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.
MPC framework reduces execution costs and schedule deviations in trading.
Simple bounds for covariance and Gram matrices across various settings.
Sharp concentration bounds for i.i.d. variables.
New risk class penalizes loss deviations from mean on both sides.
SAM improves generalization in overparameterized models, but its behavior in tensorized models is less understood.
This study improves audit sampling by using sequential procedures with statistical guarantees.
Proposes a two-stage method for testing variable interactions with FDR control.
We introduce a method for non-uniform random number generation based on sampling a physical process in a controlled environment. We demonstrate one proof-of-concept implementation of the method that reduces the error of Monte Carlo integration of a univariate Gaussian by 1068 times while doubling the speed of the Monte…
The aim of this paper is to generalize the PAC-Bayesian theorems proved by Catoni in the classification setting to more general problems of statistical inference. We show how to control the deviations of the risk of randomized estimators. A particular attention is paid to randomized estimators drawn in a small neighbor…
We provide a direct proof of Cramér's theorem for geodesic random walks in a complete Riemannian manifold . We show how to exploit the vector space structure of the tangent spaces to study large deviation properties of geodesic random walks in . Furthermore, we reveal the geometric obstructions one runs into …
We consider a long-term optimal investment problem where an investor tries to minimize the probability of falling below a target growth rate. From a mathematical viewpoint, this is a large deviation control problem. This problem will be shown to relate to a risk-sensitive stochastic control problem for a sufficiently l…
Three training methods for language models are shown to be variations of one another.
Interpolating models can have heavy-tailed risk, leading to rare but severe errors.
Control charts have traditionally been used in industrial statistics, but are constantly seeing new areas of application, especially in the age of Industry 4.0. This paper introduces a new method, which is suitable for applications in the healthcare sector, especially for monitoring a health-characteristic of a patient…
Distribution grids are currently challenged by frequent voltage excursions induced by intermittent solar generation. Smart inverters have been advocated as a fast-responding means to regulate voltage and minimize ohmic losses. Since optimal inverter coordination may be computationally challenging and preset local contr…
Study develops a data-based model for in-cylinder pressure and cyclic variations in RCCI engines.
Proves inequality linking function deviation to gradient norm on compact manifolds.
Risk control and optimal diversification constitute a major focus in the finance and insurance industries as well as, more or less consciously, in our everyday life. We present a discussion of the characterization of risks and of the optimization of portfolios that starts from a simple illustrative model and ends by a …
We propose an iterative scheme for feature-based positioning using a new weighted dissimilarity measure with the goal of reducing the impact of large errors among the measured or modeled features. The weights are computed from the location-dependent standard deviations of the features and stored as part of the referenc…
New framework embeds generalization in learning dynamics using large deviation theory.
DQNs can approximate optimal Q-functions with high accuracy on compact sets.
New method uses Coulomb gases for Monte Carlo integration with reduced errors.
Vanishing gradients hinder reinforcement finetuning of language models.
We study the optimal execution of market and limit orders with permanent and temporary price impacts as well as uncertainty in the filling of limit orders. Our continuous-time model incorporates a trade speed limiter and a trader director to provide better control on the trading rates. We formulate a stochastic control…
Suppose centers are fit to points by heuristically minimizing the -means cost; what is the corresponding fit over the source distribution? This question is resolved here for distributions with bounded moments; in particular, the difference between the sample cost and distribution cost decays with $…
Paper introduces AIF for anomaly detection with variable feature sensitivity.
Paper relaxes symmetry conditions for universal feature selection in noisy data.
SRFE clarifies KL divergences without unifying learning frameworks.
Introduces Star-Shaped deviation measures for risk analysis.
Effective network congestion control strategies are key to keeping the Internet (or any large computer network) operational. Network congestion control has been dominated by hand-crafted heuristics for decades. Recently, ReinforcementLearning (RL) has emerged as an alternative to automatically optimize such control str…
Unified framework for FDR control in knockoffs, validating Gaussian knockoffs.
This survey reviews portfolio selection problem for long-term horizon. We consider two objectives: (i) maximize the probability for outperforming a target growth rate of wealth process (ii) minimize the probability of falling below a target growth rate. We study the asymptotic behavior of these criteria formulated as l…
Since exchange economy considerably varies in the market assets, asset prices have become an attractive research area for investigating and modeling ambiguous and uncertain information in today markets. This paper proposes a new generative uncertainty mechanism based on the Bayesian Inference and Correntropy (BIC) tech…
For a stochastic factor model we maximize the long-term growth rate of robust expected power utility with parameter . Using duality methods the problem is reformulated as an infinite time horizon, risk-sensitive control problem. Our results characterize the optimal growth rate, an optimal long-term trading s…
Paper develops robust methods for large-scale testing without tuning parameters.
Paper tackles offline CMDP problems with near-optimal algorithm and sample complexity bound.
Paper characterizes monotonic mean-deviation risk measures.
Paper introduces a new project control method using Monte Carlo and statistical learning.
We consider compressed sensing formulated as a minimization problem of nonconvex sparse penalties, Smoothly Clipped Absolute deviation (SCAD) and Minimax Concave Penalty (MCP). The nonconvexity of these penalties is controlled by nonconvexity parameters, and L1 penalty is contained as a limit with respect to these para…
A simplified model for fixed income portfolio optimisation.
We consider the problem of tracking a target whose dynamics is modeled by a continuous Itō semi-martingale. The aim is to minimize both deviation from the target and tracking efforts. We establish the existence of asymptotic lower bounds for this problem, depending on the cost structure. These lower bounds can be relat…
We extend previous large deviations results for the randomised Heston model to the case of moderate deviations. The proofs involve the Gärtner-Ellis theorem and sharp large deviations tools.
Paper proves large deviation principle for stochastic approximations.
MADE improves exploration in RL by maximizing deviation from explored regions.
Unified framework for portfolio optimization using gain PDF.
A new definition of continuous-time equilibrium controls is introduced. As opposed to the standard definition, which involves a derivative-type operation, the new definition parallels how a discrete-time equilibrium is defined, and allows for unambiguous economic interpretation. The terms "strong equilibria" and "weak …
In this paper we propose the notion of dynamic deviation measure, as a dynamic time-consistent extension of the (static) notion of deviation measure. To achieve time-consistency we require that a dynamic deviation measures satisfies a generalised conditional variance formula. We show that, under a domination condition,…
Study large deviations in life insurance portfolios without identical distributions.