A new method selects anchor words for better topic discovery in text corpora.
problem Selecting anchor words for improved topic modeling in text corpora.
method Proposes a new greedy method to find a minimum edge-weight anchor clique in a word similarity graph.
result The proposed method outperforms existing methods on topic quality and is faster.
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.
Interactive topic models with user-defined anchor words.
problem Building interpretable topic models.
method Protocol allowing users to interactively define anchor words for topic modeling.
result Validation of the usefulness of interactive topic models.
In topic modeling, many algorithms that guarantee identifiability of the topics have been developed under the premise that there exist anchor words -- i.e., words that only appear (with positive probability) in one topic. Follow-up work has resorted to three or higher-order statistics of the data corpus to relax the an…
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…
We introduce a new approach for topic modeling that is supervised by survival analysis. Specifically, we build on recent work on unsupervised topic modeling with so-called anchor words by providing supervision through an elastic-net regularized Cox proportional hazards model. In short, an anchor word being present in a…
Anchors explains text classifiers by highlighting key words.
problem Interpreting machine learning models, especially for text classifiers.
method Formalizes Anchors' algorithm and analyzes its behavior on linear text classifiers.
result Anchors produces meaningful results on linear text classifiers.
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 …
Novel framework explains machine learning models using ontology-based sampling.
problem Generating precise and insightful explanations for machine learning models.
method Ontology-based sampling technique and learnable anchor algorithm.
result Our approach generates more precise and insightful explanations.
Robust SPA improves NMF robustness to outliers.
problem Non-robustness to outliers in SPA.
method Integrates outlier robustness and data fitting into SPA.
result RSPA is robust to outliers and maintains low-noise robustness.
A new method resolves non-identifiability in reward modeling using anchor labels.
problem Non-identifiability in reward modeling from pairwise preferences alone.
method Anchor-guided Variance-aware Reward Modeling (AVRM) framework.
result AVRM resolves non-identifiability and improves reward modeling performance.
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.
This paper establishes an equivalence between transitive double Lie algebroids and core diagrams.
problem Understanding and characterizing transitive double Lie algebroids.
method Using core diagrams and equivalence of transitive core diagrams with transitive double Lie groupoids.
result Transitive double Lie algebroids are completely determined by their core diagrams.
PEAKS selects key training examples incrementally based on prediction error and kernel similarity.
problem Dynamic data selection in deep learning models.
method Prediction Error Anchored by Kernel Similarity (PEAKS) for incremental data selection.
result PEAKS outperforms existing selection strategies and yields better performance returns as training data size grows.
This paper develops a method to select a reference contract for multi-contract quoting to minimize execution risk.
problem Minimizing execution risk in multi-contract quoting sequences.
method Develops a diagnostic framework using order-flow Hawkes forecasts and CLF to select a stable reference contract.
result Event-history and LOB-state signals offer complementary views for reference-contract selection.
In this paper, we provide a theoretical understanding of word embedding and its dimensionality. Motivated by the unitary-invariance of word embedding, we propose the Pairwise Inner Product (PIP) loss, a novel metric on the dissimilarity between word embeddings. Using techniques from matrix perturbation theory, we revea…
Transformers improve Alzheimer's disease progression prediction by accounting for irregular biomarker histories.
problem Difficult prediction of medium-horizon Alzheimer's disease progression due to tied clinical scores and irregular biomarker observations.
method Developed a residual gap-aware transformer that combines statistical reference with transformer-based residual learning.
result The proposed model reduces mean error and improves prediction-observation correlation compared to baseline models.
Regularizes ML algorithms for robust multivariate analysis against distribution shifts.
problem Ensuring robustness of multivariate analysis algorithms against distribution shifts.
method Integrates a causal regularisation term into the loss function of multivariate analysis algorithms.
result Demonstrates improved out-of-distribution generalisation with reduced-rank regression and partial least squares.
CLARA generates clinical reports from raw inputs, improving accuracy and efficiency.
problem Generating accurate and detailed clinical reports from raw inputs is time-consuming and error-prone.
method Interactive method that generates reports sentence by sentence based on doctors' anchor words and partially completed sentences.
result CLARA achieves significant improvements in report generation accuracy and efficiency.
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.
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…
POLAR framework interprets word embeddings using polar opposites.
problem Lack of interpretability in pre-trained word embeddings.
method Adopt semantic differentials and polar opposites to transform embeddings.
result Interpretable word embeddings maintain performance comparable to original embeddings.
Transformer model estimates keywords for better audio captioning.
problem Indeterminacy in word selection for audio events/scenes.
method Transformer-based model with keyword estimation.
result Achieved state-of-the-art performance in AAC.
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.
New distances for comparing heterogeneous probability measures efficiently.
problem Comparing probability measures across different spaces.
method Introducing Anchor Energy (AE) and Anchor Wasserstein (AW) distances, and a sweep line algorithm for exact computation.
result Exact computation of AE and AW distances in log-quadratic time, significantly faster than GW.
Neural network model improves longevity risk assessment.
problem Systematic mispricing of longevity risk in linear models.
method Hybrid-Lift framework combining Hierarchical LSTM networks and Mean-Bias Correction.
result Hybrid-Lift outperforms Li-Lee framework by 17.40% in Sweden and 12.57% in West Germany.
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.
Proposes SNML for selecting word2vec Skip-gram dimensionality.
problem Selecting optimal dimensionality for word2vec Skip-gram models.
method Information criteria (AIC, BIC, SNML) applied to SG and SG Negative Sampling models.
result SNML outperforms AIC and BIC, selecting closer optimal dimensionality.
Paper proposes MMD-Sense-Analysis for detecting word sense shifts.
problem Detecting and interpreting shifts in word meanings over time.
method Leverages Maximum Mean Discrepancy (MMD) to identify and explain word sense changes.
result Demonstrates effectiveness of MMD-Sense-Analysis through empirical results.
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…
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.
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.
SRRM improves recursive transport surrogates in the small-discrepancy regime.
problem Insufficient understanding of recursive partitioning methods' statistical behavior and resolution in the small-discrepancy regime.
method Introduced Selective Recursive Rank Matching (SRRM) to improve the resolution of Recursive Rank Matching (RRM).
result SRRM yields a higher-fidelity practical surrogate for the Wasserstein distance at moderate additional computational cost.
Study improves materials discovery for high-entropy alloys using sparse linear models.
problem Inefficient materials discovery due to combinatorial explosion in alloy compositions.
method Sparse mixed linear modeling with anchor-based guidance for feature selection and prediction.
result Developed a method that balances predictive performance and interpretability for materials discovery.
A method to learn transition matrices without anchor points improves classifier performance.
problem Learning transition matrices in label-noise learning without anchor points.
method Transition-revision (T-Revision) method to learn transition matrices. result The proposed method leads to better classifiers without anchor points.
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.
Proposes a TS approach for Bayesian optimization with preferential feedback.
problem Optimizing with preference feedback in complex applications.
method Uses Thompson Sampling with a dueling kernel and anchor invariance.
result Performance matches standard TS for scalar feedback in finite time.
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.
The top word list, i.e., the top-M words with highest marginal probability in a given topic, is the standard topic representation in topic models. Most of recent automatical topic labeling algorithms and popular topic quality metrics are based on it. However, we find, empirically, words in this type of top word list ar…
TL-ANDI distills context from source data to improve transfer learning for TFMs.
problem Limited transfer learning due to context-size constraints and distribution shifts.
method TL-ANDI uses posterior-aware distillation to construct a compact source context and locally distills labels.
result Improves transfer performance by addressing context-size and distribution shifts.
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.
In this article, we continue the classification of finite type Gauss map surfaces in the Euclidean 3-space E3 with respect to the first fundamental form by studying a subclass of tubes, namely the anchor rings. We show that anchor rings are of infinite type Gauss map.
New measure helps identify better word embedding compression methods.
problem Challenges in evaluating compressed word embeddings for downstream tasks.
method Proposed eigenspace overlap score and developed generalization bounds.
result Eigenspace overlap score correlates with better downstream performance.
A pre-Lie algebroid is an anchored bundle provided with an almost Lie bracket such that the anchor is compatible with the Lie bracket of vector fields. We firstly show how most geometrical structures intensively studied in the framework of Lie algebroid can easily be extended in the pre-Lie algebroid context. The princ…
Anchor PCA improves robustness in multi-domain PCA.
problem PCA on pooled data can focus on spurious directions.
method Anchor PCA focuses on shared directions of variation.
result Anchor PCA outperforms pooling and worst-case alternatives.
New method improves continual learning by anchoring past knowledge.
problem Catastrophic forgetting in continual learning.
method Bilevel optimization to update current task knowledge while keeping past task predictions.
result Improves accuracy and forgetting metrics compared to experience replay.
Paper proposes an alternative to anchor points for learning with noisy labels.
problem Learning with noisy labels is challenging due to inaccurate labels.
method Estimates transition matrix using clusterability condition and noisy labels.
result Estimation of transition matrix is more accurate and efficient than anchor points.
A new method for efficient inference in sequential latent-variable models.
problem Computational challenges in integrating subject-specific random effects.
method Anchored variational inference framework to approximate posterior distributions.
result The method achieves accurate estimation with significant computational gains.