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.
RQMC improves QMC by providing practical error bounds for financial applications.
Addresses theoretical and practical aspects of Gaussian differential privacy.
This paper explores AutoML for practical business applications.
This paper applies Heath-Jarrow-Morton framework to energy markets for practical use.
Complex network theory has been applied to solving practical problems from different domains. In this paper, we present a general framework for complex network applications. The keys of a successful application are a thorough understanding of the real system and a correct mapping of complex network theory to practical …
New method solves sparse deconvolution problems with theoretical guarantees and practical applications.
A practical one-shot federated learning algorithm for cross-silo setting.
TradeR uses RL to execute trades in real markets, minimizing surprise and catastrophe.
One of the main practical applications of quasi-Monte Carlo (QMC) methods is the valuation of financial derivatives. We aim to give a short introduction into option pricing and show how it is facilitated using QMC. We give some practical examples for illustration.
The extreme event statistics plays a very important role in the theory and practice of time series analysis. The reassembly of classical theoretical results is often undermined by non-stationarity and dependence between increments. Furthermore, the convergence to the limit distributions can be slow, requiring a huge am…
This article reviews forecasting theory and practice.
Good sparse approximations are essential for practical inference in Gaussian Processes as the computational cost of exact methods is prohibitive for large datasets. The Fully Independent Training Conditional (FITC) and the Variational Free Energy (VFE) approximations are two recent popular methods. Despite superficial …
In recent years, multi-armed bandit (MAB) framework has attracted a lot of attention in various applications, from recommender systems and information retrieval to healthcare and finance, due to its stellar performance combined with certain attractive properties, such as learning from less feedback. The multi-armed ban…
The paper introduces staged event trees for transparent treatment effect estimation.
In this work, we describe practical lessons we have learned from successfully using contextual bandits (CBs) to improve key business metrics of the Microsoft Virtual Agent for customer support. While our current use cases focus on single step einforcement learning (RL) and mostly in the domain of natural language proce…
Joint state and parameter estimation is a core problem for dynamic Bayesian networks. Although modern probabilistic inference toolkits make it relatively easy to specify large and practically relevant probabilistic models, the silver bullet---an efficient and general online inference algorithm for such problems---remai…
In the Best- identification problem (Best--Arm), we are given stochastic bandit arms with unknown reward distributions. Our goal is to identify the arms with the largest means with high confidence, by drawing samples from the arms adaptively. This problem is motivated by various practical applications and…
Causal discovery algorithms infer causal relations from data based on several assumptions, including notably the absence of measurement error. However, this assumption is most likely violated in practical applications, which may result in erroneous, irreproducible results. In this work we show how to obtain an upper bo…
Survey of challenges and future directions in applying RL to real-world settings.
The application of stochastic variance reduction to optimization has shown remarkable recent theoretical and practical success. The applicability of these techniques to the hard non-convex optimization problems encountered during training of modern deep neural networks is an open problem. We show that naive application…
New framework provides privacy guarantees for practical federated learning.
The diverse world of machine learning applications has given rise to a plethora of algorithms and optimization methods, finely tuned to the specific regression or classification task at hand. We reduce the complexity of algorithm design for machine learning by reductions: we develop reductions that take a method develo…
The NNGP kernel's predictions closely match those of the Matern kernel under certain conditions.
Optimization learns as it goes, improving with experience.
Gluon optimizes LMO-based methods for large-scale tasks, improving performance and theory-practice gap.
Paper improves sample efficiency of transfer learning in diffusion models.
Paper addresses RLHF alignment challenges with novel algorithms.
Proposes guidelines for developing medical AI products.
The Normal Means problem plays a fundamental role in many areas of modern high-dimensional statistics, both in theory and practice. And the Empirical Bayes (EB) approach to solving this problem has been shown to be highly effective, again both in theory and practice. However, almost all EB treatments of the Normal Mean…
We developed an efficient algorithm to factorize knots.
The paper discusses various practical consequences of treating economics and finance as an inherently dynamic and chaotic system. On the theoretical side this looks at the general applicability of the market-making pricing approach to economics in general. The paper also discuses the consequences of the endogenous crea…
Safe-EF improves federated learning for non-smooth, constrained optimization.
We propose a new concept named adaptive submodularity ratio to study the greedy policy for sequential decision making. While the greedy policy is known to perform well for a wide variety of adaptive stochastic optimization problems in practice, its theoretical properties have been analyzed only for a limited class of p…
While crowdsourcing has become an important means to label data, there is great interest in estimating the ground truth from unreliable labels produced by crowdworkers. The Dawid and Skene (DS) model is one of the most well-known models in the study of crowdsourcing. Despite its practical popularity, theoretical error …
Paper shows how to quantify uncertainty in medical ML models.
We compute Khovanov homology for tangles using TQFT.
We aim to analyze the relation between two random vectors that may potentially have both different number of attributes as well as realizations, and which may even not have a joint distribution. This problem arises in many practical domains, including biology and architecture. Existing techniques assume the vectors to …
Measuring Mutual Information (MI) between high-dimensional, continuous, random variables from observed samples has wide theoretical and practical applications. Recent work, MINE (Belghazi et al. 2018), focused on estimating tight variational lower bounds of MI using neural networks, but assumed unlimited supply of samp…
Proposes a flexible method for learning latent causal representations.
Simplifies residual flows to make flow-based modeling more practical.
As a testament to their success, the theory of random forests has long been outpaced by their application in practice. In this paper, we take a step towards narrowing this gap by providing a consistency result for online random forests.
Hypersolvers enable fast continuous-depth models for practical applications.
This thesis formalizes metric selection for machine learning applications.
New algorithm ensures global convergence in deep neural networks beyond NTK regime.
Accelerates coordinate descent methods for machine learning problems.
This paper bridges the gap between theoretical and practical OPE for bandit problems.
The Heston model is validated for option pricing using theoretical derivations and empirical market data.
The study examines how experimental design choices affect machine learning model performance.