arXiv research

Model captures external influences through random parameters and regime switching.

Dynamic treatment regimes are of growing interest across the clinical sciences as these regimes provide one way to operationalize and thus inform sequential personalized clinical decision making. A dynamic treatment regime is a sequence of decision rules, with a decision rule per stage of clinical intervention; each de…

Gradient descent converges to minimum Bayes risk for two-layer ReLU networks in mean field regime.

Study on rich regime training in deep learning, finding active parameters in bottom layers.

Unified framework detects change-points and estimates parameters in nonlinear systems with regime switching.

We propose a new procedure for inference on optimal treatment regimes in the model-free setting, which does not require to specify an outcome regression model. Existing model-free estimators for optimal treatment regimes are usually not suitable for the purpose of inference, because they either have nonstandard asympto…

A new algorithm learns model regimes and parameters efficiently.

We discuss the approximation of the value function for infinite-horizon discounted Markov Reward Processes (MRP) with nonlinear functions trained with the Temporal-Difference (TD) learning algorithm. We first consider this problem under a certain scaling of the approximating function, leading to a regime called lazy tr…

This paper presents the construction of a particle filter, which incorporates elements inspired by genetic algorithms, in order to achieve accelerated adaptation of the estimated posterior distribution to changes in model parameters. Specifically, the filter is designed for the situation where the subsequent data in on…

Lower bounds on private estimation of Gaussian covariance matrices.

Optimized portfolio management with dynamic market regimes using RL and OC learning.

The paper improves energy contract pricing models by incorporating jumps and varying parameters.

Paper analyzes and predicts Covid19 in Romania using neural networks and regime switching.

The paper aims to develop new combinatorial dimensions for bounded memory learning.

The paper identifies conditions for trend reversal in classification tasks.

Three training regimes found for scale-invariant neural networks on the sphere.

Optimal dividends strategy in a two-state regime-switching environment.

RAMBO optimizes multi-regime problems by discovering and modeling distinct energy basins.

Bayesian method detects change points in time series data.

Log-normal continuous random cascades form a class of multifractal processes that has already been successfully used in various fields. Several statistical issues related to this model are studied. We first make a quick but extensive review of their main properties and show that most of these properties can be analytic…

3D convolutional neural networks are difficult to train because they are parameter-expensive and data-hungry. To solve these problems we propose a simple technique for learning 3D convolutional kernels efficiently requiring less training data. We achieve this by factorizing the 3D kernel along the temporal dimension, r…

Bayesian neural networks reveal multimodal predictive distributions.

A hybrid framework for American option pricing under time-varying rough volatility.

Researchers adaptively analyze market regimes to reveal investor behavior shifts.

A new method for pricing European options in changing market conditions.

We study the dynamics of co-evolution of producers and customers described by bit-strings representing individual traits. Individual ''size-like'' properties are controlled by binary encounters which outcome depends upon a recognition process. Depending upon the parameter set-up, mutual selection of producers and custo…

The paper proposes a method to cluster data and estimate regression parameters using VI for financial forecasting.

NPAS trains neural networks with a fixed parameter budget, improving performance and compactness.

The paper addresses numerical integration issues in SV models, proposing a fast regime switching algorithm.

Generative model identifies temporal count data components with regime-dependent contributions.

New algorithm ensures global convergence in deep neural networks beyond NTK regime.

Study optimal portfolios in a non-Markovian regime-switching model with random time horizon.

The paper analyzes how over-parameterization affects reinforcement learning performance.

In this paper, we consider the problem of pricing discretely-sampled variance swaps based on a hybrid model of stochastic volatility and stochastic interest rate with regime-switching. Our modelling framework extends the Heston stochastic volatility model by including the CIR stochastic interest rate and model paramete…

Study bounds graph neural networks' over-parameterized error.

In this paper, we theoretically prove that the deep ReLU neural networks do not lie in spurious local minima in the loss landscape under the Neural Tangent Kernel (NTK) regime, that is, in the gradient descent training dynamics of the deep ReLU neural networks whose parameters are initialized by a normal distribution i…

Deep ReLU networks need Ω(N) parameters to interpolate at irregularly spaced points.

Paper proposes a new framework to compare trading strategies by accounting for market conditions.

EM algorithm achieves optimal sample complexity for learning two-component mixed linear regression.

We analyze operational risk in terms of a spin glass model. Several regimes are investigated, as a functions of the parameters that characterize the dynamics. The system is found to be robust against variations of these parameters. We unveil the presence of limit cycles and scrutinize the features of the asymptotic sta…

High-performing equity factor with Sharpe ratio above 13 out-of-sample.

Bayesian regression underestimates parameter uncertainties in noisy models.

This work shows linear convergence for two-layer neural networks in mean-field regime.

This paper uses RL and RNN to optimize trading with latent market information.

New risk measures incorporate economic states to assess crude oil derivatives.

Investigates optimal portfolio selection with regime-switching-induced stock price shocks.

Algorithm learns Sherrington-Kirkpatrick model parameters at low temperatures.

Optimizes consumption under regime-switching economic states with risk-sensitive preferences.