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.
Proposes using dimensionality reduction with personalized page rank to improve GCNs' performance in node classification.
CUQ-GNN adapts uncertainty quantification for graph data, improving on GPN.
Optimization is commonly employed to determine the content of web pages, such as to maximize conversions on landing pages or click-through rates on search engine result pages. Often the layout of these pages can be decoupled into several separate decisions. For example, the composition of a landing page may involve dec…
These are problems on Heegaard splittings, that were raised at the Workshop, listed according to their contributors: David Bachman, Mario Eudave-Munoz, John Hempel, Tao Li, Yair Minsky, Yoav Moriah and Richard Weidmann. On pages 285-298 of this monograph (arxiv:0904.0017) Hyam Rubinstein gives a personal collection of …
A search engine recommends to the user a list of web pages. The user examines this list, from the first page to the last, and clicks on all attractive pages until the user is satisfied. This behavior of the user can be described by the dependent click model (DCM). We propose DCM bandits, an online learning variant of t…
This paper protects rankings from differential privacy breaches.
A new method models user-specific parameters as a low-rank plus sparse component for efficient personalization.
Item recommendation is the task of predicting a personalized ranking on a set of items (e.g. websites, movies, products). In this paper, we investigate the most common scenario with implicit feedback (e.g. clicks, purchases). There are many methods for item recommendation from implicit feedback like matrix factorizatio…
We demonstrate that effortlessly accessible digital records of behavior such as Facebook Likes can be obtained and utilized to automatically distinguish a wide range of highly delicate personal traits including: life satisfaction, cultural ethnicity, political views, age, gender and personality traits. The analysis pre…
In designing personalized ranking algorithms, it is desirable to encourage a high precision at the top of the ranked list. Existing methods either seek a smooth convex surrogate for a non-smooth ranking metric or directly modify updating procedures to encourage top accuracy. In this work we point out that these methods…
Item recommendation is a personalized ranking task. To this end, many recommender systems optimize models with pairwise ranking objectives, such as the Bayesian Personalized Ranking (BPR). Using matrix Factorization (MF) --- the most widely used model in recommendation --- as a demonstration, we show that optimizing it…
Given a knot K in S^3, let Σ(K) be the double branched cover of S^3 over K. We show there is a spectral sequence whose E^1 page is (\hat{HFK}(Σ(K), K) \otimes V^{n-1}) \otimes \mathbb Z_2((q)), for V a \mathbb Z_2-vector space of dimension two, and whose E^{\infty} page is isomorphic to (\hat{HFK}(S^3, K) \otimes V^{n-…
New ranking algorithms improve online content delivery by learning from click data.
The conventional solution to the recommendation problem greedily ranks individual document candidates by prediction scores. However, this method fails to optimize the slate as a whole, and hence, often struggles to capture biases caused by the page layout and document interdepedencies. The slate recommendation problem …
Spotify improves content mix using contextual bandits.
Paper proposes a novel MTL framework for personalized modeling of diverse individuals.
SessionPath improves category suggestions in type-ahead search.
The collaborative ranking problem has been an important open research question as most recommendation problems can be naturally formulated as ranking problems. While much of collaborative ranking methodology assumes static ranking data, the importance of temporal information to improving ranking performance is increasi…
Paper proposes a two-stage ranking for personalized TV recommendations.
A search engine usually outputs a list of web pages. The user examines this list, from the first web page to the last, and chooses the first attractive page. This model of user behavior is known as the cascade model. In this paper, we propose cascading bandits, a learning variant of the cascade model where the obje…
With the rapid growth in fashion e-commerce and customer-friendly product return policies, the cost to handle returned products has become a significant challenge. E-tailers incur huge losses in terms of reverse logistics costs, liquidation cost due to damaged returns or fraudulent behavior. Accurate prediction of prod…
iSplit LBI predicts individualized partial rankings from ties, outperforming state-of-the-art methods.
Simpler GNNs with low-rank non-parametric aggregators perform well on graph benchmarks.
Time-aware fact-checking improves veracity predictions for time-sensitive claims.
Develops algorithms for personalized ranking in recommender and energy systems.
Alternative proof of Dynnikov's three-page index for torus links.
Let be the spectral sequence induced by the oriented cube of resolutions on knot Floer homology. We prove that is a triply graded link invariant whose graded Euler characteristic is the HOMFLY-PT polynomial and that the higher pages are link invariants. By construction, the spectral sequen…
POET enables large neural network training on tiny devices with reduced energy.
This paper addresses the problem of emotion recognition from physiological signals. Features are extracted and ranked based on their effect on classification accuracy. Different classifiers are compared. The inter-subject variability and the personalization effect are thoroughly investigated, through trial-based and su…
Study shows how online personalization can lead to unfair models due to biased user responses.
PAGE optimizes nonconvex problems with optimal convergence rates.
Efficient graph neural networks for large graphs without sampling.
Scalable model for slate recommendation learns reward probabilities.
Differentially private graph learning via bounded sensitivity PPR.
We consider a problem of ranking and selection via simulation in the context of personalized decision making, where the best alternative is not universal but varies as a function of some observable covariates. The goal of ranking and selection with covariates (R&S-C) is to use simulation samples to obtain a selection p…
Despite the overwhelming success of the existing Social Networking Services (SNS), their centralized ownership and control have led to serious concerns in user privacy, censorship vulnerability and operational robustness of these services. To overcome these limitations, Distributed Social Networks (DSN) have recently b…
Advances in collaborative filtering and ranking methods.
This paper improves fairness in recommendation systems by learning individual preferences across multiple dimensions.
Network embedding methodologies, which learn a distributed vector representation for each vertex in a network, have attracted considerable interest in recent years. Existing works have demonstrated that vertex representation learned through an embedding method provides superior performance in many real-world applicatio…
In this work, we investigate the importance of ethnicity in colorectal cancer survivability prediction using machine learning techniques and the SEER cancer incidence database. We compare model performances for 2-year survivability prediction and feature importance rankings between Hispanic, White, and mixed patient po…
The paper refines the three-page index for links, proving a new bound and characterizing specific links.
Study the spectrum of Page's metric on complex projective spaces.
Optimizes web page freshness with limited crawling frequencies.
We construct a series of finitely presented semigroups. The centers of these semigroups encode uniquely up to rigid ambient isotopy in 3-space all non-oriented spatial graphs. This encoding is obtained by using three-page embeddings of graphs into the product of the line with the cone on three points. By exploiting thr…
Extracts main content from web pages using neural sequence labeling.
Extends deformation theory to higher-page analogues of manifolds.
Improved linear upper bound for ribbonlength of knots.
PAGE is a simple gradient estimator for nonconvex optimization problems.