GraphSTONE uses topic models to capture graph structures, improving GCN performance.
problem GCNs focus too much on node features and not enough on graph structures.
method GraphSTONE employs topic models of graphs to capture structural topics, which guide the aggregation of node features.
result GraphSTONE outperforms GCNs in performance, efficiency, and interpretability.
While generative models such as Latent Dirichlet Allocation (LDA) have proven fruitful in topic modeling, they often require detailed assumptions and careful specification of hyperparameters. Such model complexity issues only compound when trying to generalize generative models to incorporate human input. We introduce …
We reduce a broad class of machine learning problems, usually addressed by EM or sampling, to the problem of finding the k extremal rays spanning the conical hull of a data point set. These k "anchors" lead to a global solution and a more interpretable model that can even outperform EM and sampling on generalizatio…
Graph attacks can be successful with just a few bad nodes.
problem Adversarial attacks on graph neural networks.
method Identifying and exploiting anchor nodes to compromise graph models.
result A few bad nodes can significantly degrade graph model performance.
Learning node embeddings that capture a node's position within the broader graph structure is crucial for many prediction tasks on graphs. However, existing Graph Neural Network (GNN) architectures have limited power in capturing the position/location of a given node with respect to all other nodes of the graph. Here w…
Separable Non-negative Matrix Factorization (SNMF) is an important method for topic modeling, where "separable" assumes every topic contains at least one anchor word, defined as a word that has non-zero probability only on that topic. SNMF focuses on the word co-occurrence patterns to reveal topics by two steps: anchor…
Originally designed to model text, topic modeling has become a powerful tool for uncovering latent structure in domains including medicine, finance, and vision. The goals for the model vary depending on the application: in some cases, the discovered topics may be used for prediction or some other downstream task. In ot…
Language is dynamic, constantly evolving and adapting with respect to time, domain or topic. The adaptability of language is an active research area, where researchers discover social, cultural and domain-specific changes in language using distributional tools such as word embeddings. In this paper, we introduce the gl…
LargeMvC-Net improves scalability of multi-view clustering.
problem Scalability issues in multi-view clustering.
method Deep unfolding of multi-view clustering into a network architecture with three modules.
result LargeMvC-Net consistently outperforms state-of-the-art methods in scalability and effectiveness.
GraphReach improves GNN performance by incorporating node positions.
problem Existing GNNs fail to capture node positions, leading to inaccurate predictions.
method GraphReach uses reachability estimations from anchor nodes to capture global node positions.
result GraphReach achieves up to 40% relative improvement in accuracy compared to state-of-the-art GNNs.
A new framework for graph representation learning.
problem Acquiring continuous representations of discrete objects like graphs.
method Nested SubSpace (NSS) arrangement and Disk-ANChor ARrangement (DANCAR).
result Successfully embedded WordNet in 20-dimensional space with high F1 score.
A framework evaluates the impact of different modules in graph contrastive learning.
problem Insufficient module-level evaluation in existing graph contrastive learning methods.
method Proposes a framework decomposing GCL models into four modules for module-level evaluation.
result Identifies module-level guidelines and competitive performance of different modules.
Sparse subspace clustering (SSC) is one of the current state-of-the-art methods for partitioning data points into the union of subspaces, with strong theoretical guarantees. However, it is not practical for large data sets as it requires solving a LASSO problem for each data point, where the number of variables in each…
Can neural networks learn to compare graphs without feature engineering? In this paper, we show that it is possible to learn representations for graph similarity with neither domain knowledge nor supervision (i.e.\ feature engineering or labeled graphs). We propose Deep Divergence Graph Kernels, an unsupervised method …
Reinterprets DNF as a deep generative LDA model for complex data.
problem Limited applicability of LDA in complex data scenarios.
method Proposes a discriminative normalization flow (DNF) model and interprets it as a deep generative LDA.
result DNF and its subspace version outperform conventional LDA in modeling complex data.
Despite many years of research into latent Dirichlet allocation (LDA), applying LDA to collections of non-categorical items is still challenging. Yet many problems with much richer data share a similar structure and could benefit from the vast literature on LDA. We propose logistic LDA, a novel discriminative variant o…
In latent Dirichlet allocation (LDA), topics are multinomial distributions over the entire vocabulary. However, the vocabulary usually contains many words that are not relevant in forming the topics. We adopt a variable selection method widely used in statistical modeling as a dimension reduction tool and combine it wi…
Hyper-parameters play a major role in the learning and inference process of latent Dirichlet allocation (LDA). In order to begin the LDA latent variables learning process, these hyper-parameters values need to be pre-determined. We propose an extension for LDA that we call 'Latent Dirichlet allocation Gibbs Newton' (LD…
Simple Deep LDA models achieve accuracy competitive with softmax baselines.
problem Training Deep LDA models by maximum likelihood estimation leads to overlapping or collapsed class clusters.
method Proposed a constrained Deep LDA formulation with geometric constraints to fix class means and covariance.
result MLE becomes stable under geometric constraints, yielding well-separated class clusters.
This tutorial explains Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA) as two fundamental classification methods in statistical and probabilistic learning. We start with the optimization of decision boundary on which the posteriors are equal. Then, LDA and QDA are derived for binary and mul…
E-LDA offers faster, interpretable LDA topic models.
problem Inferring topics in LDA topic models with strong guarantees.
method Non-gradient combinatorial approach for faster convergence.
result Logarithmic parallel computation time and interpretability.
LDA-GO improves LDA for high-dimensional data via gradient optimization.
problem LDA struggles in high-dimensional settings due to unreliable covariance matrix estimation.
method LDA-GO learns a low-rank precision matrix via gradient optimization, automatically selecting between Gaussian likelihood and cross-entropy loss.
result LDA-GO outperforms other LDA variants in sparse-signal high-dimensional regimes.
Novel framework improves GNN uncertainty estimates under distribution shifts.
problem Improving reliability of GNN uncertainty estimates under distribution shifts.
method Adapting stochastic data centering to graph data through novel graph anchoring strategies.
result G-ΔUQ leads to better calibrated GNNs for node and graph classification. Adaptive anchor methods improve multi-modal learning by balancing intra-modal and inter-modal information.
problem Fixed anchor methods limit multi-modal learning by over-reliance on a single modality and inadequate cross-modal correlation.
method Adaptive anchor methods using centroid-based anchors from all modalities.
result Adaptive anchor methods like CentroBind consistently outperform fixed anchor methods across various datasets.
Latent Dirichlet Allocation (LDA) is a popular topic modeling technique for discovery of hidden semantic architecture of text datasets, and plays a fundamental role in many machine learning applications. However, like many other machine learning algorithms, the process of training a LDA model may leak the sensitive inf…
LDA improves image classification accuracy with fewer features.
problem Fine-grained image classification with pretrained features.
method Supervised dimensionality reduction with LDA before linear probing.
result LDA improves accuracy over full features in 11 out of 12 configurations.
A new LDA variant improves multi-label classification performance.
problem Improving multi-label classification performance.
method Saliency-based weights redefine between-class and within-class scatter matrices for multi-label classification.
result The proposed method leads to performance improvements in various multi-label classification problems.
New method improves reliability of LDA topic modeling by assessing stability across replicated runs.
problem LDA's reproducibility issues due to initial values and Gibbs sampling.
method Cluster replicated LDA runs using modified Jaccard coefficient and pruning algorithm.
result New measure S-CLOP quantifies LDA topic stability, improving reproducibility.
Private anchors affect how information is communicated and can improve or distort transmission.
problem How private anchors influence strategic communication and information transmission.
method Analyzed a sender-receiver game with costly reports and privately observed anchors.
result Small positive reporting costs can lead to full revelation, even with zero costs.
MILDA uses unlabelled data to compute LDA projections.
problem Training LDA models with unlabelled data.
method Minimal prior information to compute LDA projection vector.
result MILDA closely matches supervised LDA performance and adapts to non-stationary data.
Anchors explain text model decisions by highlighting key words.
problem Explaining neural network decisions for text data.
method Formalized Anchors for text classification, analyzing different models.
result Neural network words with highest partial derivatives are selected by Anchors.
Derives VMP for LDA, simplifying inference for topic modeling.
problem Manual derivation of VMP equations for LDA is challenging and time-consuming.
method Detailed derivation of VMP update equations for LDA.
result Enables easier implementation of VMP for LDA models.
We introduce a novel approach for estimating Latent Dirichlet Allocation (LDA) parameters from collapsed Gibbs samples (CGS), by leveraging the full conditional distributions over the latent variable assignments to efficiently average over multiple samples, for little more computational cost than drawing a single addit…
Anchoring is a term used in psychology to describe the common human tendency to rely too heavily (anchor) on one piece of information when making decisions. A trading algorithm inspired by biological motors, introduced by L. Gil\cite{Gil}, is suggested as a testing ground for anchoring in financial markets. An exact so…
LDA-XGB1 balances fairness and accuracy in lending models.
problem Fair lending practices and model interpretability in binary classification.
method Biobjective optimization using binning and information value, leveraging XGBoost.
result Achieves effective balance between accuracy, fairness, and interpretability.
Δ-UQ uses anchoring to estimate uncertainty in models.
problem Estimating uncertainty in predictive models.
method Anchoring input into a tuple for uncertainty estimation.
result Δ-UQ outperforms baselines in various use-cases.
LDTA expands LDA's topic modeling capacity with tree-structured priors.
problem Limited expressiveness of Dirichlet priors in LDA for complex topic relationships.
method Introduces Latent Dirichlet-Tree Allocation (LDTA) with Dirichlet-Tree (DT) priors, and develops universal mean-field variational inference and Expectation Propagation.
result LDTA enables expressive, tree-structured priors over topic proportions, expanding modeling capacity of LDA.
Paper explores anchoring for vision models, improving generalization and safety.
problem Anchoring can lead to undesirable shortcuts, limiting generalization.
method Introduced a new anchored training protocol with a regularizer to mitigate undesirable shortcuts.
result Significant performance gains in generalization and safety metrics.
This paper defines less discriminatory algorithms and explores their feasibility.
problem Creating algorithms that are less discriminatory while meeting business needs.
method Formal definition of less discriminatory algorithms, evaluation of feasibility, and search for alternatives.
result Formal definitions of less discriminatory algorithms face challenges due to lack of held-out data, necessitating a reliance on reasonableness standards.
The sBIC outperforms other model selection criteria in LDA topic modeling.
problem Selecting the optimal number of topics in Latent Dirichlet Allocation (LDA) models.
method Monte Carlo simulations comparing sBIC to other criteria.
result sBIC is superior for choosing the number of topics in LDA models.
DNLL loss improves deep LDA accuracy and consistency.
problem Pathological solutions in unconstrained Deep LDA.
method Introducing Discriminative Negative Log-Likelihood (DNLL) loss.
result Deep LDA trained with DNLL produces clean latent spaces and better calibrated probabilities.
Improved topic modeling captures temporal relationships in speech.
problem Lack of temporal information in LDA for speech analysis.
method Temporal Markov chain extension to LDA for acoustic unit discovery.
result Improved phone segmentation results compared to base LDA.
A new method improves label propagation for unsupervised domain adaptation.
problem Improving unsupervised domain adaptation through semi-supervised learning techniques.
method Label Propagation with Augmented Anchors (A2LP) for UDA. result A2LP improves over representative UDA methods and benchmarks. Market stability depends on a fundamental value anchor, not price crashes.
problem Stability of order-book markets under fundamental anchoring.
method Analytical model and empirical analysis of six transmission channels.
result Fundamental anchoring stabilizes markets by mean-reverting prices and refilling books; removing the anchor leads to market failure.
SWRLDA improves LDA for multi-class classification with edge classes.
problem LDA's vulnerability to edge classes causing biased mean and large distances.
method Self-weighted robust LDA with l21-norm distance criterion.
result SWRLDA outperforms other methods on synthetic and real-world datasets.
A new LDA model with covariates for mixed-membership clusters.
problem Modeling mixed-membership clusters in discrete data with covariates.
method Negative binomial regression embedded within LDA, slice sampling within Gibbs sampling.
result Model successfully retrieves true parameter values and predicts cluster abundances using covariates.
This work merges 3-anchored bundles into 3-Lie algebroids.
problem Combining two 3-anchored bundles into a unified structure.
method Develops algebraic framework for merging bundles with mutual actions and cocycle terms.
result Unified setting for 3-Lie algebroids and special cases.
A new noise model for preferential Bayesian optimization using user anchors.
problem Inadequate assumption of homoscedastic noise in human-in-the-loop settings.
method Proposes a heteroscedastic noise model with anchors and a KDE uncertainty map.
result Risk-adjusted performance improvement and clarified anchor placement effects.