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.
Extends RRR to capture nonlinear interactions in multi-response regression.
New model enhances SPIM for solving low-rank combinatorial optimization and statistical learning problems.
LITE models improve query-document relevance with learnable late interactions.
We study the limiting behaviour of the empirical measure of a system of diffusions interacting through their ranks when the number of diffusions tends to infinity. We prove that the limiting dynamics is given by a McKean-Vlasov evolution equation. Moreover, we show that in a wide range of cases the evolution of the cum…
We study the problem of approximate ranking from observations of pairwise interactions. The goal is to estimate the underlying ranks of objects from data through interactions of comparison or collaboration. Under a general framework of approximate ranking models, we characterize the exact optimal statistical error …
Factorization machines (FM) are a popular model class to learn pairwise interactions by a low-rank approximation. Different from existing FM-based approaches which use a fixed rank for all features, this paper proposes a Rank-Aware FM (RaFM) model which adopts pairwise interactions from embeddings with different ranks.…
Interactive learning with hindsight instruction feedback achieves better performance than traditional methods.
DCN-V2 improves deep & cross network for web-scale learning to rank systems.
Bayesian model improves image completion accuracy by automatically learning low rank structure.
Recently, Factorization Machines (FM) has become more and more popular for recommendation systems, due to its effectiveness in finding informative interactions between features. Usually, the weights for the interactions is learnt as a low rank weight matrix, which is formulated as an inner product of two low rank matri…
Framework for optimizing search engine rankings using observational data.
Graphs can model interactions between vertices, but how well depends on graph structure.
Many applications of machine learning involve the analysis of large data frames-matrices collecting heterogeneous measurements (binary, numerical, counts, etc.) across samples-with missing values. Low-rank models, as studied by Udell et al. [30], are popular in this framework for tasks such as visualization, clustering…
When estimating the relevancy between a query and a document, ranking models largely neglect the mutual information among documents. A common wisdom is that if two documents are similar in terms of the same query, they are more likely to have similar relevance score. To mitigate this problem, in this paper, we propose …
A new method for decomposing non-negative tensors using energy-based modeling.
Ranking items to be recommended to users is one of the main problems in large scale social media applications. This problem can be set up as a multi-objective optimization problem to allow for trading off multiple, potentially conflicting objectives (that are driven by those items) against each other. Most previous app…
Bayesian principles improve neural additive models for better feature selection and uncertainty.
Ising models describe the joint probability distribution of a vector of binary feature variables. Typically, not all the variables interact with each other and one is interested in learning the presumably sparse network structure of the interacting variables. However, in the presence of latent variables, the convention…
Investigate the evolving structure of cryptocurrency interactions using high-frequency returns.
Scalable model for slate recommendation learns reward probabilities.
A new method reduces inference cost for FwFM by allowing it to scale with item fields only.
iKF method uncovers complex variable interactions for scientific discovery.
The paper introduces a penalized matrix estimation procedure aiming at solutions which are sparse and low-rank at the same time. Such structures arise in the context of social networks or protein interactions where underlying graphs have adjacency matrices which are block-diagonal in the appropriate basis. We introduce…
Protein Thoughts interprets protein interactions with clear reasoning, improving prediction accuracy.
Generative model reveals hidden interaction preferences in networks.
Neural interaction discoveries can be real or artifacts of model flexibility.
We consider the problem of unveiling the implicit network structure of node interactions (such as user interactions in a social network), based only on high-frequency timestamps. Our inference is based on the minimization of the least-squares loss associated with a multivariate Hawkes model, penalized by and t…
The paper analyzes matrix completion with unlabeled implicit feedback and provides error bounds.
Advertising and feed ranking are essential to many Internet companies such as Facebook and Sina Weibo. Among many real-world advertising and feed ranking systems, click through rate (CTR) prediction plays a central role. There are many proposed models in this field such as logistic regression, tree based models, factor…
Framework for joint inference of network topology and interaction types in heterogeneous systems.
Tensor decomposition is an important technique for capturing the high-order interactions among multiway data. Multi-linear tensor composition methods, such as the Tucker decomposition and the CANDECOMP/PARAFAC (CP), assume that the complex interactions among objects are multi-linear, and are thus insufficient to repres…
We conjecture that satellite operations are either constant or have infinite rank in the concordance group. We reduce this to the difficult case of winding number zero satellites, and use gauge theory to provide a general criterion sufficient for the image of a satellite operation to generate an infinite rank s…
Study shows attention-style models learn pairwise interactions efficiently.
New estimator GMIPS reduces variance in ranking policy evaluation.
Interactive user interfaces need to continuously evolve based on the interactions that a user has (or does not have) with the system. This may require constant exploration of various options that the system may have for the user and obtaining signals of user preferences on those. However, such an exploration, especiall…
Paper introduces models to discover complex structures in large hypergraphs.
Computational approaches to drug discovery can reduce the time and cost associated with experimental assays and enable the screening of novel chemotypes. Structure-based drug design methods rely on scoring functions to rank and predict binding affinities and poses. The ever-expanding amount of protein-ligand binding an…
The paper analyzes tensor recovery from symmetric rank-one measurements using information theory.
A non-parametric method for ranking stock indices according to their mutual causal influences is presented. Under the assumption that indices reflect the underlying economy of a country, such a ranking indicates which countries exert the most economic influence in an examined subset of the global economy. The proposed …
Rank concepts help explain deep learning's effectiveness.
We study consumption behaviour in systems with heterogeneous interacting agents. Two different models are introduced, respectively with long and short range interactions among agents. At any time step an agent decides whether or not to consume a good, doing so if this provides positive utility. Utility is affected by i…
AdaEnsemble learns adaptive feature interactions for CTR prediction.
We propose RoBiRank, a ranking algorithm that is motivated by observing a close connection between evaluation metrics for learning to rank and loss functions for robust classification. The algorithm shows a very competitive performance on standard benchmark datasets against other representative algorithms in the litera…
Paper proposes unbiased learning for recommendation causal effects.
The paper uses belief propagation to analyze rankings and partial orders from partial information.
Improves CRRR for better mobility analysis with DCTM.
Paper proposes CounterSample to improve convergence in LTR models.
Analyzes the structure and rank of neural network Hessians.