Presents an end-to-end constraint-aware quantum optimization pipeline using XY-mixer QAOA and Grover Adaptive Search for low-energy defect configurations in doped ZrO2, with QAOA validated against exact enumeration on a high-accuracy QUBO surrogate of MACE energies.
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Hoerl and Robert W
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representative citing papers
TS-Neyman uses posterior sampling of stratum variances to implement an adaptive Neyman allocation rule that converges almost surely to the oracle proportions and achieves near-oracle efficiency in finite-strata settings.
Operator-adaptive PLS and Ridge models integrate linear preprocessing screening internally via algebraic identities, delivering comparable or better prediction accuracy than exhaustive external search on NIR regression and classification tasks with orders-of-magnitude lower fitting time.
Balmer decrements in CV spectra form a diagnostic diagram that separates period-bouncers from pre-bounce systems via fitted logistic regression.
StarTime applies a temporal tree to enable sparse or aggregated coefficient selection in high-order autoregressions and mixed-frequency regressions, with new error bounds and better simulation performance than benchmarks.
The authors propose target-space recovery profiles to diagnose which reproducible dimensions of fMRI brain responses are captured by model predictions, showing that accuracy alone can mask alignment mismatches in visual cortex.
Non-autoregressive ionic transport predictor learns dynamics from auxiliary trajectory data during training only, achieving over 200x speedup versus autoregressive models and lower error than non-autoregressive baselines on both dataset types.
KAHM yields a compute-efficient query encoder that outperforms matched learned adapters in reconstructing a frozen Mixedbread embedding space on an Austrian-law retrieval task while delivering an 8.53x CPU speedup.
Orthogonal reparametrization via QR decomposition renders NSS linear parameters uncorrelated with diagonal conditional Fisher information, providing a scalar identifiability diagnostic and closed-form finite-horizon orthogonal basis.
EBBS augments the MIO best-subsets objective with an aggregated expert prior expressed as a log-odds penalty so that selected features align with domain consensus while reducing to ordinary best subsets when experts provide no input.
Joint NMF and binomial regression learns response-relevant text signals with competitive performance on simulations and review data.
Proposes PcovRnnp method enabling simultaneous dimension reduction and regularized coefficient estimation via nuclear norm penalty in high-dimensional settings.
Difference-in-differences analysis around ChatGPT release shows commoditization of labor in AI-exposed job categories on Upwork, with declining human capital importance and rising price importance.
Oracle Markov boundaries improve prediction on high-dimensional sparse tabular data but causal discovery pipelines rarely recover boundaries that beat using all features.
Physics-guided data augmentation combined with neural networks enables accurate indentation size effect correction in steels from small sets of shallow nanoindentation measurements, outperforming Nix-Gao in the shallow regime.
Bayesian-ARGOS is a hybrid frequentist-Bayesian method that discovers equations from limited noisy observations more efficiently than SINDy or bootstrap-ARGOS while adding uncertainty quantification.
Quantum simulation methods for Thirring and Gross-Neveu fermionic models with arbitrary flavors, including gate complexity bounds and ground-state preparation up to 20 qubits.
Frozen multimodal embeddings with trait-specific late fusion cut personality prediction MSE by 19% relative to baseline in the 2026 AVI challenge, while cognitive results are attributed to validation shortcuts rather than content-based inference.
Kernel ridge regression combined with mRMR feature selection improves prediction of full benchmark scores from question subsets over existing efficient benchmarking techniques.
Joint modeling of multiple subjects' fMRI data produces low-dimensional embeddings that outperform raw high-dimensional voxel spaces on music genre and language topic classification while increasing semantic richness.
Compares pruning methods (Variance Filter, Branch and Bound) and regularization (LASSO, ridge) for training physical reservoir computers, reporting gains on nonlinear benchmarks using a fiber-optical extreme learning machine.
Compares six meta-learners (Cox/RSF risk models paired with elastic net/RF CATE models) via simulations differing in hazard complexity and censoring, and releases the R package crsurvlearners.
citing papers explorer
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Constraint-Aware Quantum Optimization of Defect Configurations in Doped ZrO2: XY-Mixer QAOA and Grover Adaptive Search
Presents an end-to-end constraint-aware quantum optimization pipeline using XY-mixer QAOA and Grover Adaptive Search for low-energy defect configurations in doped ZrO2, with QAOA validated against exact enumeration on a high-accuracy QUBO surrogate of MACE energies.
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TS-Neyman: Posterior Sampling for Adaptive Stratified Estimation
TS-Neyman uses posterior sampling of stratum variances to implement an adaptive Neyman allocation rule that converges almost surely to the oracle proportions and achieves near-oracle efficiency in finite-strata settings.
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Reframing preprocessing selection as model-internal calibration in near-infrared spectroscopy: A large-scale benchmark of operator-adaptive PLS and Ridge models
Operator-adaptive PLS and Ridge models integrate linear preprocessing screening internally via algebraic identities, delivering comparable or better prediction accuracy than exhaustive external search on NIR regression and classification tasks with orders-of-magnitude lower fitting time.
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Balmer decrements as a new diagnostic for period-bounce Cataclysmic Variable stars
Balmer decrements in CV spectra form a diagnostic diagram that separates period-bouncers from pre-bounce systems via fitted logistic regression.
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Sparse Tree-Based Aggregation for Time Series Regressions
StarTime applies a temporal tree to enable sparse or aggregated coefficient selection in high-order autoregressions and mixed-frequency regressions, with new error bounds and better simulation performance than benchmarks.
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Beyond Prediction Accuracy: Target-Space Recovery Profiles for Evaluating Model-Brain Alignment
The authors propose target-space recovery profiles to diagnose which reproducible dimensions of fMRI brain responses are captured by model predictions, showing that accuracy alone can mask alignment mismatches in visual cortex.
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Teaching Molecular Dynamics to a Non-Autoregressive Ionic Transport Predictor
Non-autoregressive ionic transport predictor learns dynamics from auxiliary trajectory data during training only, achieving over 200x speedup versus autoregressive models and lower error than non-autoregressive baselines on both dataset types.
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Kernel Affine Hull Machines as Compute-Efficient Encoders for Frozen Semantic Spaces
KAHM yields a compute-efficient query encoder that outperforms matched learned adapters in reconstructing a frozen Mixedbread embedding space on an Austrian-law retrieval task while delivering an 8.53x CPU speedup.
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Orthogonal reparametrization of the Nelson-Siegel-Svensson interest rate curve model: conditioning, diagnostics, and identifiability
Orthogonal reparametrization via QR decomposition renders NSS linear parameters uncorrelated with diagonal conditional Fisher information, providing a scalar identifiability diagnostic and closed-form finite-horizon orthogonal basis.
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A Mathematical Optimization Approach for Expert-Informed Bayesian Best Subset Selection
EBBS augments the MIO best-subsets objective with an aggregated expert prior expressed as a log-odds penalty so that selected features align with domain consensus while reducing to ordinary best subsets when experts provide no input.
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Learning Interpretable Text Signals for Structured Responses
Joint NMF and binomial regression learns response-relevant text signals with competitive performance on simulations and review data.
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Principal Covariate Regression with Nuclear Norm Penalty
Proposes PcovRnnp method enabling simultaneous dimension reduction and regularized coefficient estimation via nuclear norm penalty in high-dimensional settings.
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Human Capital, AI, and Labor Commoditization
Difference-in-differences analysis around ChatGPT release shows commoditization of labor in AI-exposed job categories on Upwork, with declining human capital importance and rising price importance.
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The Good, the Bad, and the Ugly of Markov Boundary for Tabular Prediction
Oracle Markov boundaries improve prediction on high-dimensional sparse tabular data but causal discovery pipelines rarely recover boundaries that beat using all features.
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Data-Efficient Indentation Size Effect Correction in Steels Using Machine Learning and Physics-Guided Augmentation
Physics-guided data augmentation combined with neural networks enables accurate indentation size effect correction in steels from small sets of shallow nanoindentation measurements, outperforming Nix-Gao in the shallow regime.
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Fast and principled equation discovery from chaos to climate
Bayesian-ARGOS is a hybrid frequentist-Bayesian method that discovers equations from limited noisy observations more efficiently than SINDy or bootstrap-ARGOS while adding uncertainty quantification.
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Quantum simulation of massive Thirring and Gross--Neveu models for arbitrary number of flavors
Quantum simulation methods for Thirring and Gross-Neveu fermionic models with arbitrary flavors, including gate complexity bounds and ground-state preparation up to 20 qubits.
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Frozen Multimodal Embeddings for AI-Assisted Interview Assessment of Personality and Cognitive Ability
Frozen multimodal embeddings with trait-specific late fusion cut personality prediction MSE by 19% relative to baseline in the 2026 AVI challenge, while cognitive results are attributed to validation shortcuts rather than content-based inference.
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Efficient Benchmarking Is Just Feature Selection and Multiple Regression
Kernel ridge regression combined with mRMR feature selection improves prediction of full benchmark scores from question subsets over existing efficient benchmarking techniques.
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Low-dimensional Embodied Semantics for Music and Language
Joint modeling of multiple subjects' fMRI data produces low-dimensional embeddings that outperform raw high-dimensional voxel spaces on music genre and language topic classification while increasing semantic richness.
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Effective Training Principles of Physical Reservoirs
Compares pruning methods (Variance Filter, Branch and Bound) and regularization (LASSO, ridge) for training physical reservoir computers, reporting gains on nonlinear benchmarks using a fiber-optical extreme learning machine.
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A Guide to Estimating Conditional Average Treatment Effects in Competing Risks Settings
Compares six meta-learners (Cox/RSF risk models paired with elastic net/RF CATE models) via simulations differing in hazard complexity and censoring, and releases the R package crsurvlearners.