FEALM learns features for better nonlinear DR of hidden patterns.
problem DR misses important patterns on distorted manifolds.
method FEALM generates optimized projections using an optimization algorithm and neighbor-shape dissimilarity.
result FEALM captures important patterns on hidden manifolds.
DR-MCTS improves decision quality and sample efficiency in complex environments.
problem Improving decision quality and sample efficiency in complex environments.
method Integrates Doubly Robust off-policy estimation into Monte Carlo Tree Search (MCTS).
result DR-MCTS achieves superior performance in Tic-Tac-Toe and VirtualHome tasks.
Paper analyzes mathematical theory behind out-of-sample DR extensions.
problem Developing a solid mathematical foundation for out-of-sample DR extensions.
method Utilizes RKHS theory to treat DR extension as an extension of the identity on RKHS defined on X.
result Shows Nyström-type DR extension as an orthogonal projection and provides conditions for exact DR extension.
New DR-IC estimator reduces bias and variance in OPE.
problem Estimating value of a target policy using logged data from a different policy.
method DR-IC estimator that combines parametric reward model and context-based switching rule.
result DR-IC estimator outperforms state-of-the-art OPE algorithms.
Paper defines and evaluates DR complex for persistent homology.
problem Computing persistent homology of Euclidean point cloud data.
method Delaunay-Rips complex construction for speed and stability.
result DR produces stable persistence diagrams under point cloud perturbations.
New algorithms solve DR-submodular maximization with faster convergence.
problem Maximizing monotone DR-submodular functions under convex constraints.
method Introduced strongly DR-submodular functions and proposed SDRFW and PGA algorithms.
result SDRFW achieves optimal approximation ratio after fewer iterations.
DR technique helps deep models learn faster and better.
problem Vanishing gradients and local minima in deep model training.
method DR technique applies penalties on hidden units to improve learning.
result DR improves convergence and generalization in deep neural networks.
Paper tackles non-monotone DR-submodular maximization with approximation and regret guarantees.
problem Maximizing non-monotone DR-submodular functions over specific sets.
method Frank-Wolfe algorithm for general convex sets, Stochastic Gradient Ascent for down-closed convex sets.
result First approximation guarantees for both offline and online settings.
Online and stochastic learning has emerged as powerful tool in large scale optimization. In this work, we generalize the Douglas-Rachford splitting (DRs) method for minimizing composite functions to online and stochastic settings (to our best knowledge this is the first time DRs been generalized to sequential version).…
Dimensionality reduction (DR) is often used as a preprocessing step in classification, but usually one first fixes the DR mapping, possibly using label information, and then learns a classifier (a filter approach). Best performance would be obtained by optimizing the classification error jointly over DR mapping and cla…
Neural networks struggle with identity relations; DR units improve generalization.
problem Neural networks fail to generalize identity relations.
method Exploring various factors in neural network architecture and learning process, including number of hidden layers, activation function, and data representation.
result DR units improve generalization, leading to almost perfect test accuracy in mid fusion setting.
Paper tackles online DR-submodular maximization with various convex sets.
problem Maximizing DR-submodular functions online over different convex sets.
method Develops online algorithms with approximation guarantees for various convex sets.
result Achieves 1/e-approximation ratio with O(T2/3) regret for down-closed sets. New DR method improves robustness in high-dimensional treatment effects.
problem Estimating dynamic treatment effects with high-dimensional confounders.
method Proposes a novel DR representation for intermediate conditional outcome models.
result Achieves superior robustness guarantees with high-dimensional confounders.
Two algorithms maximize DR-submodular functions under convex constraints.
problem Maximizing non-monotone DR-submodular functions under convex constraints.
method Developed two algorithms with provable guarantees: a two-phase algorithm with 1/4 approximation and a Frank-Wolfe variant with 1/e approximation.
result Proved strong relation between stationary points and global optimum for DR-submodular functions.
DR-NMF uses unfolded ISTA for speech separation, offering interpretability and speed.
problem Speech separation in noisy environments.
method DR-NMF is a recurrent neural network that unfolds ISTA iterations for NMF of spectrograms.
result DR-NMF outperforms NMF and LSTM networks in speech separation.
Interactive DR framework for comparing datasets.
problem Limited flexibility in existing DR methods for comparative analysis.
method Unified linear comparative analysis (ULCA) with interactive optimization and visualization.
result ULCA and optimization algorithm improve comparative analysis efficiency and flexibility.
New model-free DR-RL algorithm with finite sample complexity.
problem Limited model-free DR-RL methods with convergence guarantees or sample complexities.
method Integrates Multi-level Monte Carlo (MLMC) technique with threshold mechanism.
result First model-free DR-RL approach with finite sample complexity for total variation and Chi-square divergence.
The paper studies continuous submodular functions and their optimization.
problem Maximizing continuous submodular functions in poly. time.
method Characterization of continuous submodularity, operations preserving it, and algorithms for constrained maximization.
result Continuous submodularity is equivalent to a weak DR property, leading to continuous DR-submodular functions with the full DR property.
A new DR formulation improves metric learning for faster and more stable performance.
problem Learning embeddings for class separation in metric learning.
method Distance-ratio (DR) formulation for metric learning.
result DR formulation achieves improved or comparable generalization performances.
Paper evaluates competence measures for DRS systems.
problem Choosing the best measure to quantify competence in DRS systems is challenging.
method Reviewed and adapted eight competence measures for regression problems, compared them on 15 datasets, and evaluated three DRS systems.
result DRS systems outperform individual regressors and static systems, but competence measure choice depends on the problem.
Paper tackles online learning for DR management with incentives.
problem Estimating baseline consumption in DR programs with consumer incentives.
method Online learning scheme using least-squares with perturbed reward prices.
result Achieves low regret of $\mathcal{O}\left((\log{T})^2
ight)$ compared to optimal.
Paper analyzes an algorithm for maximizing non-concave functions with budget constraints.
problem Maximizing non-concave functions with budget constraints under DR-submodularity.
method Generalized Sequential algorithm for online monotone DR-submodular function maximization.
result First competitive ratio bound matches known tight bound for linear objective functions.
New DR algorithm preserves both local and global structure.
problem Trade-off between preserving local and global structure in DR methods.
method Analysis of existing DR methods and design principles for loss functions.
result Design of PaCMAP algorithm that preserves both local and global structure.
New dual formulation reduces generalization error for ERM-fDR.
problem Generalization error in constrained optimization problems.
method Introduces a dual formulation of ERM-fDR using Legendre-Fenchel transform and implicit function theorem.
result Explicit characterizations of generalization error for algorithms under mild conditions.
We study the behaviour of analytic torsion under smooth fibrations. Namely, let F \to E \to^{f} B be a smooth fiber bundle of connected closed oriented smooth manifolds and let V be a flat vector bundle over E. Assume that E and B come with Riemannian metrics and V comes with a unimodular (not necessarily fla…
The paper shows DR maps can't be perfect in information retrieval.
problem The limitations of DR maps in achieving perfect precision and recall.
method Quantitative topology approach, proving precision bounds, introducing Wasserstein distance.
result Continuous DR maps must have imperfect precision, and a new precision measure based on Wasserstein distance is proposed.
This research applies fuzzy clustering to reduce high-dimensional text data.
problem High-dimensional sparse vectors in bag-of-words matrices.
method Fuzzy clustering as a DR method based on Unsupervised Feature Transformation (UFT).
result Fuzzy clustering outperforms PCA and SVD in reducing high-dimensional text data.
ProbDR framework interprets DR algorithms as probabilistic inference.
problem Efficiently compressing high-dimensional data into lower dimensions.
method ProbDR variational framework that treats DR as probabilistic inference.
result ProbDR unifies various DR algorithms and enables probabilistic reasoning.
DRSS method identifies unnecessary samples and features in DR covariate shift.
problem Identifying unnecessary samples and features in DR covariate shift.
method Combines DR learning and safe screening techniques.
result DRSS method provides reliable identification of unnecessary samples and features under specified distribution uncertainty.
The paper proposes a new DR model to better predict EUCs' responses in real-time pricing.
problem Static demand functions fail to capture temporal correlation in EUC behaviors.
method Proposes a dynamical DR model using neural networks to learn from historical data.
result The dynamical DR model significantly outperforms static models in predicting EUC responses.
Adapts DR objectives for both sample and feature size reduction.
problem Simultaneously reduce sample and feature sizes.
method Semi-relaxed Gromov-Wasserstein optimal transport.
result OT plan delivers competitive hard clustering.
This paper establishes non-asymptotic learning bounds for the DR covariate shift adaptation.
problem Distribution shift between training and test domains in machine learning.
method Doubly-robust (DR) estimator combining density ratio estimation and pilot regression model.
result First non-asymptotic learning bounds for DR covariate shift adaptation.
Regression aims at estimating the conditional mean of output given input. However, regression is not informative enough if the conditional density is multimodal, heteroscedastic, and asymmetric. In such a case, estimating the conditional density itself is preferable, but conditional density estimation (CDE) is challeng…
New algorithm improves mean field inference in probabilistic models.
problem Improving mean field inference in probabilistic models.
method DR-DoubleGreedy algorithm for continuous DR-submodular maximization with box-constraints.
result Achieves optimal 1/2 approximation ratio for continuous DR-submodular maximization.
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.
A new SL strategy optimizes HVAC DR in multi-zone buildings.
problem Optimal DR of HVAC units in multi-zone buildings is challenging.
method Supervised learning with ANN replication and DNN integration.
result SLAMP achieves effective DR schedules with reduced computation time.
Paper proposes methods to reduce bias and variance in recommender systems.
problem Bias in recommender systems due to users' preferences.
method Proposes a principled approach to reduce bias and variance in DR methods, and a novel semi-parametric collaborative learning approach.
result The proposed methods outperform existing debiasing methods in both theory and experiments.
This paper explores autoencoders for estimating intrinsic dimensionality.
problem Estimating the intrinsic dimensionality of random vectors.
method Use of autoencoders for dimension estimation, focusing on architectural choices and regularization techniques.
result Autoencoders can be adapted for intrinsic dimension estimation, addressing questions beyond classic DR/DE techniques.
A new algorithm interprets DR dimensions and selects features.
problem Lack of interpretability in DR algorithms.
method I-KDR algorithm that maps data to a lower dimensional space with interpretable dimensions and feature selection.
result I-KDR provides better interpretations and higher discriminative performance.
New method tackles online DR-submodular maximization with improved regret guarantees.
problem Online maximization of non-monotone DR-submodular functions over down-closed convex sets.
method 1/e-linearization through exponential reparametrization, surrogate potential, and reduction to online linear optimization.
result Achieves O(T1/2) static regret with single gradient query per round, improving state of the art. Paper presents ERM with f-divergence regularization and its properties.
problem Minimizing empirical risk with f-divergence constraints. method Introduces normalization function and solves ERM-fDR via ODE. result Characterizes difference between empirical risks and provides numerical algorithm.
Proposes DR-ACI for causal effect intervals with temporal dependence.
problem Causal effect intervals under temporal dependence.
method Doubly robust adaptive conformal inference (DR-ACI).
result Constructs prediction intervals for causal effects.
Let (M,∂M) be a compact 3-manifold with boundary which admits a complete, convex co-compact hyperbolic metric. For each hyperbolic metric g on M such that $\dr M$ is smooth and strictly convex, the induced metric on $\dr M$ has curvature K>−1, and each such metric on $\dr M$ is obtained for a unique ch…
Paper introduces a new framework for optimizing non-convex functions.
problem Optimizing non-convex functions, especially DR-submodular and concave functions.
method Developed a general meta-algorithm to convert linear/quadratic optimization to optimization of upper-linearizable/quadratizable functions.
result Unified approach to concave and DR-submodular optimization problems.
Proposes a robust estimator for RD designs.
problem Estimating treatment effects in RD designs.
method Doubly robust estimator combining two estimators.
result Enhances robustness of treatment effect estimators.
A new DR method for HSI classification improves accuracy with limited samples.
problem Challenges in DR for HSI classification with limited training samples.
method Graph-based spatial and spectral regularized local scaling cut (SSRLSC).
result Improved classification accuracy compared to spectral-only methods.
Paper proposes a new DR estimator for adaptive experiments with improved performance.
problem Improving policy evaluation in adaptive experiments with dependent samples.
method Adaptive-fitting variant of sample-splitting for non-Donsker nuisance estimators.
result Proposed DR estimator shows better performance than other estimators with dependent samples.
Paper introduces robust distribution regression using kernel methods.
problem Distribution regression from probability measures to real-valued responses.
method Introduces a robust loss function lσ and a windowing function V for two-stage sampling problems. result Shows improved learning rates and robustness with the robust distribution regression (RDR) scheme.