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arXiv research

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.

168,695 papers · 148 categories

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105210315420 · Jun 202019922001200920172026
48 results for non-monotone information

Study derives new equation for reserves in non-monotone information scenarios.

problem Modeling reserves in situations where information is not always increasing.
method Infinitesimal approach to derive generalized stochastic Thiele equation.
result New equation allows for information discarding and solves open problems.

Study finds non-monotonic Value of Information in dynamic multi-market monopoly.

problem Investigates non-monotonicity in Value of Information for a price-setting monopolist.
method Uses a Bayesian inverse problem with Kalman-Bucy-Stratonovich filter in a dynamic discrete model.
result Non-monotonic relationship between signal variance and Value of Information.

New method speeds up model selection for complex scientific tasks.

problem Exhaustive model selection is computationally infeasible for large model spaces.
method Branch-and-bound algorithm with non-monotonic criteria.
result Guaranteed identification of optimal models with significant computational speedups.

Researchers propose a non-monotone quantum natural gradient for quantum systems.

problem Applying natural gradient methods to quantum systems without monotonicity.
method Introducing a non-monotone quantum natural gradient (QNG) and demonstrating its superiority over conventional QNG.
result Non-monotone QNG outperforms conventional QNG in terms of convergence speed.

Productivity and credit limits affect aggregate production in non-monotonic ways.

problem Understanding how aggregate production is influenced by individual characteristics and financial constraints.
method Analytical proof of non-monotonic effects of productivity and credit limits on aggregate production in a general equilibrium model.
result Equilibrium aggregate production can be non-monotonic in both individual productivity and credit limit.

New risk control method for non-monotonic losses in complex parameters.

problem Controlling risk for non-monotonic losses with multidimensional parameters.
method Stability-based guarantees for generic algorithms applied to non-monotonic losses.
result Guarantees depend on algorithm stability, with looser guarantees for unstable algorithms.

Learning performance can show non-monotonic behavior. That is, more data does not necessarily lead to better models, even on average. We propose three algorithms that take a supervised learning model and make it perform more monotone. We prove consistency and monotonicity with high probability, and evaluate the algorit…

2019-11-25abs ↗pdf ↗

GradaGrad adapts learning rate non-monotonically, overcoming AdaGrad's step size decrease.

problem Fixed learning rate in AdaGrad leads to step size decrease over time.
method Introduces GradaGrad, which grows or shrinks the learning rate based on a different accumulation in the denominator.
result GradaGrad achieves similar convergence rates as AdaGrad and demonstrates non-monotone adaptation.

Study learns a neuron with non-monotonic activation functions.

problem Learning a single neuron with non-monotonic activation functions.
method Gradient descent (GD) with conditions on activation function and input distribution.
result Learnability of non-monotonic activation functions is established without monotonicity assumption.

Study non-monotonic loss functions in CRC, achieving valid risk control with large calibration samples.

problem Non-monotonic loss functions in CRC, violating existing theory's monotonicity assumption.
method Finite grid selection, calibration sample size analysis, Lipschitz continuity, monotonicity, distribution shift.
result Valid CRC achieved with large calibration samples, optimal excess risk rate of log(m)/n\sqrt{\log(m)/n}.

FLOWGEM generates complete datasets from incomplete data with non-monotone MAR missingness.

problem Dealing with non-monotone Missing at Random (MAR) missingness in data.
method Iterative particle evolution of Wasserstein Gradient Flow, approximated by local linear estimators of density ratio.
result FLOWGEM achieves state-of-the-art performance across various settings, including non-monotone MAR mechanisms.

In this work we construct Calabi quasi-morphisms on the universal cover of the group Ham(M) of Hamiltonian diffeomorphisms for some non-monotone symplectic manifolds. This complements a result by Entov and Polterovich which applies in the monotone case. Moreover, in contrast to their work, we show that these quasi-morp…

2005-08-04abs ↗pdf ↗

New algorithm maximizes non-monotone adaptive submodular functions in linear time.

problem Maximizing non-monotone adaptive submodular functions subject to a cardinality constraint.
method Developed a linear-time algorithm for non-monotone adaptive submodular maximization.
result Achieved a 1/eε1/e-ε approximation ratio with O(nε2logε1)O(nε^{-2}\log ε^{-1}) value oracle queries.

We present an experimental and simulated model of a multi-agent stock market driven by a double auction order matching mechanism. Studying the effect of cumulative information on the performance of traders, we find a non monotonic relationship of net returns of traders as a function of information levels, both in the e…

2006-10-04abs ↗pdf ↗

Paper tackles non-monotonic resource utilization in sequential decision-making.

problem Sequential decision-making under uncertainty with resource constraints.
method Introduces a new MDP policy with constant regret against LP relaxation.
result Develops a learning algorithm with logarithmic regret for unknown outcome distributions.

In this paper, we study fundamental problems of maximizing DR-submodular continuous functions that have real-world applications in the domain of machine learning, economics, operations research and communication systems. It captures a subclass of non-convex optimization that provides both theoretical and practical guar…

2019-09-25abs ↗pdf ↗

New findings show privacy affects generalization error in a non-monotonic way.

problem Privacy and robustness in distributed learning.
method Theoretical analysis and matching lower/upper bounds on algorithmic stability.
result Generalization error is non-monotonically affected by privacy, depending on noise level.

We study the informational efficiency of a market with a single traded asset. The price initially differs from the fundamental value, about which the agents have noisy private information (which is, on average, correct). A fraction of traders revise their price expectations in each period. The price at which the asset …

2010-09-26abs ↗pdf ↗

In an economy with asymmetric information, the smart contract in the blockchain protocol mitigates uncertainty. Since, as a new trading platform, the blockchain triggers segmentation of market and differentiation of agents in both the sell and buy sides of the market, it recomposes the asymmetric information and genera…

2018-02-27abs ↗pdf ↗

Study on MMV in jump-diffusion models resolves MV's non-monotonicity issues.

problem Non-monotonicity and free cash flow stream problems in MV preferences.
method Explicit solution for MMV preferences in jump-diffusion models, proving non-negative potential measures.
result MMV resolves MV's non-monotonicity and free cash flow stream issues.

The paper analyzes Variable Annuities with surrender charges, providing a pricing formula and optimal exercise boundary.

problem Analyzing Variable Annuities with surrender charges and early termination rights.
method Formulated as an optimal stopping problem with a discontinuous payoff, non-monotonic optimal stopping boundaries are proven continuous and regular.
result A rigorous pricing formula and optimal exercise boundary for surrender options are derived.

We propose non-stationary spectral kernels for Gaussian process regression. We propose to model the spectral density of a non-stationary kernel function as a mixture of input-dependent Gaussian process frequency density surfaces. We solve the generalised Fourier transform with such a model, and present a family of non-…

2017-05-24abs ↗pdf ↗

New insights explain why ββ-VAEs fail at disentanglement.

problem Disentanglement performance of ββ-VAEs peaks at intermediate ββ and collapses as regularization increases.
method Formalized information-theoretic mechanism, introduced λβλβ-VAE to stabilize disentanglement.
result Strong regularization pressure leads to mutual information collapse in ββ-VAEs.

The paper improves PCS approximation for ranking and selection under limited simulation budgets.

problem Improving finite sample performance in Ranking and Selection.
method Develops a Bahadur-Rao type expansion for PCS, proposes a novel FCBA policy.
result FCBA policy achieves superior PCS performance compared to traditional methods.

Insider trading is reduced when penalized, affecting expected penalties in a non-monotone way.

problem Reducing insider trading behavior when insiders face legal penalties.
method Characterized via a backward stochastic differential equation (BSDE) with a non-linear operator.
result The insider's expected penalties are non-monotone in the fee structure and determined by relative entropy.

Study tackles criterion collapse in learning criteria, showing conditions for loss minimization.

problem Criterion collapse in optimization, focusing on error probability minimizers.
method Analyzes various learning criteria, including DRO, OCE risks, and non-monotonic criteria.
result Non-monotonic criteria can avoid collapse, while monotonic ones cannot.

Standard sequential generation methods assume a pre-specified generation order, such as text generation methods which generate words from left to right. In this work, we propose a framework for training models of text generation that operate in non-monotonic orders; the model directly learns good orders, without any ad…

2019-02-05abs ↗pdf ↗

Paper develops an online covariance estimator for nonsmooth stochastic approximation problems.

problem Estimating covariance in nonsmooth, potentially non-monotone settings.
method Online batch-means covariance matrix estimator.
result Estimator achieves convergence rate of O(dn1/8+ε)O(\sqrt{d}n^{-1/8+\varepsilon}).

Paper tackles stochastic kk-submodular bandits with full feedback, achieving sublinear regret.

problem Online optimization of kk-submodular functions with full-bandit feedback.
method Proposes online algorithms for various kk-submodular stochastic combinatorial multi-armed bandit problems.
result Achieves sublinear αα-regret bounds for multiple kk-submodular stochastic combinatorial multi-armed bandit problems.

The paper explores capital allocation using Euler formula with VaR and ES, revealing non-monotonicity and providing estimation methods.

problem Non-monotonicity in VaR-based capital allocation and the need for consistent risk measures.
method Use of Euler formula, Value-at-Risk (VaR), Expected shortfall (ES), simulation, and Markov chain Monte Carlo.
result Capital allocation with VaR is not monotonous, and consistent risk measures are crucial.

New method tackles online DR-submodular maximization with improved regret guarantees.

problem Online maximization of non-monotone DR-submodular functions over down-closed convex sets.
method 1/e-linearization through exponential reparametrization, surrogate potential, and reduction to online linear optimization.
result Achieves O(T1/2)O(T^{1/2}) static regret with single gradient query per round, improving state of the art.

Improved analysis of extragradient methods for structured VIPs.

problem Efficiently solving large-scale VIPs with weaker conditions.
method Single-call stochastic extragradient methods with expected residual condition.
result Convergence guarantees for quasi-strongly monotone and weak Minty VIPs.

Gradient descent on neural nets often operates at the Edge of Stability, where loss behavior is complex but loss decreases over time.

problem Understanding the optimization dynamics of neural networks at the Edge of Stability.
method Empirical demonstration of gradient descent behavior in neural network training.
result Gradient descent on neural networks typically occurs at the Edge of Stability, where loss behavior is non-monotonic but loss decreases over time.

In this paper we consider backward stochastic differential equations with time-delayed generators of a moving average type. The classical framework with linear generators depending on (Y(t),Z(t))(Y(t),Z(t)) is extended and we investigate linear generators depending on (1t0tY(s)ds,1t0tZ(s)ds)(\frac{1}{t}\int_0^tY(s)ds, \frac{1}{t}\int_0^tZ(s)ds). We…

2010-08-22abs ↗pdf ↗

Study shows informed traders harm market makers but price discovery benefits outweigh costs.

problem Informed traders' impact on market makers' profitability.
method Agent-based model with heterogeneous learning agents, multi-agent reinforcement learning.
result Informed market order flow is harmful when aggregate informedness is low but beneficial as it increases.