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.
Gradient methods learn a single neuron under mild assumptions.
Bayesian model selection improves causal discovery in complex datasets.
Unified theory for kernel regression generalizes well under realistic assumptions.
Examines optimal risk sharing with realistic risk attitudes, finding risk seeking in certain subdomains.
Paper generalizes teacher-student model for realistic data.
Characterizes Hessian eigenspectra for realistic nonlinear models.
Enhances queue-reactive model for realistic limit order book simulation.
We introduce a geometrically transparent strict saddle property for nonsmooth functions. This property guarantees that simple proximal algorithms on weakly convex problems converge only to local minimizers, when randomly initialized. We argue that the strict saddle property may be a realistic assumption in applications…
We derive and analyze a new, efficient, pool-based active learning algorithm for halfspaces, called ALuMA. Most previous algorithms show exponential improvement in the label complexity assuming that the distribution over the instance space is close to uniform. This assumption rarely holds in practical applications. Ins…
A financial market model where agents trade using realistic combinations of buy-and-hold strategies is considered. Minimal assumptions are made on the discounted asset-price process - in particular, the semimartingale property is not assumed. Via a natural market viability assumption, namely, absence of arbitrages of t…
In recent years the possibility of relaxing the so-called Faithfulness assumption in automated causal discovery has been investigated. The investigation showed (1) that the Faithfulness assumption can be weakened in various ways that in an important sense preserve its power, and (2) that weakening of Faithfulness may h…
Evaluates transfer learning methods in dynamic data availability scenarios.
Study membership inference under skewed priors and adaptive thresholds, improving attack accuracy.
When modelling stock market dynamics, the price formation is often based on an equilbrium mechanism. In real stock exchanges, however, the price formation is goverend by the order book. It is thus interesting to check if the resulting stylized facts of a model with equilibrium pricing change, remain the same or, more g…
We introduce an equilibrium asset pricing model, which we build on the relationship between a novel risk measure, the Expected Downside Risk (EDR) and the expected return. On the one hand, our proposed risk measure uses a nonparametric approach that allows us to get rid of any assumption on the distribution of returns.…
Classification problems in security settings are usually contemplated as confrontations in which one or more adversaries try to fool a classifier to obtain a benefit. Most approaches to such adversarial classification problems have focused on game theoretical ideas with strong underlying common knowledge assumptions, w…
Paper proposes a new method to simulate realistic markets from data.
The paper examines prediction and estimation risks of ridgeless least squares under general error assumptions.
Adam converges to stationary points under relaxed conditions.
New bucketing scheme improves Byzantine robustness for heterogeneous data.
We study the problem of using i.i.d. samples from an unknown multivariate probability distribution to estimate the mutual information of . This problem has recently received attention in two settings: (1) where is assumed to be Gaussian and (2) where is assumed only to lie in a large nonparametric smooth…
New method extrapolates spectral densities from smaller models to larger ones.
Optimal liquidation strategy for a risk-averse investor in a one-sided limit order book driven by a Levy process.
Matrix completion is a well-studied problem with many machine learning applications. In practice, the problem is often solved by non-convex optimization algorithms. However, the current theoretical analysis for non-convex algorithms relies heavily on the assumption that every entry is observed with exactly the same pro…
We introduce Deep Variational Bayes Filters (DVBF), a new method for unsupervised learning and identification of latent Markovian state space models. Leveraging recent advances in Stochastic Gradient Variational Bayes, DVBF can overcome intractable inference distributions via variational inference. Thus, it can handle …
We analyze a negative-parameter variant of the diversity-weighted portfolio studied by Fernholz, Karatzas, and Kardaras (Finance Stoch 9(1):1-27, 2005), which invests in each company a fraction of wealth inversely proportional to the company's market weight (the ratio of its capitalization to that of the entire market)…
Improved peak detection in ChIP-seq data reduces over-dispersion.
A new method generates synthetic data with realistic marginal distributions.
Optimization methods are used to determine equilibria of investment in cryptocurrencies. The basic assumptions involve existence of a core group (the "wealthy") that fears the loss of substantial assets through government seizure. Speculators constitute another group that tends to introduce volatility and risk for the …
Inverse classification, the process of making meaningful perturbations to a test point such that it is more likely to have a desired classification, has previously been addressed using data from a single static point in time. Such an approach yields inflated probability estimates, stemming from an implicitly made assum…
Before the massive spread of computer technology, information was far from complex. The development of technology shifted the paradigm: from individuals who faced scarce and costly information to individuals who face massive amounts of information accessible at low costs. Nowadays we are living in the era of big data a…
A new method for optimizing hyperparameters using conformalized quantile regression.
New method AnInfoNCE uncovers latent factors in contrastive learning with practical variability.
We describe a layer-by-layer algorithm for training deep convolutional networks, where each step involves gradient updates for a two layer network followed by a simple clustering algorithm. Our algorithm stems from a deep generative model that generates mages level by level, where lower resolution images correspond to …
This paper evaluates knowledge graph completion models under the open-world assumption, revealing unexpected behavior of metrics.
One of the shortcomings of the Black and Scholes model on option pricing is the assumption that trading of the underlying asset does not affect the price of that asset. This assumption can be fulfilled only in perfectly liquid markets. Since most markets are illquid, this assumption might be too restrictive. Thus, taki…
Characterizes RFF regression in large setting, providing precise learning phases and double descent curve.
Deep neural networks struggle with numerical instability during training.
Test verifies if data meets SCAR assumption for PU learning.
We consider a structural model where the survival/default state is observed together with a noisy version of the firm value process. This assumption makes the model more realistic than most of the existing alternatives, but triggers important challenges related to the computation of conditional default probabilities. I…
Gradient span algorithms show consistent progress in high dimensions.
This paper uses deep reinforcement learning to optimize stock portfolios considering transaction costs and risks.
Transformers learn to recall with non-orthogonal embeddings in realistic settings.
New bounds for generative models under weaker assumptions.
Study validates Libor model for insurance benefits calculation.
This paper is motivated by the non-linear stability problem for the expanding region of Kerr de Sitter cosmologies in the context of Einstein's equations with positive cosmological constant. We show that under dynamically realistic assumptions the conformal Weyl curvature of the spacetime decays towards future null inf…
Pseudo rehearsal uses non-photo-realistic images to save resources without sacrificing performance.
This work evaluates PDA methods without target labels, revealing significant accuracy drops.