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.
New gossip algorithms improve robustness of rank-based statistics in decentralized systems.
This paper tackles ranking-based performance normalization for optimization algorithms.
New algorithm achieves almost exact graph matching in almost quadratic time.
An Atlas model is a rank-based system of continuous semimartingales for which the steady-state values of the processes follow a power law, or Pareto distribution. For a power law, the log-log plot of these steady-state values versus rank is a straight line. Zipf's law is a power law for which the slope of this line is …
Study consumption-investment problem in markets with rank-based returns.
Constructs rank-based continuous semimartingales for financial markets.
Rank-based metrics are some of the most widely used criteria for performance evaluation of computer vision models. Despite years of effort, direct optimization for these metrics remains a challenge due to their non-differentiable and non-decomposable nature. We present an efficient, theoretically sound, and general met…
Paper introduces a new method to compute pseudoinverse for ELM with large datasets.
The problem of active diagnosis arises in several applications such as disease diagnosis, and fault diagnosis in computer networks, where the goal is to rapidly identify the binary states of a set of objects (e.g., faulty or working) by sequentially selecting, and observing, (noisy) responses to binary valued queries. …
Develops a new fairness learning approach for multi-task regression models.
The paper critiques the ambiguity of rank-based evaluation methods for entity alignment and link prediction.
Study shows how market firm capitalization models converge to stochastic PDE solutions.
QS-BO optimizes functions using only rank-based feedback.
Reward collapse occurs when ranking-based reward models yield uniform rewards for different prompts.
We analyze different re-ranking algorithms for diversification and show that majority of them are based on maximizing submodular/modular functions from the class of parameterized concave/linear over modular functions. We study the optimality of such algorithms in terms of the `total curvature'. We also show that by adj…
There has been an increasing interest in testing the equality of large Pearson's correlation matrices. However, in many applications it is more important to test the equality of large rank-based correlation matrices since they are more robust to outliers and nonlinearity. Unlike the Pearson's case, testing the equality…
Efficient method for vertex embedding and community detection.
A new algorithm difFOCI improves feature learning from data.
In the seminal work [9], several macroscopic market observables have been introduced, in an attempt to find characteristics capturing the diversity of a financial market. Despite the crucial importance of such observables for investment decisions, a concise mathematical description of their dynamics has been missing. W…
New method improves compatibility of risk stratification models without sacrificing accuracy.
We consider systems of diffusion processes ("particles") interacting through their ranks (also referred to as "rank-based models" in the mathematical finance literature). We show that, as the number of particles becomes large, the process of fluctuations of the empirical cumulative distribution functions converges to t…
New method for learning causal relationships in PNL models.
This article describes the R package varrank. It has a flexible implementation of heuristic approaches which perform variable ranking based on mutual information. The package is particularly suitable for exploring multivariate datasets requiring a holistic analysis. The core functionality is a general implementation of…
This paper improves model robustness to underrepresented groups using ranking metrics and reweighting.
LxCIM metric improves binary classification performance evaluation.
Improved MORE algorithm reduces regret in black-box optimization and RL tasks.
We discuss a natural game of competition and solve the corresponding mean field game with \emph{common noise} when agents' rewards are \emph{rank dependent}. We use this solution to provide an approximate Nash equilibrium for the finite player game and obtain the rate of convergence.
E-values enhance conformal prediction methods.
Proposes a new method to improve Bayesian computation accuracy using flexible classification.
Optimal privacy-preserving ranking from noisy comparisons.
Paper discusses extending Gini score for tied rankings and case weights.
A new framework for consistent segmentation evaluation reduces operating losses.
Cross-modal hashing has been receiving increasing interests for its low storage cost and fast query speed in multi-modal data retrievals. However, most existing hashing methods are based on hand-crafted or raw level features of objects, which may not be optimally compatible with the coding process. Besides, these hashi…
Modern retrieval systems are often driven by an underlying machine learning model. The goal of such systems is to identify and possibly rank the few most relevant items for a given query or context. Thus, such systems are typically evaluated using a ranking-based performance metric such as the area under the precision-…
A new Bayesian optimization method using Poisson process for better noise robustness.
We propose a novel non-parametric adaptive anomaly detection algorithm for high dimensional data based on rank-SVM. Data points are first ranked based on scores derived from nearest neighbor graphs on n-point nominal data. We then train a rank-SVM using this ranked data. A test-point is declared as an anomaly at alpha-…
Object ranking or "learning to rank" is an important problem in the realm of preference learning. On the basis of training data in the form of a set of rankings of objects represented as feature vectors, the goal is to learn a ranking function that predicts a linear order of any new set of objects. In this paper, we pr…
RCPO uses ranked choice modeling for better LLM alignment.
Extreme Multi-label classification (XML) is an important yet challenging machine learning task, that assigns to each instance its most relevant candidate labels from an extremely large label collection, where the numbers of labels, features and instances could be thousands or millions. XML is more and more on demand in…
Throughout science and technology, receiver operating characteristic (ROC) curves and associated area under the curve (AUC) measures constitute powerful tools for assessing the predictive abilities of features, markers and tests in binary classification problems. Despite its immense popularity, ROC analysis has been su…
This paper presents privileged multi-label learning (PrML) to explore and exploit the relationship between labels in multi-label learning problems. We suggest that for each individual label, it cannot only be implicitly connected with other labels via the low-rank constraint over label predictors, but also its performa…
New model predicts stock performance in large equity markets.
Matrix factorization is a well-studied task in machine learning for compactly representing large, noisy data. In our approach, instead of using the traditional concept of matrix rank, we define a new notion of link-rank based on a non-linear link function used within factorization. In particular, by applying the round …
Study ranks of elliptic curves via prime averages.
New metrics reveal oversmoothing in GNNs more accurately than traditional methods.
Unified multitask learning framework for mixed-type outcomes.
Rank-based Bayesian Optimization improves molecule selection in chemical systems.
Gini index needs auto-calibration for consistent decision-making.