The paper tackles partial inference in structured prediction using a convex optimization approach.
problem Maximizing a score function with unary and pairwise potentials in graph label spaces.
method Generative model approach with two-stage convex optimization for label recovery.
result Conditions for recovering a majority of labels with provable guarantees.
Study improves model robustness in noisy datasets.
problem Instance-specific label noise in robust classification tasks.
method Coordinated Sparse Recovery (CSR) method introduces a collaboration matrix and confidence weights to reduce generalization error.
result CSR and CSR+ significantly reduce generalization error compared to existing methods.
Crowdsourced labeling recovers task types with minimal queries.
problem Labeling tasks accurately with minimal queries.
method Worker clustering, skill estimation, weighted majority voting.
result Achieves any targeted recovery accuracy with minimum queries.
Study finds the cutoff for exact recovery in Gaussian mixture models.
problem Determining the separation of cluster centers for exact recovery in Gaussian mixture models.
method Used information theory and SDP relaxation of K-means clustering. result Sharp threshold for exact recovery of cluster labels without assuming cluster center symmetry.
Detecting and recovering labels in binomial logistic mixtures is challenging due to an information gap.
problem Detecting and recovering labels in binomial logistic mixtures
method Propose two feasibility-aware inference procedures
result Avoid misleading component selections and improve label probability calibration
Paper tackles sparse recovery with shuffled labels, establishing statistical and computational limits.
problem Sparse recovery with shuffled labels, focusing on permutation matrix and sparse signal reconstruction.
method Statistical and computational analysis, including minimax lower bounds and exhaustive-search based estimator.
result Established statistical and computational limits for correct recovery of permutation matrix and support set.
A new method improves graph-based semi-supervised classification by removing noise and mixed signs.
problem Inaccurate soft labels and noise in graph-based semi-supervised classification.
method Triple-matrix-recovery-based robust auto-weighted label propagation framework (ALP-TMR).
result Improved robustness to noise and outliers in label estimation.
A new framework CL embeds features and labels for multi-label classification.
problem Exponential growth of output space in multi-label classification.
method Compact Learning (CL) framework that embeds features and labels simultaneously.
result CMLL maximizes label-feature dependency and minimizes label space loss.
A non-convex algorithm recovers low-rank matrices from one-bit labels efficiently.
problem Learning with one-bit labels in multi-label scenarios.
method Formulated as one-bit rank-one matrix sensing, developed an alternating power iteration algorithm.
result Achieves linear convergence and nearly optimal sampling complexity.
Proposes methods to recover labels from shuffled networks using graph averages.
problem Recovering labels from a shuffled network using graph averages.
method Cluster networks into classes, then match the new graph to cluster-averages, minimizing the graph matching objective function.
result Higher fidelity matching performance when clustering networks into different classes.
The study examines how side information quality and quantity affect community recovery in graphs.
problem Recovering a hidden community of size K=o(n) in a graph of size n. method Maximum likelihood detection and belief propagation are used to calculate necessary and sufficient conditions for exact and weak recovery. A local voting procedure is also designed and analyzed.
result Tight necessary and sufficient conditions for exact and weak recovery are derived, showing how side information needs to evolve with n to improve recovery thresholds. A framework for discrete structure recovery using iterative algorithms.
problem Recovering various discrete structures from data.
method General iterative algorithm for discrete structure recovery.
result Linear convergence of the proposed algorithm under certain conditions.
Study on ReLU regression with Massart noise, achieving exact parameter recovery.
problem Efficiently fitting ReLUs to data in the presence of Massart noise.
method Developed an efficient algorithm for exact parameter recovery under mild assumptions.
result Achieved exact parameter recovery in ReLU regression with Massart noise.
The paper shows how label noise in training can lead to solutions that solve a Lasso program.
problem Understanding the implicit bias of training algorithms in overparametrised models.
method Analyzing the continuous time version of the training dynamics of a quadratically parametrised model.
result The stochastic flow implicitly solves a Lasso program, providing convergence guarantees and support recovery conditions.
Paper models graph edge dependencies using latent variables for community detection.
problem Graphs' edge dependencies not fully explained by community membership.
method Introduces auxiliary latent variables to model edge dependencies and analyzes conditions for exact recovery.
result Exact recovery possible by semidefinite programming down to maximum likelihood threshold.
New algorithm improves community detection in partially labeled SBM models.
problem Community detection in partially labeled stochastic block models.
method Developed a fast linearized message-passing algorithm.
result Exponential improvement in error rate for strong recovery.
Paper uses SDP for community detection with side information.
problem Community detection in graphs with additional non-graph data.
method Formulates SDP relaxation for maximum likelihood node labeling with side information.
result SDP achieves same exact recovery threshold as maximum likelihood with side information.
Develops a semidefinite program for various blockmodel instances.
problem Community detection and blockmodel instances.
method Semidefinite programming tailored to different blockmodel types.
result Establishes label recovery conditions and excess risk bounds.
Local graph clustering improves with noisy labels, enhancing accuracy and performance.
problem Local graph clustering with noisy labels for node information.
method Constructing a weighted graph with noisy labels and using diffusion-based clustering.
result Diffusion in the weighted graph yields more accurate recovery of target clusters.
Fairness constraints improve exact recovery in structured prediction models.
problem Exact recovery of fair binary node labels from noisy observations.
method Analyzed Globerson et al. (2015) model with fairness constraints and improved exact recovery for graphs with poor expansion properties.
result Fairness constraints improve the probability of exact recovery from noisy observations.
Exact inference in structured prediction for various graphs.
problem Exact recovery of labels in structured prediction models.
method Analysis of graph structures and application of Cheeger's inequality.
result Exact recovery is possible and achievable in polynomial time for a large class of graphs.
New algorithm IAC recovers hidden communities in labeled SBM with optimal performance.
problem Recovering hidden communities in Labeled Stochastic Block Model with varying cluster sizes.
method IAC (Instance-Adaptive Clustering) algorithm, consisting of spectral clustering and iterative likelihood-based improvements.
result IAC achieves optimal performance matching instance-specific lower bounds in expectation and with high probability.
Study optimal spectral estimator for semi-supervised node classification.
problem Semi-supervised node classification on CSBM with limited labels.
method Spectral estimator inspired by PCA, graph ridge regression, GCN.
result Achieves information-theoretical threshold for exact recovery.
New model for community detection with side information improves recovery accuracy.
problem Community detection in networks with additional node data.
method Data Block Model (DBM) with Chernoff--TV divergence for threshold characterization and efficient algorithm.
result Sharp exact recovery threshold and efficient algorithm for DBM.
This paper tackles exact recovery of clusters in a stochastic Ising model on a SBM graph.
problem Recovering clusters in a stochastic Ising model on a SBM graph.
method Proposes a Stochastic Ising Block Model (SIBM) and establishes a sharp threshold for exact recovery.
result Sharp threshold m∗ for exact recovery of clusters in SIBM, with O(n) time complexity for m≥m∗. Unified theory explains housing cycle across metros, showing credit expansion impacts.
problem Puzzling correlations between income and mortgage growth across ZIP codes and metros.
method Unified credit expansion theory, double differences, instrumental variables.
result Credit expansion drives housing cycle, affecting boom, bust, and recovery phases.
Efficient algorithms recover two sparse models from a mix of linear queries.
problem Recovering two sparse models from a mix of linear queries.
method Efficient algorithms for query complexity problem.
result Improved query complexity for model recovery.
Paper corrects deep learning for noisy labels.
problem Overfitting to imperfectly labeled data.
method Distribution correction approach to handle noisy inputs.
result Significantly higher accuracy compared to alternative methods.
Transfer learning improves loan recovery rate forecasting under data scarcity.
problem Data scarcity in loan portfolios limits RR modeling accuracy.
method Introduces FT-MDN-Transformer, a mixture-density tabular Transformer architecture for TL.
result FT-MDN-Transformer outperforms baseline models in RR forecasting, especially under covariate and conditional shifts.
The paper addresses bias in fraud detection models by improving label recovery in payment networks.
problem Systematic bias in chargeback labels in payment networks.
method Formalizes the observation pipeline as a sequential missing-data problem with three stages and a corruption layer. Constructs the Sequential Triply Robust (STR) estimator to correct for all four impairments simultaneously.
result Achieves the semiparametric efficiency bound and provably dominates naive chargeback-based training in mean squared error.
Method uses TV minimization for semi-supervised learning on network data.
problem Semi-supervised learning from partially-labeled network data.
method Graph signal recovery interpretation, total variation minimization, primal-dual method for non-smooth convex optimization.
result TV minimization recovers clusters in the empirical graph of the data under certain network conditions.
Sharp threshold for exact recovery in non-uniform hypergraph stochastic block model.
problem Community detection in random hypergraphs with non-uniform hyperedge probabilities.
method Sharp threshold established; two efficient algorithms for exact recovery.
result Sharp threshold for exact recovery; information-theoretic lower bound on misclassification.
Contrastive learning outperforms autoencoders and GANs in feature recovery and downstream tasks.
problem Theoretical understanding of contrastive learning's superiority in feature learning.
method Theoretical analysis of contrastive learning in linear representation settings.
result Contrastive learning outperforms autoencoders and GANs for feature recovery and in-domain downstream tasks.
Paper analyzes classical multidimensional scaling for cluster recovery.
problem Cluster recovery from noisy data.
method Classical multidimensional scaling followed by distance-based clustering.
result Scaling conditions for high probability cluster recovery.
We analyze the local Rademacher complexity of empirical risk minimization (ERM)-based multi-label learning algorithms, and in doing so propose a new algorithm for multi-label learning. Rather than using the trace norm to regularize the multi-label predictor, we instead minimize the tail sum of the singular values of th…
A neural network detects anomalies without labels by identifying the underlying subspace.
problem Unsupervised anomaly detection in data.
method Robust Subspace Recovery (RSR) layer within an autoencoder.
result RSR layer effectively distinguishes inliers from outliers in latent space.
This work tackles community detection in networks with node attributes, achieving exact recovery.
problem Community detection in networks with correlated node attributes.
method Information-theoretic criterion and iterative clustering algorithm maximizing joint likelihood.
result Exact recovery of community labels under a general model for network and node attributes.
Study community detection in multi-view data with various types of information.
problem Community detection in multi-view data with different types of information.
method Unified theoretical framework, mutual information analysis, sharp thresholds, iterative algorithms.
result Sharp thresholds for community recovery in various multi-view settings.
Study how noisy labels affect semi-supervised learning.
problem Effect of noisy labels on semi-supervised learning performance.
method Proposed an algorithm derived from a continuous relaxation of the Maximum A Posteriori (MAP) estimator for a Degree Corrected Stochastic Block Model (DC-SBM).
result Our approach achieves promising performance even with very noisy labeled data.
Study of active learning in geometric block model for community detection.
problem Active learning for community detection in geometric block model.
method Proposed two active learning algorithms combining motif-counting with label query policies.
result Sampling labels of a vanishingly small fraction of nodes is sufficient for exact recovery.
Paper proposes a method to recover accurate labels from partially valid data in multi-label learning.
problem Tackles noisy supervision in multi-label learning with partially valid labels.
method Develops a two-stage method that estimates label enrichment and ground-truth confidences.
result Demonstrates improved performance over state-of-the-art PML methods.
ReLU networks learn simple models even with many parameters, overcoming traditional wisdom.
problem Generalization of overparameterized neural networks.
method Convex optimization and sparse recovery perspective applied to two-layer ReLU networks with standard weight decay.
result ReLU networks learn simple models that explain the data, analogous to sparse recovery in compressed sensing.
New algorithm trains ReLU gates provably in linear time.
problem Training ReLU gates in realizable settings with mild conditions.
method Iterative stochastic algorithm with moment assumptions.
result First recovery of true labels under data-poisoning attacks.
Paper proposes efficient methods for clustering and signal recovery in high-dimensional data with block structures.
problem High-dimensional clustering and signal recovery under block signal structures.
method CFA-PCA and MA-PCA methods for sparse and dense block signals.
result Proposed methods achieve computational minimax optimality for clustering and signal recovery.
A new method fills missing labels in multi-label classification problems.
problem Missing feature and label values in multi-label classification.
method Proposes co-completion (COCO) algorithm based on subgradient descent.
result Demonstrates theoretical and practical effectiveness of COCO.
DM2L tackles missing labels in multi-label learning by modeling local and global rank structures.
problem Missing labels in multi-label learning.
method DM2L imposes local low-rank structures and global high-rank structures on predictions of instances from the same and different labels, respectively.
result DM2L outperforms state-of-the-art methods in multi-label learning with missing labels.
Study on identifying labels in pooled tests with noise and errors.
problem Identifying labels in pooled tests with noise and errors.
method Exact asymptotic threshold and information-theoretic framework for noisy and noisy-noise models.
result Noise can significantly increase the difficulty of the problem, even at low levels.
U-aggregation combines multiple models without labels for better risk prediction.
problem Challenges in selecting best model for new populations due to limited data and lack of true labels.
method U-aggregation, an unsupervised model aggregation method that integrates pre-trained models without observed labels.
result U-aggregation improves genetic risk prediction of complex traits using publicly available models.