arXiv research

Introduces new performance measures using scaled utility functions.

This paper studies stability of the exponential utility maximization when there are small variations on agent's utility function. Two settings are considered. First, in a general semimartingale model where random endowments are present, a sequence of utilities defined on R converges to the exponential utility. Under a …

Investment strategies for rank-dependent utility agents are derived in a continuous-time market.

New insights into risk aversion for complex decision models.

We examine Kreps' (2019) conjecture that optimal expected utility in the classic Black--Scholes--Merton (BSM) economy is the limit of optimal expected utility for a sequence of discrete-time economies that "approach" the BSM economy in a natural sense: The $n$th discrete-time economy is generated by a scaled $n$-step r…

Optimizes portfolios using CPT utility via convex optimization.

The paper confirms a conjecture about optimal expected utility in markets with insider information.

Specifying utility functions is a key step towards applying the discrete choice framework for understanding the behaviour processes that govern user choices. However, identifying the utility function specifications that best model and explain the observed choices can be a very challenging and time-consuming task. This …

Study scaling limits of utility indifference prices in discretized Bachelier model.

Study utility indifference pricing with delayed investment information in a Bachelier model.

New learning dynamics achieve fast convergence in games without needing to know utility scales.

Feedback alignment methods need to be evaluated for accuracy and gradient cosine similarity.

Study on utility maximization with Tsallis entropy in reinforcement learning.

Differentially private log-location-scale regression models improve privacy in statistical analysis.

New learning rates derived for Tikhonov-regularized problems without kernel assumptions.

We use the Minority Game as a testing frame for the problem of the emergence of diversity in socio-economic systems. For the MG with heterogeneous impacts, we show that the direct generalization of the usual agents' profit does not fit some real-world situations. As a typical example we use the traffic formulation of t…

Efficiently solves large-scale robust portfolio optimization problems.

The paper analyzes Tikhonov regularization in Hilbert scales for statistical inverse problems.

DET unifies geometric and functional alignment for high-dimensional scientific data.

Framework for online resource allocation using social welfare functions.

Study utility indifference pricing in a Bachelier model with small linear price impact.

New method calibrates noise for attack risk, improving ML model accuracy.

New method for fair resource allocation in AI-aware networks with unknown utility functions.

We develop a scale-invariant truncated Lévy (STL) process to describe physical systems characterized by correlated stochastic variables. The STL process exhibits Lévy stability for the probability density, and hence shows scaling properties (as observed in empirical data); it has the advantage that all moments are fini…

We consider convex-concave saddle point problems with a separable structure and non-strongly convex functions. We propose an efficient stochastic block coordinate descent method using adaptive primal-dual updates, which enables flexible parallel optimization for large-scale problems. Our method shares the efficiency an…

Proposes a text perturbation method using a Mahalanobis metric to balance privacy and utility.

The sheer scale of modern datasets has resulted in a dire need for summarization techniques that identify representative elements in a dataset. Fortunately, the vast majority of data summarization tasks satisfy an intuitive diminishing returns condition known as submodularity, which allows us to find nearly-optimal sol…

Hashing has been widely used for large-scale approximate nearest neighbor search because of its storage and search efficiency. Recent work has found that deep supervised hashing can significantly outperform non-deep supervised hashing in many applications. However, most existing deep supervised hashing methods adopt a …

Study on statistical inference for nonlinear stochastic approximation with Markovian data.

The maximum entropy principle can be used to assign utility values when only partial information is available about the decision maker's preferences. In order to obtain such utility values it is necessary to establish an analogy between probability and utility through the notion of a utility density function. According…

ConvNets improve nonstationary covariance estimation for large-scale spatial data.

Investigates conditions for risk or utility functionals to be sensitive to large losses.

A framework for eliciting utility functions from investor preferences.

Develops a Bayesian framework for portfolio choice with a new posterior distribution.

New method selects diffusion scales for graph wavelets.

Knowledge distillation is an effective technique that transfers knowledge from a large teacher model to a shallow student. However, just like massive classification, large scale knowledge distillation also imposes heavy computational costs on training models of deep neural networks, as the softmax activations at the la…

Generative models enhance BO for large batch optimization.

We introduce deep scale-spaces (DSS), a generalization of convolutional neural networks, exploiting the scale symmetry structure of conventional image recognition tasks. Put plainly, the class of an image is invariant to the scale at which it is viewed. We construct scale equivariant cross-correlations based on a princ…

SVGP KAN integrates sparse variational GP with KANs for scalable probabilistic inference.

The multi-scale, mutli-physics nature of fusion plasmas makes predicting plasma events challenging. Recent advances in deep convolutional neural network architectures (CNN) utilizing dilated convolutions enable accurate predictions on sequences which have long-range, multi-scale characteristics, such as the time-series…

This paper studies the trading volumes and wealth distribution of a novel agent-based model of an artificial financial market. In this model, heterogeneous agents, behaving according to the Von Neumann and Morgenstern utility theory, may mutually interact. A Tobin-like tax (TT) on successful investments and a flat tax …

Large-batch stochastic gradient descent (SGD) is widely used for training in distributed deep learning because of its training-time efficiency, however, extremely large-batch SGD leads to poor generalization and easily converges to sharp minima, which prevents naive large-scale data-parallel SGD (DP-SGD) from convergin…

Two major financial market complexities are transaction costs and uncertain volatility, and we analyze their joint impact on the problem of portfolio optimization. When volatility is constant, the transaction costs optimal investment problem has a long history, especially in the use of asymptotic approximations when th…

Solves asset allocation for investors with utility functions and limits.

We model the logarithm of the price (log-price) of a financial asset as a random variable obtained by projecting an operator stable random vector with a scaling index matrix $\underline{\underline{E}}$ onto a non-random vector. The scaling index $\underline{\underline{E}}$ models prices of the individual financial asse…

Optimal portfolios are found for a wide range of utility functions under hyperbolic returns.

A new method extends Bayesian optimization to more models and utilities.

A key issue in the estimation of energy hedges is the hedgers' attitude towards risk which is encapsulated in the form of the hedgers' utility function. However, the literature typically uses only one form of utility function such as the quadratic when estimating hedges. This paper addresses this issue by estimating an…