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.
Logic approach finds real singularities in differential equations.
Quantifier elimination enhances safety assurance of deep neural networks.
Unknot recognition is one of the fundamental questions in low dimensional topology. In this work, we show that this problem can be encoded as a validity problem in the existential fragment of the first-order theory of real closed fields. This encoding is derived using a well-known result on SU(2) representations of kno…
We prove that the first order theory of nonabelian free groups eliminates the "there exists infinitely many" quantifier (in eq). Equivalently, since the theory of nonabelian free groups is stable, it does not have the finite cover property. We also extend our results to torsion-free hyperbolic groups under some conditi…
New study shows non-adaptive trials can be outperformed by adaptive designs in treatment selection.
Let K be an algebraically closed field endowed with a complete non-archimedean norm with valuation ring R. Let f:Y -> X be a map of K-affinoid varieties. In this paper we study the analytic structure of the image f(Y) in X; such an image is a typical example of a subanalytic set. We show that the subanalytic sets are p…
Proposes a flexible tournament design combining knockout and round-robin.
New formula identifies and quantifies costs for automated market makers.
Improved elimination strategies for adaptive bandit identification reduce sample complexity and computational burden.
The curve graph's model theory reveals its central role in surface study.
The authors propose a parametric model called the arena model for prediction in paired competitions, i.e. paired comparisons with eliminations and bifurcations. The arena model has a number of appealing advantages. First, it predicts the results of competitions without rating many individuals. Second, it takes full adv…
Classification may not be reliable for several reasons: noise in the data, insufficient input information, overlapping distributions and sharp definition of classes. Faced with several possibilities neural network may in such cases still be useful if instead of a classification elimination of improbable classes is done…
RW-based learning is vulnerable to the Pac-Man attack, which eliminates active RWs.
Paper tackles inconsistent CATE estimation across group assignments.
New algorithm eliminates arms to minimize regret in complex bandit problems.
A risk of small defined-benefit pension schemes is that there are too few members to eliminate idiosyncratic mortality risk, that is there are too few members to effectively pool mortality risk. This means that when there are few members in the scheme, there is an increased risk of the liability value deviating signifi…
Learning how to act when there are many available actions in each state is a challenging task for Reinforcement Learning (RL) agents, especially when many of the actions are redundant or irrelevant. In such cases, it is sometimes easier to learn which actions not to take. In this work, we propose the Action-Elimination…
We introduce Neural Choice by Elimination, a new framework that integrates deep neural networks into probabilistic sequential choice models for learning to rank. Given a set of items to chose from, the elimination strategy starts with the whole item set and iteratively eliminates the least worthy item in the remaining …
Aims to eliminate domain bias in authentication without domain labels.
Financial markets are exposed to systemic risk (SR), the risk that a major fraction of the system ceases to function, and collapses. It has recently become possible to quantify SR in terms of underlying financial networks where nodes represent financial institutions, and links capture the size and maturity of assets (l…
Bayesian method learns graph structures from Gaussian data efficiently.
New method for PINNs uncertainty quantification without prior distribution.
We simplify Khovanov homology for torus braids using Gaussian elimination.
Optimizes experiment design for causal structure learning in linear models with cycles.
A wide class of machine learning algorithms can be reduced to variable elimination on factor graphs. While factor graphs provide a unifying notation for these algorithms, they do not provide a compact way to express repeated structure when compared to plate diagrams for directed graphical models. To exploit efficient t…
We develop an approach for feature elimination in statistical learning with kernel machines, based on recursive elimination of features.We present theoretical properties of this method and show that it is uniformly consistent in finding the correct feature space under certain generalized assumptions.We present four cas…
Paper introduces a new metric to select optimal Graph Shift Operator for GNNs.
In this paper, we theoretically prove that adding one special neuron per output unit eliminates all suboptimal local minima of any deep neural network, for multi-class classification, binary classification, and regression with an arbitrary loss function, under practical assumptions. At every local minimum of any deep n…
Two new feature selection algorithms improve on RFE.
PoWER-BERT speeds up BERT inference by eliminating redundant word-vectors.
GPE algorithm optimizes nonparametric contextual bandits with efficient regret bounds.
Scientists and engineers rely on accurate mathematical models to quantify the objects of their studies, which are often high-dimensional. Unfortunately, high-dimensional models are inherently difficult, i.e. when observations are sparse or expensive to determine. One way to address this problem is to approximate the or…
Paper tackles online learning in large MDPs with low Bellman rank using AVE algorithm.
This study presents an ANWSER model (asset network systemic risk model) to quantify the risk of financial contagion which manifests itself in a financial crisis. The transmission of financial distress is governed by a heterogeneous bank credit network and an investment portfolio of banks. Bankruptcy reproductive ratio …
IntDC framework uncovers causal relationships from non-interventional data.
Single neuron learns predictive uncertainty in deep learning models.
TFB simplifies Bayesian LLM uncertainty estimation without extra training.
The paper compares LOCO and Shapley values for feature importance, highlighting their limitations and suggesting improvements.
We propose a computationally efficient wrapper feature selection method - called Autoencoder and Model Based Elimination of features using Relevance and Redundancy scores (AMBER) - that uses a single ranker model along with autoencoders to perform greedy backward elimination of features. The ranker model is used to pri…
Probabilistic graphical models offer a powerful framework to account for the dependence structure between variables, which is represented as a graph. However, the dependence between variables may render inference tasks intractable. In this paper we review techniques exploiting the graph structure for exact inference, b…
We present a provably optimal differentially private algorithm for the stochastic multi-arm bandit problem, as opposed to the private analogue of the UCB-algorithm [Mishra and Thakurta, 2015; Tossou and Dimitrakakis, 2016] which doesn't meet the recently discovered lower-bound of [Shar…
Paper proposes a method to prune neural networks, reducing storage and computation costs.
ACI identifies cause-effect relationships and causal influence ranges in dynamical systems.
A new algorithm optimizes local objectives in federated learning with heterogeneous clients.
A new framework uses uncertainty to learn from raw data without explicit models.
New estimators outperform maximum likelihood without hyper-parameter estimation.
Correlation filters (CFs) are a class of classifiers that are attractive for object localization and tracking applications. Traditionally, CFs have been designed in the frequency domain using the discrete Fourier transform (DFT), where correlation is efficiently implemented. However, existing CF designs do not account …
Paper eliminates warm-up phase for PO in linear MDPs, achieving optimal regret.