Develops a multilevel method for solving BLUP and GLS models with high dimensional data.
problem Numerical instability and ill-conditioned covariance matrices in high-dimensional data.
method Multilevel basis construction and transformation of covariance matrices using kD-tree partitioning.
result The multilevel method solves BLUP and GLS models accurately and efficiently, scaling well with dimensions and observations.
Random investment strategies outperform sensible ones, even with forecasts.
problem The usefulness of investment strategies based on forecasts is questioned.
method Investigated the performance of sensible and nonsensical investment strategies, including forecasts.
result There is no substantial difference between the performances of ``best'' and ``trivial'' forecasts.
Generalizes Barankin bound for vector cases in mean square error.
problem Achieving the lower bound of mean square error for vector estimates.
method Finite dimensional vector Riesz representation theorem and linear matrix inequality.
result Necessary and sufficient conditions for achieving the lower bound.
DeepKriging uses DNNs to predict spatial data with improved accuracy and scalability.
problem Predicting spatial processes with non-linear and non-Gaussian data.
method Adds an embedding layer of spatial coordinates with basis functions to DNNs.
result DeepKriging provides non-linear predictions with smaller approximation errors and is scalable for large datasets.
Quantized Stochastic Primal-Dual Methods for Distributed Optimization
problem Distributed optimization with stochastic gradients and finite-bit communication
method q-PDGD, a quantized stochastic primal-dual method
result Linear contraction to an explicit neighborhood under RSI, O(1/k) convergence under PL inequality
We propose an algebraic combinatorial method for solving large sparse linear systems of equations locally - that is, a method which can compute single evaluations of the signal without computing the whole signal. The method scales only in the sparsity of the system and not in its size, and allows to provide error estim…
Improves survey sampling with unbiased machine learning methods.
problem Design-consistent model-assisted estimation lacks a general theory for machine learning.
method Proposes a subsampling Rao-Blackwell method for design-unbiased estimation.
result Yields efficiency gains over standard methods while ensuring valid estimation.
New estimator for prediction error using AMP algorithm for sparse penalties.
problem Estimating prediction error for penalized linear regression models.
method Approximate message passing (AMP) algorithm for calculating generalized degrees of freedom.
result Asymptotically unbiased estimator for Gaussian distributed predictors.
Algorithm finds best Dirac mass approximation of target measure.
problem Finding optimal Dirac mass approximation of target measure.
method Minimizes statistical distance between original measure and quantized version using Huber-energy kernel.
result HEMQ algorithm robust and versatile, matches intuitive behavior.
Attention improves edge prediction in e-commerce graphs.
problem Predicting edges in graphs from node attributes in e-commerce.
method Used attention mechanism in simple feedforward networks, derived analytically tractable model AttEST.
result Attention network outperforms LSTM architectures by over 20% on F-1 score.
The paper offers methods to estimate causal functions using machine learning.
problem Estimating causal effects and their derivatives accurately.
method Neyman-orthogonal signal adjustment, best linear predictor, Gaussian bootstrap inference.
result Automatic targeting of smooth structural functions and group average effects.
Paper presents IPVI for unbiased DGP inference with efficiency.
problem Intractable exact inference in deep Gaussian processes.
method Implicit posterior variational inference framework.
result IPVI achieves unbiased posterior belief with efficiency.
A new method selects variables for random survival forests using maximally selected rank statistics.
problem Random survival forests can be biased in selecting variables, especially for non-linear effects.
method Use maximally selected rank statistics for variable selection in random survival forests, comparing on p-value scale.
result The new method outperforms other approaches in prediction performance and computational speed.
The paper proposes multicalibration to improve matching in graphs with imperfect predictors.
problem Finding the best matching in graphs with imperfect predictors.
method Introduces multicalibration as a fairness notion to ensure unbiasedness on protected sets of contexts.
result Constructing a multicalibrated predictor that outperforms standard optimal rules in matching algorithms.
Develops efficient algorithms for spatial field reconstruction and sensor selection in heterogeneous weather sensor networks.
problem Efficient spatial field reconstruction and query-based sensor set selection in heterogeneous sensor networks.
method Spatial Best Linear Unbiased Estimator (S-BLUE) and Cross Entropy method.
result Efficient algorithms with performance guarantees for spatial field reconstruction and sensor selection.
Proposes a deep neural network for predicting clustered time-to-event data.
problem Predicting clustered time-to-event data with subject-specific frailties.
method Deep neural network based gamma frailty model (DNN-FM) trained using negative profiled h-likelihood.
result Enhances prediction performance compared to existing methods.
Linear-Core Surrogates combine fast optimization and statistical efficiency in classification and structured prediction.
problem The trade-off between smoothness and margin-based losses in classification and structured prediction.
method Linear-Core (LC) Surrogates, a family of convex loss functions that stitch a linear core to a smooth tail.
result LC Surrogates achieve fast linear consistency rates while maintaining differentiability and strict H-consistency bounds. Transformers with linear space and time complexity for accurate attention estimation.
problem Efficiently estimating attention in large-scale tasks without relying on priors.
method Performers use Fast Attention Via positive Orthogonal Random features (FAVOR+) for linear approximation of softmax attention.
result Performers achieve competitive results on various tasks, demonstrating the effectiveness of their attention-learning approach.
Proposes an adversarial algorithm to learn unbiased representations via HGR coefficient.
problem Learning fair representations without sensitive attribute information.
method Adversarial algorithm using Hirschfeld-Gebelein-Renyi (HGR) maximal correlation coefficient.
result Significant improvements in bias mitigation compared to existing methods.
New method reduces training cost by using approximate gradients.
problem Training neural networks is computationally expensive.
method Uses control variates to approximate gradients without full backward pass.
result Efficacy demonstrated on a vision transformer classification task.
Equalized coverage method ensures fair prediction intervals across protected groups.
problem Ensuring fair use of data-driven recommendation systems by communicating their uncertainty.
method Operational methodology that forces unbiased prediction intervals across all protected groups, offering rigorous distribution-free coverage guarantees.
result Equalized coverage constructs unbiased prediction intervals, unlike competitive methods.
Subset selection struggles in high noise; new method improves performance.
problem Subset selection's poor performance in high noise levels.
method Regularized version of least-squares criterion.
result Proposed estimators outperform best subset selection in high noise.
Combines multiple OPE estimators into a more accurate and efficient estimate.
problem Offline evaluation of recommender systems using biased data.
method Meta-analysis of correlated OPE estimators, accounting for inter-estimator correlation.
result Improved statistical efficiency and accuracy in estimating policy value.
Study shows practical optimization can outperform theoretical best models in competitive scenarios.
problem Does the best theoretical model always win in competitive machine learning?
method Game theoretic approach to analyze reward functions of models.
result In some cases, practical optimization can yield better rewards than theoretical best models.
In applications of Gaussian processes where quantification of uncertainty is of primary interest, it is necessary to accurately characterize the posterior distribution over covariance parameters. This paper proposes an adaptation of the Stochastic Gradient Langevin Dynamics algorithm to draw samples from the posterior …
New framework for unbiased sampling of temporal networks.
problem Challenges in analyzing and modeling large, continuous temporal networks.
method General framework for unbiased temporal network sampling with online, single-pass algorithms and unbiased estimators.
result Effective algorithms for fast, accurate, and memory-efficient statistical estimation of temporal network patterns and properties.
New variance-reduction methods solve stochastic composite inclusions.
problem Solving nonmonotone stochastic composite inclusions.
method Developed unbiased and biased variance-reduced estimators for FRBS method.
result Achieved best oracle complexities for finite-sum and expectation settings.
New particle filter estimates model evidence without bias.
problem Unbiased estimation of marginal likelihood for model comparison.
method Particle filter with rejection control.
result Unbiased estimation of marginal likelihood.
A new method speeds up sampling of Boltzmann distribution in high-dimensional systems.
problem High computational cost of obtaining Jacobian of flow-based models in high dimensions.
method Flow perturbation method that incorporates stochastic perturbations and reweighting.
result Achieves unbiased sampling of Boltzmann distribution with orders of magnitude speedup.
Linear-cost unbiased estimates for complex models via couplings.
problem High-dimensional Bayesian models with crossed effects and matrix factorization.
method Coupled Gibbs samplers for linear computational cost.
result Unbiased posterior estimates at linear cost.
Proposes MR estimator for unbiased recommendation models.
problem Data biases in recommender systems lead to inaccurate predictions.
method Introduces multiple robust (MR) learning approach combining multiple imputation and propensity models.
result MR estimator achieves unbiasedness when any of the models is accurate.
Optimal Gaussian noise mechanisms achieve nearly optimal error in unbiased mean estimation.
problem Efficiently estimating the mean of high-dimensional data while preserving privacy.
method Differential privacy mechanisms with Gaussian noise, focusing on optimal covariance.
result Gaussian noise mechanisms achieve nearly optimal error among all private unbiased mean estimation mechanisms.
SUCAG accelerates convergence in distributed optimization with unbiased curvature information.
problem Efficiently solving finite sum optimization problems in distributed settings.
method Stochastic Unbiased Curvature-aided Gradient (SUCAG) method using Hessian information.
result Establishes linear convergence for strongly convex problems under asynchronous model.
New insights and algorithms improve prediction models with time-series privileged information.
problem Efficient learning of nonlinear prediction models with limited data.
method Generalization of LuPI to nonlinear tasks, using random features and representation learning.
result Theoretical and empirical evidence supports the use of privileged time-series information for nonlinear prediction.
Methods for combining predictions from different models in a supervised learning setting must somehow estimate/predict the quality of a model's predictions at unknown future inputs. Many of these methods (often implicitly) make the assumption that the test inputs are identical to the training inputs, which is seldom re…
Unbiased gradient estimation for Markov chains
problem Estimating gradients of stationary means in Markov chains
method Propose new unbiased estimators
result Improves efficiency for slow mixing Markov chains
SELO model predicts link signs better than SDGNN using subgraph encoding and linear optimization.
problem Inferring the sign of links in signed networks with limited sign data.
method Subgraph Encoding via Linear Optimization (SELO) approach to learn edge embeddings.
result SELO model outperforms state-of-the-art methods on multiple real-world signed networks.
Study improves dynamic PT fleet optimization under noisy demand predictions.
problem Accurately predicting dynamic public transport demand for effective fleet management.
method Experimental case study in Copenhagen, using linear programming to optimize fleets.
result Optimized fleet performance is mainly affected by noise distribution skew and large errors.
Improves treatment effect estimates using coordinated deep learning.
problem Estimating treatment effects from observational data with high-dimensional covariates.
method Uses double machine learning with a coordinated deep learning algorithm to reduce bias.
result Demonstrates improved empirical performance through numerical experiments.
DeepRAM evaluates and selects the best deep learning architecture for DNA/RNA binding specificity prediction.
problem Selecting the best deep learning architecture for predicting DNA/RNA binding specificity.
method Systematic exploration of various deep learning architectures using deepRAM, an end-to-end deep learning tool.
result A k-mer embedding convolutional layer and recurrent layer architecture outperforms other methods.
Paper addresses high-dimensional linear regression with missing data, proposing efficient and nearly unbiased estimators.
problem High-dimensional linear regression with blockwise missing covariates and partially observed responses.
method Proposes a computationally efficient estimator and nearly unbiased debiased estimators using blockwise imputation and estimating equations.
result Asymptotically valid confidence intervals and statistical tests constructed based on debiased estimators.
New method uses rank-conditioned Horvitz-Thompson estimation for unbiased sample reuse in Plackett-Luce best-of-K objective.
problem Estimating the expected maximum reward in Plackett-Luce draws without replacement.
method Rank-conditioned Horvitz-Thompson estimation with joint-score REINFORCE for unbiased sample reuse.
result Unbiased estimation of the Plackett-Luce best-of-K objective with finite second moment guarantees.
The paper presents unbiased estimators for random design regression.
problem Bias in least squares solutions for random design regression.
method Volume-rescaled sampling of input points to produce unbiased estimators.
result An unbiased estimator can be constructed with a sample size of O(dlogd + d/ε).
In this paper we address the problem of pool based active learning, and provide an algorithm, called UPAL, that works by minimizing the unbiased estimator of the risk of a hypothesis in a given hypothesis space. For the space of linear classifiers and the squared loss we show that UPAL is equivalent to an exponentially…
Volume sampling technique improves linear regression predictions.
problem Predict hidden response values for a subset of input points in linear regression.
method Reverse iterative volume sampling for constructing linear predictions.
result Volume sampled subproblem yields an unbiased estimator of the optimal solution.
New ATD methods improve TD learning efficiency and stability.
problem Improving TD learning efficiency and stability.
method Accelerated Gradient Temporal Difference (ATD) methods.
result ATD methods provide similar data efficiency to least squares methods with reduced computation and storage requirements and improved parameter sensitivity.
We consider the question of learning in general topological vector spaces. By exploiting known (or parametrized) covariance structures, our Main Theorem demonstrates that any continuous linear map corresponds to a certain isomorphism of embedded Hilbert spaces. By inverting this isomorphism and extending continuously, …
Quantum algorithm speeds up Gibbs partition function estimation.
problem Estimating partition functions in sublinear time.
method Sublinear-time quantum algorithm using quantum phase and amplitude estimation.
result First sublinear-time speed-up for partition function estimation.