A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Anchor-TS uses median anchoring to improve online decision-making from offline data with distribution shift.
New method explains survival analysis models using median-SHAP.
We improve the recently-proposed "MixMatch" semi-supervised learning algorithm by introducing two new techniques: distribution alignment and augmentation anchoring. Distribution alignment encourages the marginal distribution of predictions on unlabeled data to be close to the marginal distribution of ground-truth label…
Adaptive anchor methods improve multi-modal learning by balancing intra-modal and inter-modal information.
Private anchors affect how information is communicated and can improve or distort transmission.
Anchors explain text model decisions by highlighting key words.
LargeMvC-Net improves scalability of multi-view clustering.
We construct compactifications for median spaces with compact intervals, generalising Roller boundaries of cube complexes. Examples of median spaces with compact intervals include all finite rank median spaces and all proper median spaces of infinite rank. Our methods also work for general median algebra…
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…
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…
Δ-UQ uses anchoring to estimate uncertainty in models.
Paper explores anchoring for vision models, improving generalization and safety.
New concept of coarse medians for higher rank symmetric spaces.
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…
Unique median structures found in hyperbolic spaces.
Study on median algebra structures on Euclidean spaces and manifolds with local CAT(0) cubulation.
Market stability depends on a fundamental value anchor, not price crashes.
We prove a version of the Tits alternative for groups acting on complete, finite rank median spaces. This shows that group actions on finite rank median spaces are much more restricted than actions on general median spaces. Along the way, we extend to median spaces the Caprace-Sageev machinery and part of Hagen's theor…
This work merges 3-anchored bundles into 3-Lie algebroids.
A new noise model for preferential Bayesian optimization using user anchors.
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…
We show that uniform lattices of isometries of products of real hyperbolic spaces act properly discontinuously and cocompactly on a median space. For lattices in products of at least two factors, this is the strongest degree of compatibility possible with the median geometry. Our theorem is also relevant for potential …
Convex cores found for group actions on median spaces.
We introduce and begin to explore the mean and median of finite sets of shapes represented as integral currents. The median can be computed efficiently in practice, and we focus most of our theoretical and computational attention on medians. We consider questions on the existence and regularity of medians. While the me…
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.
A new method resolves non-identifiability in reward modeling using anchor labels.
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…
This paper is a short summary of our recent work on the medians and means of probability measures in Riemannian manifolds. Firstly, the existence and uniqueness results of local medians are given. In order to compute medians in practical cases, we propose a subgradient algorithm and prove its convergence. After that, F…
Anchor PCA improves robustness in multi-domain PCA.
New method improves continual learning by anchoring past knowledge.
Paper proposes an alternative to anchor points for learning with noisy labels.
A new method for efficient inference in sequential latent-variable models.
A method for estimating the median of gradients in stochastic optimization.
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…
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…
This paper introduces online algorithms to estimate robust geometric median in large data streams.
Study improves predictive models for ICU data across hospitals.
The study finds that maximizing median returns is the only viable strategy in portfolio selection.
New graph properties inherited by Frechet mean and median.
ADA augments data using AR replicas for robust regression.
The consistency of Fréchet medians is proved for probability measures in proper metric spaces. In the context of Riemannian manifolds, assuming that the probability measure has more than a half mass lying in a convex ball and verifies some concentration conditions, the positions of its Fréchet medians are estimated. It…
Develops a new framework for temporal anchoring in deep embedding spaces.
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…
In high dimensions, the mean and geometric median are nearly identical.
New subspace prototype flag median improves clustering on noisy data.
Improved median of means estimator with tighter bounds.
Empirical median performs well in estimating location with varying scales.
FALL improves local model training with anchor regularization.