Paper uses EXIT analysis for community detection with side information.
problem Community detection in the presence of side information.
method Imported EXIT method from iterative decoding of error control codes.
result Predicts asymptotic phase transition and residual errors for community detection.
Analyzes first exit times in a modified Barndorff-Nielsen and Shephard model.
problem Analyzing first exit times in a modified Barndorff-Nielsen and Shephard model.
method Formulated an approximate model driven by Brownian motion and Lévy subordinator, analyzed first exit times of log-return process.
result First exit time process decomposes into Brownian motion and Lévy subordinator components.
Study of a generalized geometric Brownian motion with varying entry and exit rates.
problem Understanding the long-run behavior of economic systems with growth, volatility, entry, and exit.
method Generalized geometric Brownian motion framework with varying entry and exit rates, analyzing moments and survival probability.
result Optimal exit rate minimizes mean first-passage time, influencing system outcome.
Investing in declining tech boosts profits, study finds.
problem Optimal decision-making in declining profit streams.
method Modeling profit stream as Brownian motion with negative drift, analyzing thresholds for investment and exit.
result Investment threshold decreases in volatility when profit boost is large.
Investors optimize liquid staking decisions in LSP and AMM protocols.
problem Optimal timing and allocation in liquid staking protocols.
method Derive optimal allocation strategy and model optimal exit timing using Laplace transforms and free-boundary techniques.
result Optimal stop-loss strategy maximizes expected payoff, influenced by fees and opportunity gains.
Deep RL model optimizes pedestrian evacuation in multi-exit scenarios.
problem Optimizing pedestrian evacuation in multi-exit indoor environments.
method MultiExit-DRL using Deep Reinforcement Learning with DQN and DNN.
result MultiExit-DRL reduces evacuation frames and optimizes exit utilization.
Study uses LLMs to optimize VC exit timing after IPO.
problem Optimal exit timing after IPO is crucial but not well studied.
method Uses LLMs to analyze financial data and market signals.
result LLMs can improve VC exit timing and generate better returns.
Study examines strategic exit timing in uncertain competition.
problem Timing of strategic exit decisions in competitive markets with uncertainty.
method Constructs a stochastic game equilibrium for exit strategies involving state variable and posterior belief process.
result Unique equilibrium found for symmetric Bayesian players.
A framework combining HSMM and survival analysis for lifecycle-oriented mobility analysis.
problem Understanding individual metro usage dynamics over multi-year horizons.
method A state-based lifecycle modeling framework integrating HSMM and discrete-time survival analysis.
result Identification of interpretable mobility states, transition dynamics, and state-dependent exit and re-entry processes.
Enhances KANs for accuracy and interpretability with multi-exit architecture.
problem Unclear optimal depth for KANs and difficulty in optimization and interpretation.
method Introduces multi-exit KANs with each layer having its own prediction branch.
result Multi-exit KANs outperform single-exit versions on various datasets.
Risk control improves EENNs to make faster predictions without sacrificing accuracy.
problem Determining safe times for EENNs to exit early without degrading performance.
method Adapting risk control frameworks to EENNs to tune their exiting mechanism.
result Risk control enables EENNs to make faster predictions while maintaining user-specified performance goals.
In this paper we develop a statistical arbitrage trading strategy with two key elements in hi-frequency trading: stop-loss and leverage. We consider, as in Bertram (2009), a mean-reverting process for the security price with proportional transaction costs; we show how to introduce stop-loss and leverage in an optimal t…
This work optimizes DNN inference for energy-harvesting devices by compressing and selectively executing neural network exits.
problem Inference delays and energy inefficiency in energy-harvesting devices.
method Developed a power trace-aware and exit-guided network compression algorithm for multi-exit neural networks.
result Superior accuracy and reduced latency compared to state-of-the-art techniques.
New method controls mean exit time in stochastic systems using machine learning and quasipotential.
problem Controlling mean exit time in stochastic dynamical systems with white noise.
method Developed a neural network to compute the quasipotential function and designed an algorithm to calculate the controller.
result Effective and accurate control strategy demonstrated through numerical experiments.
Previous work in hierarchical reinforcement learning has faced a dilemma: either ignore the values of different possible exit states from a subroutine, thereby risking suboptimal behavior, or represent those values explicitly thereby incurring a possibly large representation cost because exit values refer to nonlocal a…
Unified ML approach for SDEs in bounded domains.
problem Challenges in simulating SDEs with particle exit phenomena.
method Hybrid approach combining diffusion model and exit prediction network.
result Accurate modeling of interior dynamics and boundary interactions.
Paper analyzes venture capital exit decisions under inconsistent preferences.
problem Time-inconsistent preferences in venture capital exit timing.
method Modeling four types of venture capitalists with varying levels of inconsistency.
result Time-inconsistent venture capitalists exit earlier than consistent ones.
E2CM uses class means for efficient early exits in neural networks.
problem Efficient early exits in neural networks with low computational cost.
method Early Exit Class Means (E2CM) based on class means of samples, without gradient-based training. result E2CM achieves higher accuracy with fixed training time budget and boosts existing early exit schemes. EENNs improve inference efficiency but need nested prediction sets for reliable uncertainty estimates.
problem Non-nested prediction sets from standard uncertainty quantification methods in EENNs.
method Introduced anytime-valid confidence sequences (AVCSs) tailored for EENNs.
result AVCSs generate nested prediction sets across EENN exits, addressing the issue of non-nested sets.
Enhances early-exit neural networks for anytime classification.
problem Lack of guaranteed prediction quality improvement with longer computation time.
method Post-hoc modification based on Product-of-Experts to enforce conditional monotonicity.
result Achieves conditional monotonicity in prediction quality, enabling anytime classification.
EERO optimizes resource usage for efficient classification.
problem Managing computational resources in complex machine learning models.
method EERO uses multiple classifiers with a reject option to adaptively shorten processing paths.
result EERO effectively manages budget allocation and enhances accuracy in complex scenarios.
Based on Markvorsen and Palmer's work on mean time exit and isoperimetric inequalities we establish slightly better isoperimetric inequalities and mean time exit estimates for minimal submanifolds of N×R. We also prove isoperimetric inequalities for submanifolds of Hadamard spaces with tamed second fund…
Developed policy gradient methods for stochastic control with exit time, outperforming traditional techniques in share repurchase pricing.
problem Optimal control with exit time in stochastic models.
method Two types of algorithms: direct policy learning and alternately learning value function and control.
result Policy gradient methods outperform PDE or neural networks in share repurchase pricing.
The purpose of this article is to compute the expected first exit times of Brownian motion from a variety of domains in the Euclidean plane and in the hyperbolic plane.
We prove explicit upper and lower bounds for the L1-moment spectra for the Brownian motion exit time from extrinsic metric balls of submanifolds Pm in ambient Riemannian spaces Nn. We assume that P and N both have controlled radial curvatures (mean curvature and sectional curvature, respectively) as view…
Eigenfunctions constructed via Brownian motion exit times on curved spaces.
problem Constructing eigenfunctions of Laplacian on curved spaces.
method Using exit times of Brownian motion on a Riemannian manifold.
result Eigenfunctions can be constructed for a wide range of Laplacian eigenvalues.
Study shows submanifolds can't be immersed in certain spaces.
problem Non-immersibility of submanifolds with infinite mean exit time.
method Not based on the weak maximum principle at infinity, generalizes previous results.
result Estimates for complete tower of moments for submanifolds with small mean curvature.
This paper investigates sufficient conditions for a Feynman-Kac functional up to an exit time to be the generalized viscosity solution of a Dirichlet problem. The key ingredient is to find out the continuity of exit operator under Skorokhod topology, which reveals the intrinsic connection between overfitting Dirichlet …
The paper accelerates LLM inference by adding early exit heads trained in a self-supervised manner.
problem Inference speed in large language models (LLMs) is slow and resource-intensive.
method Adding self-supervised early exit heads at intermediate transformer layers to stop computation early based on confidence thresholds.
result Entropy provides the most reliable confidence metric for stopping computation early.
Mean exit times concentrate near equators and minimal hypersurfaces in high dimensions.
problem Understanding mean exit times on spheres and manifolds.
method Analyzing Brownian motion on spheres and manifolds with minimal hypersurfaces.
result Mean exit times concentrate near equators and minimal hypersurfaces in high dimensions.
Model predicts exit of private companies using voting of three classifiers.
problem Predicting the exit of privately held companies from limited data.
method Combines three classifiers (Logistic Regression, Random Forest, SVM) on extracted data.
result Achieves 63% predictive accuracy for Private Equity investors.
We apply the theory of continuous time random walks to study some aspects of the extreme value problem applied to financial time series. We focus our attention on extreme times, specifically the mean exit time and the mean first-passage time. We set the general equations for these extremes and evaluate the mean exit ti…
In the domain of technology startups, biotechnology has often been considered as specific. Their unique technology content, the type of founders and managers they have, the amount of venture capital they raise, the time it takes them to reach an exit as well as the technology clusters they belong to are seen as such un…
This paper introduces early exits in neural networks for faster inference.
problem Reducing inference time and preventing overfitting in neural networks.
method Designing and training multi-output neural networks with early exits.
result Significant reductions in inference time and improved robustness.
Optimizes liquidity withdrawal timing for AMM LPs to balance fees and impermanent loss.
problem Balancing fees and impermanent loss in automated market makers.
method Stochastic control problem with endogenous stopping time, numerical solutions via Euler scheme and Longstaff-Schwartz method.
result Optimal exit strategy depends on volatility, fees, and market dynamics.
Optimal exit strategies of CPT gamblers in unfair gambles
problem Optimal exit strategies of gamblers with CPT preferences in games with strictly negative expected payoffs
method Formulating the problem as an optimal stopping problem on asymmetric random walks, applying geometric transformation, randomized strategies, and changing the decision variable
result The unfair problem in the infinite time horizon has finite values for a wide range of CPT parameter specifications
Study predicts startup outcomes like funding, patenting, IPOs using machine learning.
problem Forecasting startup success metrics like funding, patenting, IPOs.
method Developed interpretable machine learning framework, used preprocessing, class imbalance handling, and compared multiple models.
result Achieved high AUROC values for patent, funding, and exit predictions.
This paper proposes and analyses a new multilevel Monte Carlo method for the estimation of mean exit times for multi-dimensional Brownian diffusions, and associated functionals which correspond to solutions to high-dimensional parabolic PDEs through the Feynman-Kac formula. In particular, it is proved that the complexi…
We study theoretical and empirical aspects of the mean exit time of financial time series. The theoretical modeling is done within the framework of continuous time random walk. We empirically verify that the mean exit time follows a quadratic scaling law and it has associated a pre-factor which is specific to the analy…
The paper explores conditions for compactness and finiteness in stratified homotopy theory.
problem Conditions for compactness and finiteness in stratified homotopy theory.
method Analyzes conditions for compactness and finiteness in stratified homotopy theory, providing sufficient conditions and deducing results.
result Conditions for compactness and finiteness in stratified homotopy theory are established.
Investigations of inverse statistics (a concept borrowed from turbulence) in stock markets, exemplified with filtered Dow Jones Industrial Average, S&P 500, and NASDAQ, have uncovered a novel stylized fact that the distribution of exit time follows a power law p(τρ)∼τρ−α with α≈1.5 at large τρ an…
Motivated by the industry practice of pairs trading, we study the optimal timing strategies for trading a mean-reverting price spread. An optimal double stopping problem is formulated to analyze the timing to start and subsequently liquidate the position subject to transaction costs. Modeling the price spread by an Orn…
Classifies domains critical for heat content and exit-time moments.
problem Understanding critical domains for heat content and exit-time moments.
method First variation of heat content, constant flow property, isoparametric foliation.
result Domains critical for heat content at all times have constant flow property and isoparametric foliation.
ELF improves long-tailed classification by focusing on hard examples.
problem Overfitting to majority classes in long-tailed data distributions.
method EARLY-exiting Framework with auxiliary branches.
result Improves accuracy by more than 3 percent on ImageNet LT and iNaturalist'18.
By appealing to renewal theory we determine the equations that the mean exit time of a continuous-time random walk with drift satisfies both when the present coincides with a jump instant or when it does not. Particular attention is paid to the corrections ensuing from the non-Markovian nature of the process. We show t…
This research examines relationship between staging of Venture Capital (VC) investments and social feedback visible in publicly available data on the Web. We address the question of Venture Capital investment sensitivity to performance and prospects of new venture, given as likelihood of obtaining future financing, ava…
Paper solves stock loan pricing with finite maturity using integral equations.
problem Valuation of margin-call stock loans with finite maturities.
method Fourier Sine transform and Volterra integral equation approach.
result Integral representation of margin-call stock loan value.
The Regularized Nonlinear Acceleration (RNA) algorithm is an acceleration method capable of improving the rate of convergence of many optimization schemes such as gradient descend, SAGA or SVRG. Until now, its analysis is limited to convex problems, but empirical observations shows that RNA may be extended to wider set…