for invalid instruments.
problem Estimating treatment effects with invalid instruments.
method Two-stage algorithm: first stage uses machine learning for nonlinearities, second stage selects and projects out instrument violations.
result Effective treatment effect estimation even with invalid instruments.
Power supply from renewable resources is on a global rise where it is forecasted that renewable generation will surpass other types of generation in a foreseeable future. Increased generation from renewable resources, mainly solar and wind, exposes the power grid to more vulnerabilities, conceivably due to their variab…
A novel two-stage resampling method improves CNN training on imbalanced colorectal cancer image data.
problem Data imbalance in medical image datasets, especially in histopathological images.
method Two-stage resampling: first oversampling in image space, then undersampling in feature space.
result The proposed method enhances CNN training on imbalanced colorectal cancer image datasets.
Proposes a two-stage method for selecting correlated predictors in high-dimensional data.
problem Selecting correlated predictors in high-dimensional data with unknown group structures.
method Two-stage approach: variable clustering followed by group selection.
result The two-stage method improves prediction accuracy and active predictor selection.
A two-stage hybrid model by using artificial neural networks as feature construction algorithmsstat.ML
We propose a two-stage hybrid approach with neural networks as the new feature construction algorithms for bankcard response classifications. The hybrid model uses a very simple neural network structure as the new feature construction tool in the first stage, then the newly created features are used as the additional i…
Joint training improves model accuracy by selectively using privileged information.
problem Two-stage training can lead to model failure with noisy privileged information.
method Joint training of two models to use privileged information selectively.
result Joint training outperforms two-stage baselines on synthetic and real-world tasks.
This paper proposes a two-stage scoring approach to help lenders decide their fund allocations in the peer-to-peer (P2P) lending market. The existing scoring approaches focus on only either probability of default (PD) prediction, known as credit scoring, or profitability prediction, known as profit scoring, to identify…
Localized Kernel Projection Outlyingness: A Two-Stage Approach for Multi-Modal Outlier Detectioncs.LG
Two-stage framework detects multi-modal outliers.
problem Detecting outliers in multi-modal data structures.
method Combines global kernel PCA and local clustering stages.
result Significantly outperforms existing methods on challenging datasets.
We propose a novel method designed for large-scale regression problems, namely the two-stage best-scored random forest (TBRF). "Best-scored" means to select one regression tree with the best empirical performance out of a certain number of purely random regression tree candidates, and "two-stage" means to divide the or…
Subspace clustering refers to the problem of clustering high-dimensional data into a union of low-dimensional subspaces. Current subspace clustering approaches are usually based on a two-stage framework. In the first stage, an affinity matrix is generated from data. In the second one, spectral clustering is applied on …
With the increasing complexity of modern power systems, conventional dynamic load modeling with ZIP and induction motors (ZIP + IM) is no longer adequate to address the current load characteristic transitions. In recent years, the WECC composite load model (WECC CLM) has shown to effectively capture the dynamic load re…
Proposes a two-stage method for estimating heterogeneous treatment effects using gradient boosting trees.
problem Estimating heterogeneous treatment effects in randomized clinical trials with high-dimensional predictive markers.
method Two-stage statistical learning procedure using gradient boosting trees (XGBoost) to estimate main effects and HTE.
result Improves efficiency in estimating heterogeneous treatment effects through nonparametric function estimation.
The paper addresses statistical estimation in MDPs with confounders using instrumental variables.
problem Statistical estimation of value functions in MDPs with unobservable confounders.
method Two-stage estimator based on instrumental variables for confounded linear MDPs.
result Established statistical properties of the two-stage estimator, including error bounds and asymptotic normality.
Recent work on discriminative segmental models has shown that they can achieve competitive speech recognition performance, using features based on deep neural frame classifiers. However, segmental models can be more challenging to train than standard frame-based approaches. While some segmental models have been success…
We present generalization bounds for the TS-MKL framework for two stage multiple kernel learning. We also present bounds for sparse kernel learning formulations within the TS-MKL framework.
Optimized deferral improves accuracy in imbalanced settings.
problem Imbalance in expert predictions leads to suboptimal performance in two-stage learning to defer.
method Developed novel cost-sensitive learning algorithms and margin-based loss functions tailored for expert imbalance.
result MILD algorithm shows clear improvements over baselines in image classification and LLM routing tasks.
SARD improves adversarial robustness in two-stage L2D systems.
problem Adversarial attacks can manipulate query allocation in two-stage L2D systems.
method Introduces SARD, a convex learning algorithm with provable guarantees.
result SARD significantly improves robustness under adversarial attacks while maintaining strong clean performance.
Sparse principal component analysis (PCA) involves nonconvex optimization for which the global solution is hard to obtain. To address this issue, one popular approach is convex relaxation. However, such an approach may produce suboptimal estimators due to the relaxation effect. To optimally estimate sparse principal su…
Kernel methods estimate causal effects with a single proxy for deterministic confounders.
problem Estimating causal effects with a single proxy for an unobserved confounder.
method Two kernel-based methods: two-stage regression and maximum moment restriction.
result Both kernel methods can consistently estimate the causal effect.
Develops a two-stage conformal prediction method for Parkinson's disease medication needs.
problem Heterogeneous disease progression and treatment response in Parkinson's Disease.
method Two-stage conformal prediction framework with statistical guarantees.
result Quantifies uncertainty in medication needs predictions, improving clinical trust and quality of life.
CASP selects reliable policies for two-stage recommender systems by considering both value and support.
problem The selection of a generator in two-stage recommender systems affects both the policy value and the data support used to estimate it.
method CASP combines doubly robust value estimation with a support-burden penalty.
result CASP selects lower-burden policies when estimated value and support credibility are in tension.
Two-stage TMLE reduces bias and improves efficiency in CRTs.
problem Differential outcome measurement and imbalance in baseline predictors in CRTs.
method Two-stage targeted minimum loss-based estimator (TMLE) to adjust for baseline covariates.
result Our approach nearly eliminates bias due to differential outcome measurement.
Proposes a two-stage method for testing variable interactions with FDR control.
problem Testing pairwise interactions in high-dimensional data with dependence.
method Two-stage testing procedure with FDR control using Cramér type moderate deviation technique.
result The proposed method controls FDR and has comparable or improved statistical power.
Bayesian optimization tackles non-convex, two-stage stochastic problems efficiently.
problem Solving non-convex, two-stage stochastic optimization problems with expensive, black-box evaluations.
method Knowledge-gradient-based acquisition function for joint optimization of first- and second-stage variables.
result Comparable and superior empirical results compared to alternatives.
Two-stage recommender systems struggle with exploration, leading to linear regret.
problem Linear regret in two-stage recommender systems due to exploration issues.
method Proposed a method to synchronize exploration strategies between the ranker and nominators using LinUCB.
result Demonstrated the effectiveness of the proposed algorithm experimentally.
Two-stage mechanism designs reduce regret in recommender systems with stochastic covariates.
problem Designing effective recommender systems with user covariates sampled online.
method Two-stage algorithm integrating incentivized exploration with offline learning methods.
result Achieves sublinear regret while maintaining incentive compatibility.
Two-stage model improves credit scoring predictions.
problem Distribution shift in finance datasets.
method Two-stage model with out-of-distribution detection and domain knowledge.
result Highly reliable predictions for most datasets.
CobBO optimizes expensive functions in high dimensions by using a two-stage kernel approach.
problem Bayesian optimization struggles in high dimensions due to computational inefficiency.
method Coordinate backoff Bayesian Optimization with two-stage kernels.
result CobBO finds solutions comparable to or better than other methods in high dimensions.
The sheer scale of modern datasets has resulted in a dire need for summarization techniques that identify representative elements in a dataset. Fortunately, the vast majority of data summarization tasks satisfy an intuitive diminishing returns condition known as submodularity, which allows us to find nearly-optimal sol…
The paper develops classifiers that encourage positive adaptation in machine learning settings.
problem Strategic behavior by decision subjects leads to performance loss in machine learning models.
method Formulates a two-stage game to characterize optimal strategies for model designers and decision subjects.
result Trained classifiers maintain accuracy while inducing higher improvement and less manipulation.
New algorithm protects privacy in IVaR regression while maintaining accuracy.
problem Privacy leakage in classical IVaR methods.
method Noisy two-stage gradient descent with differential privacy guarantees.
result Achieves statistical efficiency and privacy in IVaR regression.
Paper tackles moment estimation under covariate shift with a two-stage algorithm.
problem Estimating moments under covariate shift when source and target distributions differ.
method Proposes a two-stage algorithm: first, an optimal estimator for the source distribution; second, likelihood ratio reweighting for calibration.
result Achieves minimax optimal bound for moment estimation.
Current deep learning results on video generation are limited while there are only a few first results on video prediction and no relevant significant results on video completion. This is due to the severe ill-posedness inherent in these three problems. In this paper, we focus on human action videos, and propose a gene…
JTT improves model worst-group accuracy without group annotations.
problem Low worst-group accuracy in standard ERM models with spurious correlations.
method Two-stage approach: first ERM, then upweight misclassified examples.
result JTT closes 75% of the gap in worst-group accuracy compared to group DRO.
Differentially private method for estimating individualized treatment rules.
problem Estimating individualized treatment rules while preserving privacy.
method Differentially private two-stage empirical risk minimization (DP-2ERM).
result Improved privacy-utility trade-off demonstrated through simulations and applications.