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.
Generative model reveals hidden interaction preferences in networks.
Generates low-dimensional node vectors for graphs with privacy while preserving structural preferences.
Large-scale graph data in real-world applications is often not static but dynamic, i. e., new nodes and edges appear over time. Current graph convolution approaches are promising, especially, when all the graph's nodes and edges are available during training. When unseen nodes and edges are inserted after training, it …
Understanding how users navigate in a network is of high interest in many applications. We consider a setting where only aggregate node-level traffic is observed and tackle the task of learning edge transition probabilities. We cast it as a preference learning problem, and we study a model where choices follow Luce's a…
Large-scale industrial recommender systems are usually confronted with computational problems due to the enormous corpus size. To retrieve and recommend the most relevant items to users under response time limits, resorting to an efficient index structure is an effective and practical solution. The previous work Tree-b…
Task offloading is an emerging technology in fog-enabled networks. It allows users to transmit tasks to neighbor fog nodes so as to utilize the computing resources of the networks. In this paper, we investigate a stochastic task offloading model and propose a multi-armed bandit framework to formulate this model. We con…
This work derives closed-form expressions computing the expectation of co-presence and of number of co-occurrences of nodes on paths sampled from a network according to general path weights (a bag of paths). The underlying idea is that two nodes are considered as similar when they often appear together on (preferably s…
Improves decentralized learning by teleporting active nodes for better convergence.
TechRank ranks companies and technologies based on investor preferences.
PGRec improves recommendation by modeling user-item preferences as a graph and embedding it for better predictions.
Link prediction (LP) algorithms propose to each node a ranked list of nodes that are currently non-neighbors, as the most likely candidates for future linkage. Owing to increasing concerns about privacy, users (nodes) may prefer to keep some of their connections protected or private. Motivated by this observation, our …
Random survival forests (RSF) are a powerful method for risk prediction of right-censored outcomes in biomedical research. RSF use the log-rank split criterion to form an ensemble of survival trees. The most common approach to evaluate the prediction accuracy of a RSF model is Harrell's concordance index for survival d…
Spectral clustering is widely used to partition graphs into distinct modules or communities. Existing methods for spectral clustering use the eigenvalues and eigenvectors of the graph Laplacian, an operator that is closely associated with random walks on graphs. We propose a new spectral partitioning method that exploi…
Increased data gathering capacity, together with the spread of data analytics techniques, has prompted an unprecedented concentration of information related to the individuals' preferences in the hands of a few gatekeepers. In the present paper, we show how platforms' performances still appear astonishing in relation t…
We design a self size-estimating feed-forward network (SSFN) using a joint optimization approach for estimation of number of layers, number of nodes and learning of weight matrices. The learning algorithm has a low computational complexity, preferably within few minutes using a laptop. In addition the algorithm has a l…
Study bandit problem on smooth graph functions for recommender systems.
Bandit problem on graphs aims to recommend items with high expected ratings.
We present Zeno, a technique to make distributed machine learning, particularly Stochastic Gradient Descent (SGD), tolerant to an arbitrary number of faulty workers. Zeno generalizes previous results that assumed a majority of non-faulty nodes; we need assume only one non-faulty worker. Our key idea is to suspect worke…
Model-based methods for recommender systems have been studied extensively in recent years. In systems with large corpus, however, the calculation cost for the learnt model to predict all user-item preferences is tremendous, which makes full corpus retrieval extremely difficult. To overcome the calculation barriers, mod…
This work develops a generic framework, called the bag-of-paths (BoP), for link and network data analysis. The central idea is to assign a probability distribution on the set of all paths in a network. More precisely, a Gibbs-Boltzmann distribution is defined over a bag of paths in a network, that is, on a representati…
The paper tackles a bandit problem on graphs with smooth functions, aiming to recommend items with high expected ratings.
Two regularization techniques improve GCNN explainability and preference from chemists.
In economic and financial networks, the strength of each node has always an important economic meaning, such as the size of supply and demand, import and export, or financial exposure. Constructing null models of networks matching the observed strengths of all nodes is crucial in order to either detect interesting devi…
A large GPS dataset reveals that a single route often covers 60% of travel observations.
New model detects communities in network data from edge nominations.
AutoLL uses neural networks to automatically reorder graph nodes for linear layouts.
Optimizes molecular generation for chemist preferences.
We consider the problem of optimal recovery of true ranking of items from a randomly chosen subset of their pairwise preferences. It is well known that without any further assumption, one requires a sample size of for the purpose. We analyze the problem with an additional structure of relational graph $G([…
New method adapts to user preferences dynamically, improving recommendation models.
The paper uses belief propagation to analyze rankings and partial orders from partial information.
Many real-world engineering problems rely on human preferences to guide their design and optimization. We present PrefOpt, an open source package to simplify sequential optimization tasks that incorporate human preference feedback. Our approach extends an existing latent variable model for binary preferences to allow f…
Enhances preference learning by incorporating response times into binary choices.
Bayesian optimization learns DM preferences for multi-outcome experiments.
New study shows personalized content recommendations can lead to polarization of user preferences.
Bayesian optimization agent learns user preferences from pairwise comparisons.
New RLHF framework handles general preference oracles without reward functions.
This paper studies robust forward investment and consumption preferences within a zero-volatility context. Different from previous works, we consider an incomplete financial market model due to general investment portfolio constraints. We provide a new PDE characterization and a novel semi-explicit saddle-point constru…
Decentralized ranking consensus via gossip for robust and scalable systems.
In preference-based reinforcement learning (RL), an agent interacts with the environment while receiving preferences instead of absolute feedback. While there is increasing research activity in preference-based RL, the design of formal frameworks that admit tractable theoretical analysis remains an open challenge. Buil…
Study on identifying most preferred policy in bandits with vector-valued rewards.
Stable and consistent model alignment for language models without assuming human preference models.
Dropping a tiny fraction of preferences can significantly alter the rankings of top LLMs.
Paper explores limits and possibilities of aligning LLMs with human preferences.
Paper improves parameter estimation of continuous distributions using preference feedback.
Paper investigates monotonicity issues in AI preference learning.
DOPL learns from preference feedback to solve RMAB problems.
Direct Density Ratio Optimization aligns LLMs with human preferences without assuming specific models.
Bayesian optimization with preference learning identifies preferred solutions in multi-objective problems.