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.
This paper explains how deep learning performs hierarchical learning efficiently.
Backward SDEs help price XVA for OTC derivatives.
U-turn chains improve sampling from complex distributions.
Study on feature learning dynamics in infinite-depth neural networks, focusing on ResNets.
New HMC method handles features in POS tagging, outperforming MEMM.
In this era of big data, feature selection techniques, which have long been proven to simplify the model, makes the model more comprehensible, speed up the process of learning, have become more and more important. Among many developed methods, forward and stepwise feature selection regression remained widely used due t…
The aim of this short note is to fill in a gap in our earlier paper [16] on 2BSDEs with reflections, and to explain how to correct the subsequent results in the second paper [15]. We also provide more insight on the properties of 2RBSDEs, in the light of the recent contributions [13, 23] in the so--called framework…
We propose a new forward-backward stochastic differential equation solver for high-dimensional derivatives pricing problems by combining deep learning solver with least square regression technique widely used in the least square Monte Carlo method for the valuation of American options. Our numerical experiments demonst…
New method corrects bias in stochastic gradient samplers.
Study loop corrections in random feature models affecting training and test errors.
New Hessian estimates for heat equations on manifolds.
Pricing Bermudan swaptions with few exercise dates using analytic methods.
A new method ranks and selects features without model fitting.
This paper proposes a new evaluation metric and boosting method for weight separability in neural network design. In contrast to general visual recognition methods designed to encourage both intra-class compactness and inter-class separability of latent features, we focus on estimating linear independence of column vec…
We consider forward-backward greedy algorithms for solving sparse feature selection problems with general convex smooth functions. A state-of-the-art greedy method, the Forward-Backward greedy algorithm (FoBa-obj) requires to solve a large number of optimization problems, thus it is not scalable for large-size problems…
This work uses adversarial learning to detect and correct feature shifts in various datasets.
Proposes a progressive label correction method for feature-dependent label noise.
STAS selects optimal spatio-temporal scales for bias correction in precipitation forecasts.
Paper proves estimates for heat and conjugate heat equations under Ricci flow, leading to monotonicity of parabolic frequencies.
Study numerical methods for singular FBSDEs with degenerate forward component.
New framework selects key features for better query performance prediction.
This article presents a generic model for pricing financial derivatives subject to counterparty credit risk. Both unilateral and bilateral types of credit risks are considered. Our study shows that credit risk should be modeled as American style options in most cases, which require a backward induction valuation. To co…
Random feature model shows slow self-correction of generalization gap.
Paper finds a new principle for optimizing consumption and wealth using Tsallis entropy.
A new framework for fair representation learning using correction vectors.
In this study, we propose a novel deep neural network and its supervised learning method that uses a feedforward supervisory signal. The method is inspired by the human visual system and performs human-like association-based learning without any backward error propagation. The feedforward supervisory signal that produc…
Proposes log density gradient to improve reinforcement learning sample complexity.
A model corrects Lithuanian grammatical errors.
How can neural networks such as ResNet efficiently learn CIFAR-10 with test accuracy more than 96%, while other methods, especially kernel methods, fall relatively behind? Can we more provide theoretical justifications for this gap? Recently, there is an influential line of work relating neural networks to kernels in t…
Proposes a model combining difference-attention and error-correction LSTMs for improved time series prediction.
We propose a modification that corrects for split-improvement variable importance measures in Random Forests and other tree-based methods. These methods have been shown to be biased towards increasing the importance of features with more potential splits. We show that by appropriately incorporating split-improvement as…
New estimator corrects bias in CKA for sparsely sampled neurons.
We consider the mean-variance hedging problem under partial Information. The underlying asset price process follows a continuous semimartingale and strategies have to be constructed when only part of the information in the market is available. We show that the initial mean variance hedging problem is equivalent to a ne…
New methods ensure feature importance rankings are correct with high probability.
Mutual information has been successfully adopted in filter feature-selection methods to assess both the relevancy of a subset of features in predicting the target variable and the redundancy with respect to other variables. However, existing algorithms are mostly heuristic and do not offer any guarantee on the proposed…
We propose a novel algorithm which allows to sample paths from an underlying price process in a local volatility model and to achieve a substantial variance reduction when pricing exotic options. The new algorithm relies on the construction of a discrete multinomial tree. The crucial feature of our approach is that -- …
Forward-backward selection is one of the most basic and commonly-used feature selection algorithms available. It is also general and conceptually applicable to many different types of data. In this paper, we propose a heuristic that significantly improves its running time, while preserving predictive accuracy. The idea…
Deep model improves option pricing for CSI 300 index with sentiment and volatility features.
Paper presents a new backward deep BSDE method for solving nonlinear FBSDE problems.
Proposes a new algorithm for Sparse Bayesian Learning connected to Stepwise Regression.
Unified view of improving tree model interpretability and debiasing feature importance.
We adress the maximization problem of expected utility from terminal wealth. The special feature of this paper is that we consider a financial market where the price process of risky assets can have a default time. Using dynamic programming, we characterize the value function with a backward stochastic differential equ…
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…
The paper relaxes constraints on predictive coding models, making them more biologically plausible.
We study how finite Bayesian neural networks adapt their hidden representations.
In this paper, we study the evolving behaviors of the first eigenvalue of Laplace-Beltrami operator under the normalized backward Ricci flow, construct various quantities which are monotonic under the backward Ricci flow and get upper and lower bounds. We prove that in cases where the backward Ricci flow converges to a…
In this article, we look at the effect of volatility clustering on the risk indifference price of options described by Sircar and Sturm in their paper (Sircar, R., & Sturm, S. (2012). From smile asymptotics to market risk measures. Mathematical Finance. Advance online publication. doi:10.1111/mafi.12015). The indiffere…
New method corrects correlation bias in feature importance.