Stochastic version of proximal distance algorithm analyzed and validated.
problem Optimization of constrained estimation problems.
method Stochastic proximal distance algorithm, with convergence guarantees and finite error bounds.
result Convergence guarantees and finite error bounds for the first time.
Improves time series classification with forest proximities.
problem Time series classification accuracy and efficiency.
method PF-GAP, an extension of RF-GAP proximities to proximity forests, combined with Multi-Dimensional Scaling and Local Outlier Factors.
result Forest proximities show stronger connection between misclassified points and outliers.
Extends RF proximities to all supervised distance-based machine learning contexts.
problem Limited utility of RF proximities in various machine learning tasks.
method Introduces generalized Proximity Forest (PF) model and variant for regression.
result Demonstrates unique advantages over RF and k-nearest neighbors models.
Proposes a new metric learning method for image recognition.
problem Improving image recognition performance using learned distance representations.
method Introduces a Generalized Hybrid Metric Loss (GHM-Loss) to learn hybrid proximity features combining geometric and probabilistic spaces.
result Demonstrates superior performance compared to existing methods on public datasets.
Random Forest proximity distances reveal feature contributions in black-box models.
problem Understanding feature contributions in complex, opaque machine learning models.
method Observing changes in input affecting proximity distances and instance movement in decision space.
result Each feature's independent contribution to model decisions can be calculated and analyzed.
Method uses NMF for clustering with partial distance measurements.
problem Proximity clustering with partial distance measurements.
method Nyström approximation with Nonnegative Matrix Factorization.
result Find nearly optimal clustering quality on synthetic and real-world data.
Proposes a Riemannian optimization for policy improvement in MDPs.
problem Optimizing policy functions in Markov decision processes (MDPs).
method Riemannian proximal optimization algorithm with Gaussian mixture model (GMM).
result Guaranteed convergence and efficacy demonstrated in preliminary experiments.
Wasserstein distance plays increasingly important roles in machine learning, stochastic programming and image processing. Major efforts have been under way to address its high computational complexity, some leading to approximate or regularized variations such as Sinkhorn distance. However, as we will demonstrate, regu…
Paper uses UKS to improve BLE RSSI for proximity inference in mobile phone apps.
problem Improper BLE RSSI for accurate proximity inference during pandemics.
method Single-dimensional Unscented Kalman Smoother (UKS) with Gaussian process observation transforms.
result UKS outperforms traditional methods in predicting infection risk from BLE RSSI.
New algorithm samples from log concave distributions efficiently.
problem Sampling from log concave distributions efficiently.
method Stochastic Proximal Langevin Algorithm (SPLA) with potential splitting.
result Established nonasymptotic convergence rates for SPLA.
CFR-Pro enhances treatment effect estimation by incorporating local proximity.
problem Treatment selection bias in HTE estimation from observational data.
method Proximity-enhanced CounterFactual Regression (CFR-Pro) with pair-wise proximity regularizer and subspace projector.
result Significantly outperforms competitors in HTE estimation accuracy.
New adapted Wasserstein distance improves financial model accuracy.
problem Financial models may not accurately reflect reality.
method Introduced adapted Wasserstein distance to measure model proximity.
result Hedging strategies can be more stable and meaningful.
Complex embeddings handle non-metric proximity data better than traditional methods.
problem Proximities not always metric or inner product-based, causing convergence issues.
method Proposes complex-valued embeddings for non-vectorial data.
result Complex embeddings outperform traditional techniques on benchmarks.
Study on volumes of quasifuchsian manifolds, focusing on similarities and proximities.
problem Understanding the relationship between renormalized volume and dual volume of quasifuchsian manifolds.
method Analyzing similarities and proximities between renormalized volume and dual volume, using variational formulas and Weil-Petersson distance.
result Renormalized volume and dual volume are closely related, with bounded distance between related objects.
DE-PSGLD samples from constrained distributions in a decentralized manner.
problem Sampling from log-concave distributions with constraints.
method Decentralized Proximal Stochastic Gradient Langevin Dynamics with proximal regularization.
result DE-PSGLD converges to a regularized Gibbs distribution and maintains posterior concentration.
Introduces a new divergence measure for optimal transport.
problem Optimal transport distances and information divergences.
method Infimal convolution formulation of proximal optimal transport divergence.
result Establishes connections to dynamic formulations and partial differential equations.
Paper proposes a new method for supervised manifold learning using random forest proximities.
problem Existing supervised manifold learning methods fail to uncover meaningful embeddings due to using class-conditional distances.
method Proposes a data-geometry-preserving variant of random forest proximities as an initialization for manifold learning methods.
result Local and global structure preservation is near universal across manifold learning approaches using diffusion-based algorithms.
Neural networks can learn distance metrics affecting model performance.
problem Understanding how neural networks learn and represent data.
method Experiments with six MNIST architectures, constrained to learn either distance or intensity representations.
result Distance-based learning affects model performance, validating the geometric framework.
POP3D is a new reinforcement learning algorithm that improves upon PPO.
problem The shortcomings of existing reinforcement learning algorithms.
method Policy Optimization with Penalized Point Probability Distance (POP3D) as a lower bound to the square of total variance divergence.
result POP3D is highly competitive compared to PPO in various benchmarks.
New method improves matrix factorization speed and accuracy.
problem Matrix factorization optimization problems suffer from biased solutions and lack of convergence guarantees.
method Proposes a novel Bregman distance for matrix factorization, enabling non-alternating schemes with convergence proof.
result Convergence to a stationary point proved for matrix factorization problems.
Dynamic time warping applied to temporal graphs for pattern recognition.
problem Comparing temporal graphs using a proximity measure.
method Dynamic Time Warping (DTGW) on temporal graphs.
result DTGW is a flexible measure for temporal graph comparison, NP-hard in general but with polynomial-time solvable special cases.
The paper improves proximity estimates for hypersurfaces with almost constant curvature in space forms.
problem Proximity to a single sphere for hypersurfaces with curvature functions close to a constant.
method Unified approach using the method of moving planes.
result Sharp quantitative estimates of proximity to a single sphere.
WDAIL uses Wasserstein distance for more effective reward shaping in IL.
problem Fixed reward functions in GAIL limit performance on complex tasks.
method Introduces Wasserstein distance and PPO for improved reward shaping and stability.
result Significant performance improvement in complex MuJoCo tasks.
Proximity Forest classifies time series in milliseconds from large datasets.
problem Classifying time series from large datasets with high accuracy and speed.
method Ensemble of randomized Proximity Trees, leveraging proximity measures instead of attribute values.
result Proximity Forest achieves high accuracy on large datasets and is significantly faster than state-of-the-art models.
We introduce a proximal subdifferential and develop a calculus for nonsmooth functions defined on any Riemannian manifold M M M . We give several applications of this theory, concerning: 1) differentiability and geometrical properties of the distance function to a closed subset C C C of M M M ; 2) solvability and implicit func…
Develops minibatch stochastic proximal gradient for large-scale learning models.
problem Finding optimal predictors with complex regularizers in large-scale learning models.
method Minibatch variants of stochastic proximal gradient algorithm for composite objective functions.
result Minibatch size N N N after O ( 1 N ε ) \mathcal{O}(\frac{1}{Nε}) O ( N ε 1 ) iterations achieves ε − ε- ε − suboptimality in expected quadratic distance. Unified four trade-off curves for assessing generative model proximity.
problem Quantitative assessment of proximity between two probability distributions.
method Unified four existing curves: PR, Lorenz, ROC, and Rényi divergence frontiers.
result Explicit relationship between PR and Lorenz curves with domain adaptation bounds.
New algorithm improves convergence rates for convex optimization problems.
problem Convex optimization problems with noisy stochastic data.
method Stochastic proximal point algorithm with weak linear regularity condition.
result Achieves $\mathcal{O}\left(\frac{1}{k}
ight)$ convergence rate for SPP.
This paper presents a general notion of Mahalanobis distance for functional data that extends the classical multivariate concept to situations where the observed data are points belonging to curves generated by a stochastic process. More precisely, a new semi-distance for functional observations that generalize the usu…
A characterization of the proximal normal cone is obtained and a separation theorem for convex subsets of Riemannian manifolds is established. Moreover, the convexity of the distance function d S d_S d S for a convex subset S S S in the cases where the boundary of S S S contains a geodesic segment, the boundary of S S S is C 2 C^2 C 2 o…
Quantile regression using random forest proximities improves prediction and uncertainty quantification.
problem Forecasting corporate bond volume with uncertainty quantification.
method Introduced a novel approach to compute quantile regressions from random forests using proximity metrics.
result Superior performance in approximating conditional target distributions and prediction intervals.
A new algorithm solves signed Fréchet regression on manifolds with bounded curvature.
problem Signed Fréchet regression on Riemannian manifolds with bounded curvature.
method Proximal DC algorithm (FRIDA) for computing signed Fréchet regression fits.
result Existence and interiority of minimizers, strong convexity of proximal subproblems, and convergence to stationary points.
A new optimization method, BPM, converges linearly in non-convex, non-smooth problems.
problem Non-smooth and non-convex optimization challenges.
method Ball-Proximal Point Method (BPM), inspired by Proximal Point Method (PPM).
result BPM converges linearly and in a finite number of steps in non-convex, non-smooth problems.
New algorithm speeds up sampling from complex distributions.
problem Efficiently sampling from non-log-concave distributions.
method Stochastic Proximal Samplers (SPS) based on SGLD and MALA.
result SPS-SGLD and SPS-MALA achieve faster sampling with reduced gradient complexity.
This paper explores a new framework for reinforcement learning based on online convex optimization, in particular mirror descent and related algorithms. Mirror descent can be viewed as an enhanced gradient method, particularly suited to minimization of convex functions in highdimensional spaces. Unlike traditional grad…
WAPPO optimizes feature distributions for better visual transfer in RL.
problem Improving visual transfer in reinforcement learning.
method WAPPO uses Wasserstein Confusion to minimize feature distribution distance.
result WAPPO outperforms previous methods in visual transfer across different environments.
Unified framework for lifted training and inversion of neural networks.
problem Challenges in gradient-based training of deep neural networks.
method Unified framework encapsulating various lifted training strategies.
result Unified framework improves training landscape and stability.
Algorithm samples from composite log-concave distributions using gradient evaluations and restricted Gaussian oracles.
problem Sampling from composite log-concave distributions with limited gradient evaluations.
method Proximal gradient algorithm with RGO for g g g and strong/strongly convex conditions for f f f . result Achieves ε ε ε error in total variation distance in O ~ ( κ d log 4 ( 1 / ε ) ) \widetilde{\mathcal O}(κ\sqrt d \log^4(1/ε)) O ( κ d log 4 ( 1/ ε )) iterations. Robust GW distance improves graph data alignment.
problem Outliers in GW distance lead to inaccurate comparisons.
method Optimistically perturbed marginal constraints within a Kullback-Leibler divergence-based ambiguity set.
result RGW reduces inaccuracies in graph data alignment.
The structure of return spillovers is examined by constructing Granger causality networks using daily closing prices of 20 developed markets from 2nd January 2006 to 31st December 2013. The data is properly aligned to take into account non-synchronous trading effects. The study of the resulting networks of over 94 sub-…
Paper proposes a supervised similarity framework for corporate bonds using RF proximities.
problem Challenges in measuring similarity for corporate bonds due to noisy data and lack of ground truth.
method Proposes a supervised similarity framework using Random Forest for corporate bonds, introducing a novel metric to evaluate similarities.
result Random Forest outperforms other methods in evaluating similarities for corporate bonds.
Study shows effective resistance distance yields more accurate network barycenter than Hamming distance.
problem Identifying the best metric for computing the Fréchet mean network.
method Compared the effectiveness of Hamming distance and effective resistance distance in capturing network topology.
result Effective resistance distance produces a more accurate Fréchet mean network.
Unified pipeline classifies time series using complex networks and persistent homology.
problem Classifying univariate time series using various graph constructions and metrics.
method Time series to graph, graph to dissimilarity matrix, filtration to persistence diagrams, vectorization to features.
result Persistence-based features are robust to noise and optimal graph type depends on signal structure.
Corpus poisoning can manipulate word meanings in word embeddings, affecting natural language processing tasks.
problem Controlling word meanings via corpus modifications.
method Developed an explicit expression over corpus features to control word embeddings.
result Demonstrated the ability to manipulate word meanings in word embeddings, affecting various downstream tasks.
New analysis for learning and applying preconditioners in MCMC improves efficiency.
problem Improving efficiency of MCMC algorithms by modifying them with preconditioners.
method Analyzes and compares computational costs of MCMC schemes with and without preconditioners.
result Establishes non-asymptotic guarantees for MCMC algorithms that learn and use preconditioners.
Efficiently augments triplet data for better data analytics.
problem Lack of direct pairwise distance information for data analysis.
method Triplets augmentation to infer hidden information from existing data.
result Improves quality of kernel-based and kernel-free data analytics.
New methods solve non-Lipschitz smooth problems with guaranteed convergence.
problem Non-Lipschitz smooth problems in machine learning and signal processing.
method Bregman-divergence based algorithms for relatively smooth problems.
result Guaranteed convergence to second-order stationary points for any relatively smooth problem.
ALP outperforms other data descriptors in one-class classification.
problem Challenges in one-class classification using data descriptors.
method Determined optimal default hyperparameters for data descriptors, proposed ALP, evaluated using leave-one-dataset-out procedure.
result ALP outperforms other data descriptors, including IF and SVM.