arXiv research

Neural network approximates weakly efficient frontier of convex vector optimization problems.

Improved neural network approximates analytic and L^p functions efficiently.

We develop an efficient method to calibrate CDS spreads using asymptotic approximations.

We discuss Bayesian methods for learning Bayesian networks when data sets are incomplete. In particular, we examine asymptotic approximations for the marginal likelihood of incomplete data given a Bayesian network. We consider the Laplace approximation and the less accurate but more efficient BIC/MDL approximation. We …

We propose a stochastic approximation method for approximating the efficient frontier of chance-constrained nonlinear programs. Our approach is based on a bi-objective viewpoint of chance-constrained programs that seeks solutions on the efficient frontier of optimal objective value versus risk of constraint violation. …

New algorithm finds near-optimal policies efficiently in zero-sum games.

Deep networks can learn functions approximated by shallow networks, but not all functions.

This work improves sample efficiency in neural function approximation for reinforcement learning.

Efficiently selects predictors in sparse regression without approximations.

Neural approximate computing gains enormous energy-efficiency at the cost of tolerable quality-loss. A neural approximator can map the input data to output while a classifier determines whether the input data are safe to approximate with quality guarantee. However, existing works cannot maximize the invocation of the a…

Nyström KPCA balances computational efficiency and statistical accuracy.

Deep networks can approximate score functions in high-dimensional graphical models efficiently.

New method approximates CV efficiently for large-scale problems.

Proposes efficient Gaussian approximations for non-Gaussian likelihoods.

Efficient clustering for large datasets using a sampling-based approach.

Posterior refinement improves sample efficiency in Bayesian neural networks.

New efficient algorithm for approximate PML distribution.

Sumformer simplifies Transformers to handle long sequences efficiently.

Efficient offline reinforcement learning with neural networks using differentiable function approximation.

Efficient approximation lies at the heart of large-scale machine learning problems. In this paper, we propose a novel, robust maximum entropy algorithm, which is capable of dealing with hundreds of moments and allows for computationally efficient approximations. We showcase the usefulness of the proposed method, its eq…

The intrinsic error tolerance of neural network (NN) makes approximate computing a promising technique to improve the energy efficiency of NN inference. Conventional approximate computing focuses on balancing the efficiency-accuracy trade-off for existing pre-trained networks, which can lead to suboptimal solutions. In…

We describe a simple and efficient procedure for approximating the Lévy measure of a $\text{Gamma}(α,1)$ random variable. We use this approximation to derive a finite sum-representation that converges almost surely to Ferguson's representation of the Dirichlet process based on arrivals of a homogeneous Poisson process.…

The Hull-White one factor model is used to price interest rate options. The parameters of the model are often calibrated to simple liquid instruments, in particular European swaptions. It is therefore very important to have very efficient pricing formula for simple instruments. Such a formula is proposed here for Europ…

SIM-Shapley improves SV approximation efficiency and stability.

Develops efficient methods for approximating densities of financial models with jumps.

Efficiently private clustering algorithms with tight approximation ratios.

Efficiently approximates neural network function space distance.

Efficient algorithm approximates discrete random variables with minimal Kolmogorov distance.

Efficiently finds sparse solutions to max-plus equations for convex regression.

We utilize long-term memory, fractal dimension and approximate entropy as input variables for the Efficiency Index [Kristoufek & Vosvrda (2013), Physica A 392]. This way, we are able to comment on stock market efficiency after controlling for different types of inefficiencies. Applying the methodology on 38 stock marke…

Paper develops efficient RL algorithm for general value function approximation.

Efficiently reduces tensor ranks using mean-field approximation.

Unified view of GP approximations improves efficiency.

$Q$-learning with function approximation is one of the most popular methods in reinforcement learning. Though the idea of using function approximation was proposed at least 60 years ago, even in the simplest setup, i.e, approximating $Q$-functions with linear functions, it is still an open problem on how to design a pr…

New method for finite approximations improves inference efficiency and applicability.

Proposes sampling from reverse diffusion posteriors for contextual bandits.

VISA improves inference efficiency for complex models.

The LIBOR market model is very popular for pricing interest rate derivatives, but is known to have several pitfalls. In addition, if the model is driven by a jump process, then the complexity of the drift term is growing exponentially fast (as a function of the tenor length). In this work, we consider a Lévy-driven LIB…

Recently it was shown that the problem of Maximum Inner Product Search (MIPS) is efficient and it admits provably sub-linear hashing algorithms. Asymmetric transformations before hashing were the key in solving MIPS which was otherwise hard. In the prior work, the authors use asymmetric transformations which convert th…

Efficiently simulates SABR model with novel sampling methods.

This paper introduces a spline-based method for nonparametric ADVI that handles complex posterior distributions.

New RL method explores environments without rewards, achieving efficient policy generation.

Efficient algorithms for online learning with changing action sets, achieving no-approximate-regret guarantees.

Efficiently estimates shrinkage coefficient for RTME using LOOCV approximation.

Efficient simulation scheme for rough Heston model reduces computational cost.

A new method for efficient uncertainty estimation in deep learning models.

New findings on how convolutional architectures approximate time series data.

A new method for efficient Gaussian process inference using sparse approximations.