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.
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…
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.
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.
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.
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.
In label-noise learning, \textit{noise transition matrix}, denoting the probabilities that clean labels flip into noisy labels, plays a central role in building \textit{statistically consistent classifiers}. Existing theories have shown that the transition matrix can be learned by exploiting \textit{anchor points} (i.e…
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.
New method shows spectral clustering is consistent with theoretical guarantees.
problem Lack of theoretical support for anchor-based spectral clustering.
method Defined and analyzed a specific anchor-based algorithm.
result Theoretical consistency of the method in asymptotic settings.
Study improves predictive models for ICU data across hospitals.
problem Degradation of predictive model performance in new hospitals.
method Anchor regression and anchor boosting for domain generalization.
result Anchor regularization enhances out-of-distribution performance.
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.
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.
ADA augments data using AR replicas for robust regression.
problem Improving robustness in nonlinear over-parametrized regression.
method Extends Anchor regression (AR) for data augmentation, using replicas of modified samples.
result ADA provides more robust regression predictions compared to state-of-the-art solutions.
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.
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.
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…
AutoAnchor uses cross-attention to improve text-to-image model unlearning.
problem Mitigating harmful or copyrighted content in text-to-image models.
method Two-stage framework that automatically synthesizes manifold-proximal anchors using cross-attention consistency loss.
result Effective robust and unbiased unlearning across various baselines.
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.
FALL improves local model training with anchor regularization.
problem Efficient local model training in regression tasks.
method Regularization with precomputed anchor models, closed-form solution.
result FALL outperforms network Lasso in accuracy with significantly less training time.
Some tasks, such as surface normals or single-view depth estimation, require per-pixel ground truth that is difficult to obtain on real images but easy to obtain on synthetic. However, models learned on synthetic images often do not generalize well to real images due to the domain shift. Our key idea to improve domain …
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.
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 formalism of anchor words has enabled the development of fast topic modeling algorithms with provable guarantees. In this paper, we introduce a protocol that allows users to interact with anchor words to build customized and interpretable topic models. Experimental evidence validating the usefulness of our approach…
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.
We present a semi-supervised learning algorithm for learning discrete factor analysis models with arbitrary structure on the latent variables. Our algorithm assumes that every latent variable has an "anchor", an observed variable with only that latent variable as its parent. Given such anchors, we show that it is possi…
Proposes KAR for nonlinear causal discovery using kernel methods.
problem Learning causal relationships in nonlinear settings.
method Kernel anchor regression (KAR) with improved three-stage nonparametric regression.
result KAR outperforms existing methods in nonlinear causal discovery.
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…
ANT learns sparse embeddings for large vocabularies efficiently.
problem Lack of scalable methods for embedding large vocabularies in neural networks.
method Anchor & Transform (ANT) algorithm that learns a small set of anchor embeddings and a sparse transformation matrix.
result ANT achieves stronger performance with fewer parameters (up to 40x compression) compared to existing methods.
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.
Anchor-TS uses median anchoring to improve online decision-making from offline data with distribution shift.
problem Improving online decision-making from offline data with distribution shift.
method Sample-Mean Anchored Thompson Sampling (Anchor-TS) with median anchoring.
result Anchor-TS safely leverages offline data to accelerate online learning and reduces regret.
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…
Paper tackles robust Euclidean distance estimation with sparse outliers.
problem Estimating point positions from corrupted distance measurements.
method Proposes a novel algorithm using Nyström method and robust PCA.
result Achieves accurate recovery with minimal anchors and sparse outliers.
AGCA approximates angular variation on the unit sphere, reducing extremal dependence problems to eigenanalysis.
problem Approximating angular variation in multivariate extremes.
method Anchored geodesic component analysis (AGCA) approximates angular variation by great subspheres constrained to pass through a chosen reference direction.
result AGCA finds concentrated tail directions in daily equity-portfolio losses, explaining about 91% of anchored variation.
We extend and improve the work of Model Agnostic Anchors for explanations on image classification through the use of generative adversarial networks (GANs). Using GANs, we generate samples from a more realistic perturbation distribution, by optimizing under a lower dimensional latent space. This increases the trust in …
Develops a new framework for temporal anchoring in deep embedding spaces.
problem Temporal anchoring in deep embedding spaces, especially drift and convergence issues.
method Operator-theoretic framework with drift maps and event-indexed blocks, proving convergence theorems and equivalence theorems.
result Proves convergence theorems and equivalence theorems for the proposed framework.
Predicting human behavior is a difficult and crucial task required for motion planning. It is challenging in large part due to the highly uncertain and multi-modal set of possible outcomes in real-world domains such as autonomous driving. Beyond single MAP trajectory prediction, obtaining an accurate probability distri…
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…
Unified framework for imbalanced data resampling improves classification performance.
problem Data imbalance negatively impacts machine learning performance.
method Unified framework combining over- and undersampling with radial basis functions optimization.
result Potential Anchoring outperforms state-of-the-art resampling algorithms.
Proposes a method to correct for covariate shift in meta-analysis of randomized trials.
problem Invalidation of standard IPD meta-analysis due to covariate shift across studies.
method Placebo-anchored transport framework that treats source-trial outcomes as proxy signals and target-trial placebo outcomes as gold labels.
result Yields target-identified effect estimates in connected targets and a principled screen--then--transport procedure in disconnected targets.
A framework visualizes embedding spaces of neural survival analysis models using anchor directions.
problem Visualizing complex embeddings in neural survival analysis models.
method Estimating anchor directions through clustering or user-supplied concepts, revealing relationships with raw inputs and survival times.
result Visualization strategies reveal how anchor directions relate to raw clinical features and survival time distributions.
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. First, we extend the notion of second order differential equations (SODE) on a smooth manifold to anchored Banach vector bundles. Then we define the Banach Lie algebroids as Lie algebroids structures modeled on anchored Banach vector bundles and prove that they form a category.
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…
PQR estimates reward functions from actions and states without assuming state-only rewards.
problem Estimating reward functions from actions and states without state-only assumptions.
method Deep learning approach that sequentially estimates policy, Q-function, and reward.
result PQR uniquely recovers true reward with known transitions and bounds error with unknown transitions.
FedAMD framework improves federated learning with partial client participation.
problem Data heterogeneity and inactive client updates in partial client participation.
method Anchor sampling divides clients into anchor and miner groups, using large and small batches respectively.
result FedAMD achieves faster convergence and improved model performance compared to state-of-the-art methods.
New method explains survival analysis models using median-SHAP.
problem Need for explainable AI in medical applications, especially for survival analysis.
method Introduces median-SHAP for explaining survival analysis models.
result Conventionally used mean anchor point can lead to misleading interpretations; median-SHAP provides a better approach.