New nonparametric tests for high-dimensional k-sample comparisons.
problem High-dimensional k-sample comparison problems.
method Nonparametric distribution-free tests based on spectral graph theory.
result The tests are effective and have practical applications.
Paper proposes universally consistent K-sample tests using any dependence measure.
problem Testing whether K groups of data points are drawn from the same distribution.
method Demonstrates the use of any dependence measure for K-sample testing.
result Achieves universally consistent K-sample testing using distance correlation and Hilbert-Schmidt independence criterion.
hyppo simplifies multivariate hypothesis testing in Python.
problem Inconsistent multivariate hypothesis testing interfaces in Python.
method Unified library for multivariate testing procedures.
result Easy-to-use and flexible for future extensions.
Massively parallel RWS improves inference in complex models.
problem Exponential sample requirement for effective importance weighting.
method Draws K samples of all n latent variables and individually reasons through all combinations. result Significant improvements over standard RWS.
The paper addresses hypothesis selection with local differential privacy, requiring more samples than non-private methods.
problem Hypothesis selection under local differential privacy constraints.
method Develops algorithms for hypothesis selection with local differential privacy, achieving near-optimal sample and round complexities.
result Non-interactive algorithms for k-wise simple hypothesis testing require O~(k) samples and O(loglogk) rounds of interaction. CEDA analyzes large categorical datasets using tree geometry and binary codes.
problem Analyzing large categorical datasets with extreme-K samples. method CEDA uses tree geometry and binary codes to analyze categorical data.
result CEDA discovers patterns and evaluates their reliability in large categorical datasets.
Algorithm learns CNF formulas from random solutions under specific conditions.
problem Learning a CNF formula from uniform random solutions.
method Revisits Valiant's algorithm and applies Lovász local lemma conditions.
result Significantly reduces sample complexity for learning CNFs.
Recurrent models can produce infinite sequences, causing bias; new methods prevent this.
problem Inconsistency in decoding infinite-length sequences from recurrent language models.
method Defined and proved inconsistency of common decoding algorithms; proposed remedies.
result Proposed methods prevent inconsistency in practice.
We introduce a new sampling method for large language models that balances diversity and parallelism.
problem Balancing diversity and parallelism in decoding for large language models.
method Arithmetic sampling framework compatible with various sampling variations.
result Improves estimation of expected BLEU score reward and reduces the gap with beam search.
Three-layer neural networks learn hierarchical polynomial functions efficiently.
problem Learning hierarchical polynomial functions with three-layer neural networks.
method Layerwise gradient descent on square loss, focusing on feature learning.
result Achieves optimal sample complexity for learning hierarchical polynomials.
Private method measures nonlinear correlations between data hosted across two entities.
problem Measuring nonlinear correlations between sensitive data hosted across multiple parties while preserving privacy.
method Differentially private estimator of distance correlation.
result First private estimator of nonlinear correlations in a multi-party setup.
A new measure k-variance captures local distributional shape.
problem Summarizing distributional shape with local information.
method Random bipartite matchings and stochastic approximation.
result Easily approximated k-variance measures capture local distributional properties. Study learns Gaussian mixtures from censored data.
problem Learning Gaussian mixtures with incomplete data.
method Proposes an algorithm to estimate weights and means with minimal samples.
result Achieves accurate estimation with very few samples.
OPORP combines permutation and random projection for efficient data vector compression.
problem Efficiently estimating cosine similarity in embedding-based retrieval applications.
method OPORP uses a permutation followed by a random vector dot product, then aggregates and normalizes the results into bins.
result OPORP improves the estimation of cosine similarity, reducing variance and improving accuracy.
Study uses reinforcement learning to optimize portfolios under recursive utility.
problem Improving portfolio allocation using risk-sensitive objectives.
method Approximated certainty equivalent via Monte Carlo, trained actor-critic algorithms (PPO, A2C).
result Recursive-utility agent outperforms discounted baseline in Sharpe ratio, max drawdown, and cumulative return.
A new SGD variant chooses the sample with lowest loss to make the model more robust to outliers.
problem Outliers can skew the parameters of machine learning models trained via SGD.
method Choose a set of k samples, then select the one with the smallest current loss for update.
result The new method makes SGD more robust for ML problems that are sums of convex losses.
New metrics improve quantum ensemble learning efficiency and power.
problem Quantum ensembles' distances poorly understood due to measurement constraints.
method Introduce MMD-k hierarchy of integral probability metrics for quantum ensembles. result MMD-k requires fewer samples for full discriminative power at higher k. Adaptive contrastive search improves text generation quality and diversity.
problem Decoding high-quality text from language model outputs.
method Adaptive contrastive search incorporating uncertainty-guided degeneration penalty.
result Enhanced text generation quality and diversity across different models and datasets.
New definitions and algorithms improve sample complexity for identifying good arms in multi-armed bandits.
problem Identifying arms with means close to the maximum or larger than a threshold.
method Formalized definitions, lower bounds, and practical algorithms.
result Achieved nearly matching upper bounds with sample complexities of Θ( rac{n}{m}) and Θ( rac{n}{m}k).
Multi-sample, importance-weighted variational autoencoders (IWAE) give tighter bounds and more accurate uncertainty estimates than variational autoencoders (VAE) trained with a standard single-sample objective. However, IWAEs scale poorly: as the latent dimensionality grows, they require exponentially many samples to r…
SLOPE outperforms LASSO in low noise scenarios but is suboptimal in large noise scenarios.
problem Sparse linear regression with high-dimensional data.
method Characterized SLOPE's estimation error under specific conditions and compared it with LASSO and bridge regression.
result SLOPE is optimal for low noise scenarios but suboptimal in large noise scenarios.
A new growth model for dynamic networks using Markovian latent points.
problem Modeling temporal dynamic networks with latent points and distances.
method Markovian latent space dynamic with Euclidean Sphere sampling and connection probabilities based on geodesic distances.
result Theoretical guarantees for non-parametric estimation of the latitude and envelope functions.
Software package assesses spherical data distributions and clusters.
problem Assessing and clustering spherical data distributions.
method Innovative goodness-of-fit tests and clustering algorithms using kernel-based quadratic distances.
result Efficient and mathematically sound goodness-of-fit tests for spherical data.
Paper proposes a chi-square test for distance correlation.
problem Testing distance correlation is computationally expensive.
method Proposes a chi-square test for distance correlation, non-parametric, fast, applicable to various metrics.
result Chi-square test exhibits similar power to permutation test and can be valid and universally consistent for testing independence.
Graph comparison ties to Alexandrov's theorems.
problem Graph comparison conditions on metric spaces.
method Proof of Alexandrov's implications from graph comparisons.
result Complete description of graphs with trivial comparisons.
New measure FTC quantifies how much a ReLU network can fine-tune.
problem Analyzing memorization capacity in fine-tuned neural networks.
method Defined Fine-Tuning Capacity (FTC) for additive fine-tuning of ReLU networks.
result Upper and lower bounds on FTC for 2 and 3-layer ReLU networks.
A study on the communication complexity of estimating correlations between variables.
problem Estimating the correlation between two sets of correlated random variables with limited communication.
method One-way interactive protocol exchanging k bits, optimizing over interaction protocol and estimator.
result Achieves optimal performance with communication complexity of 1/k, improving over naive schemes.
Improved linear regression for diverse data batches.
problem Learning from multiple heterogeneous data sources.
method Gradient-based algorithm for different input distributions.
result Significant reduction in the number of required batches and sample size.
In this work, we will verify some comparison results on Kahler manifolds. They are complex Hessian comparison for the distance function from a closed complex submanifold of a Kahler manifold with holomorphic bisectional curvature bounded below by a constant, eigenvalue comparison and volume comparison in terms of scala…
The paper extends volume comparison results to total σ_l-curvature.
problem Comparing total σ_l-curvature with σ_k-curvature.
method Volume comparison theorem extension to σ-curvature comparison.
result Comparison holds for metrics close to strictly stable positive Einstein metrics.
Paper extends curvature estimates to new tensor types.
problem Mean curvature and volume comparison estimates for integral generalized quasi-Einstein tensors.
method Extends existing comparison results to new tensor types.
result Global diameter estimates derived from comparison results.
New method improves prediction accuracy in comparison data.
problem Efficiently predicting outcomes in limited comparison data.
method Empirical Bayes shrinkage methods for pairwise uncertainty estimation.
result Empirical Bayes shrinkage outperforms standard methods in comparison data.
A new comparison theorem for geometric spaces.
problem Geometric space comparison theorems.
method Relative form of Toponogov comparison theorem.
result New geometric space comparison theorem established.
Paper investigates rigidity phenomena for weighted Ricci curvature bounds with Laplacian comparison theorem.
problem Investigating rigidity phenomena for weighted Ricci curvature bounds.
method Derived comparison geometric estimates and generalized for non-symmetric Laplacian.
result Obtained rigidity results for Laplacian comparison theorem, diameter comparisons, and volume comparisons.
Flatness of manifolds with open flat subsets proven using bipolar comparisons.
problem Conditions for flatness in Riemannian manifolds.
method Using (3,3)-bipolar comparisons and open flat subsets.
result Flatness of manifolds proven under specific conditions.
Study on manifolds with density using modified Hessians for curvature comparison.
problem Developing comparison geometry on manifolds with density.
method Modified Hessian approach based on weighted sectional curvature framework.
result Derivation of Hessian comparison and shape operator comparison theorems.
Optimal sampling bounds for various classification losses under different regularization terms.
problem Achieving optimal sampling complexity for classification losses under different regularization terms.
method Proved optimal sampling bounds for a broad class of Lipschitz continuous classification loss functions under various regularization terms.
result Proved k2/ε2 upper and lower bounds for ∥⋅∥2/k regularization, and k/ε2 upper and lower bounds for ∥⋅∥1/k regularization. New model reduces bias in crowdsourced pairwise comparisons.
problem Crowdsourced pairwise comparisons are biased due to perceptual factors.
method factorBT model accounts for irrelevant factors affecting worker answers.
result factorBT produces more accurate rankings than previous models.
Researchers developed volume comparison theorems in Finsler spacetimes.
problem Volume comparison in Finsler spacetimes with specific curvature conditions.
method Riccati equation techniques applied to (1+n)-dimensional Lorentz--Finsler manifolds. result Established volume comparison theorems for standard sets in Lorentzian volumes (SCLVs).
The study establishes comparison theorems for weighted Finsler manifolds and spacetimes.
problem Analyzing weighted Finsler manifolds and spacetimes with curvature conditions.
method Using weight function and ε-range, the Bonnet-Myers theorem, Laplacian comparison theorem, and Bishop-Gromov volume comparison theorem are formulated. result New comparison theorems for weighted Finsler manifolds and spacetimes are derived, including those for weighted Riemannian manifolds.
CV inference can be invalid for relatively unstable model comparisons.
problem The validity of cross-validation for model comparison is questioned when models are relatively unstable.
method The study proves that simple, individually stable models can generate relatively unstable comparisons, invalidating CV inference.
result The Lasso and soft-thresholding generate relatively unstable comparisons, invalidating CV inferences.
Paper proves a new volume comparison theorem for Riemannian manifolds.
problem Comparing volumes of boundaries in Riemannian manifolds.
method Inspired by Schur's theorem, applies to Riemannian manifolds with Ricci curvature.
result Provides a new Schur's type volume comparison theorem.
Comprisk simplifies competing-risks analysis in Python.
problem Analyzing medical time-to-event data with competing risks.
method A scikit-learn-compatible toolkit for competing-risks survival analysis.
result Comprisk provides a unified API for various competing-risks methods.
Proposes a revenue function to evaluate dendrograms from comparisons.
problem Evaluate dendrograms from comparisons without ground-truth.
method Introduces a new revenue function related to Dasgupta's cost.
result Revenue function allows meaningful evaluation of dendrograms.
We study the problem of interactively learning a binary classifier using noisy labeling and pairwise comparison oracles, where the comparison oracle answers which one in the given two instances is more likely to be positive. Learning from such oracles has multiple applications where obtaining direct labels is harder bu…
Trees and their products compared to octahedron.
problem Comparing trees and their products to octahedron.
method Product comparison of trees to octahedron.
result Trees and their products meet octahedron comparison.
Minimal surfaces help compare geometric shapes.
problem Comparing different geometric shapes.
method Applications of minimal surfaces.
result New insights into geometric comparisons.
ROVAE uses noisy pairwise comparisons to disentangle factors in VAEs.
problem Disentangling factors in VAEs requires an inductive bias.
method Robust Ordinal VAE (ROVAE) incorporates noisy pairwise ordinal comparisons to disentangle factors.
result ROVAE outperforms existing methods and is more robust to noisy comparisons.