arXiv research

Paper tackles efficient evaluation of natural stochastic policies in offline RL.

Stochastic models analyze traffic network performance.

A new hybrid algorithm reduces stochastic gradient evaluations for nonconvex optimization.

This paper optimizes portfolio management in incomplete markets with stochastic factors, considering periodic wealth evaluations.

Adaptive replication improves stochastic function optimization.

In this paper we study the problem of minimizing the average of a large number ($n$) of smooth convex loss functions. We propose a new method, S2GD (Semi-Stochastic Gradient Descent), which runs for one or several epochs in each of which a single full gradient and a random number of stochastic gradients is computed, fo…

New approach for prudent risk evaluation using model aggregation.

Study variance-reduced method for estimating fixed points in Banach spaces.

Stochastic Bayesian Neural Network improves scalability and performance.

Proposes a new resampling method for off-policy evaluation in stochastic control.

Paper introduces a new multi-kernel algorithm for better gradient approximation.

The paper shows how shared random seeds can reduce variance in machine learning evaluations.

Stochastic optimization problems often involve the expectation in its objective. When risk is incorporated in the problem description as well, then risk measures have to be involved in addition to quantify the acceptable risk, often in the objective. For this purpose it is important to have an adjusted, adapted and eff…

Scalar dynamic risk measures for univariate positions in continuous time are commonly represented as backward stochastic differential equations. In the multivariate setting, dynamic risk measures have been defined and studied as families of set-valued functionals in the recent literature. There are two possible extensi…

Discretizations of Langevin diffusions provide a powerful method for sampling and Bayesian inference. However, such discretizations require evaluation of the gradient of the potential function. In several real-world scenarios, obtaining gradient evaluations might either be computationally expensive, or simply impossibl…

Study optimal portfolio management with periodic evaluations in stochastic models, considering convex constraints.

In this study, we develop a deterministic nonlinear filtering algorithm based on a high-dimensional version of Kitagawa (1987) to evaluate the likelihood function of models that allow for stochastic volatility and jumps whose arrival intensity is also stochastic. We show numerically that the deterministic filtering met…

BOSH optimizes functions with stochastic evaluations more efficiently and precisely.

New methods improve temporal difference learning for policy evaluation in Markov decision processes.

Paper proposes a new method to speed up diffusion models.

New simulation approaches to evaluating path-dependent options without matrix inversion issues nor Euler bias are evaluated. They employ three main contributions: Stochastic approximation replaces regression in the LSM algorithm; Explicit weak solutions to stochastic differential equations are developed and applied to …

Paper tackles offline SSP with value iteration for policy evaluation and learning.

Policy evaluation is a crucial step in many reinforcement-learning procedures, which estimates a value function that predicts states' long-term value under a given policy. In this paper, we focus on policy evaluation with linear function approximation over a fixed dataset. We first transform the empirical policy evalua…

New framework for evaluating ad auctions using stochastic modeling.

Stochastic zeroth-order (SZO), or gradient-free, optimization allows to optimize arbitrary functions by relying only on function evaluations under parameter perturbations, however, the iteration complexity of SZO methods suffers a factor proportional to the dimensionality of the perturbed function. We show that in scen…

Stochastic Stein Discrepancies improve inference efficiency.

Stochastic parameterizations account for uncertainty in the representation of unresolved sub-grid processes by sampling from the distribution of possible sub-grid forcings. Some existing stochastic parameterizations utilize data-driven approaches to characterize uncertainty, but these approaches require significant str…

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

We quantify content availability and user discovery opportunities in recommender systems.

This paper proposes a stochastic variant of a classic algorithm---the cubic-regularized Newton method [Nesterov and Polyak 2006]. The proposed algorithm efficiently escapes saddle points and finds approximate local minima for general smooth, nonconvex functions in only $\mathcal{\tilde{O}}(ε^{-3.5})$ stochastic gradien…

mAIS improves free energy evaluation efficiency.

Improved convergence for non-log-concave sampling.

Bayesian inference using stochastic neural networks ensembles.

Stochastic structured prediction under bandit feedback follows a learning protocol where on each of a sequence of iterations, the learner receives an input, predicts an output structure, and receives partial feedback in form of a task loss evaluation of the predicted structure. We present applications of this learning …

In this chapter, we consider volatility swap, variance swap and VIX future pricing under different stochastic volatility models and jump diffusion models which are commonly used in financial market. We use convexity correction approximation technique and Laplace transform method to evaluate volatility strikes and estim…

New algorithm solves complex optimization problems without needing projections.

Leveraging advances in variational inference, we propose to enhance recurrent neural networks with latent variables, resulting in Stochastic Recurrent Networks (STORNs). The model i) can be trained with stochastic gradient methods, ii) allows structured and multi-modal conditionals at each time step, iii) features a re…

The paper models financial asset prices with jumps and evaluates European option prices using numerical methods.

Paper proposes a weak approximation of reflection coupling for non-convex optimization.

Deep learning solves complex stochastic control with jumps.

We consider derivative-free algorithms for stochastic and non-stochastic convex optimization problems that use only function values rather than gradients. Focusing on non-asymptotic bounds on convergence rates, we show that if pairs of function values are available, algorithms for $d$-dimensional optimization that use …

Bayesian optimization reduces hyperparameter tuning cost for stochastic models.

A new method for high-dimensional RBDO using stochastic emulators.

Bayesian optimization tackles non-convex, two-stage stochastic problems efficiently.

In this paper, we prove some convergence results of a special case of optimistic policy iteration algorithm for stochastic shortest path problem. We consider both Monte Carlo and $TD(λ)$ methods for the policy evaluation step under the condition that the termination state will eventually be reached almost surely.

Many machine learning algorithms minimize a regularized risk, and stochastic optimization is widely used for this task. When working with massive data, it is desirable to perform stochastic optimization in parallel. Unfortunately, many existing stochastic optimization algorithms cannot be parallelized efficiently. In t…

New method reduces mixture model evaluation cost for large models.

New algorithm optimizes convex functions with noisy evaluations in one dimension.