The paper explains how speculative supply and demand amplify financial market fluctuations.
problem The wild fluctuations of financial prices.
method Formally, the paper shows that asset returns follow a multiplicative random growth with exogenous input.
result The theory explains the power-law distribution of returns and related variables.
Study examines how COVID-19 intensified demand variability in U.S. supply chains.
problem The amplification of demand variability (Bullwhip Effect) in supply chains during the pandemic.
method Extensive industry-level data analysis using traditional and advanced empirical techniques.
result COVID-19 significantly amplified the Bullwhip Effect across different U.S. industries.
In this article we quantify the bullwhip effect (the variance amplification in replenishment orders) when demands and lead times are predicted in a simple two-stage supply chain with one supplier and one retailer. In recent research the impact of stochastic order lead time on the bullwhip effect is investigated, but th…
New feature selection methods reduce bias amplification in models.
problem Bias amplification in machine learning models.
method Analysis and development of feature selection algorithms.
result Feature-wise bias amplification can be mitigated through targeted feature selection.
Economic growth is unpredictable unless demand is quantified. We solve this problem by introducing the demand for unpaid spare time and a user quantity named human capacity. It organizes and amplifies spare time required for enjoying affluence like physical capital, the technical infrastructure for production, organize…
Improves privacy amplification by shuffling for differential privacy.
problem Enhancing privacy guarantees in systems with anonymous data contributions.
method Theoretical and numerical analysis of Rényi differential privacy parameters and privacy amplification by shuffling.
result First asymptotically optimal analysis of Rényi differential privacy parameters for shuffled outputs.
Advantage amplification helps RL in slow-evolving latent-state environments.
problem Challenges in reinforcement learning for long-horizon latent-state environments.
method Temporal abstraction and aggregation methods to overcome belief state error and small action advantage.
result Proven advantage amplification in settings with slowly evolving latent states.
This paper extends a Kyle model to include price-responsive traders, revealing new dynamics and equilibria.
problem Real-world market dynamics involve price-responsive traders, affecting market equilibrium and insider profits.
method Developed a continuous-time Kyle model with two types of price-responsive traders (momentum and contrarian), leading to a forward-backward Riccati system for equilibrium.
result The model shows that feedback effects can lead to multiple equilibria and amplify price informativeness.
Mitigates gender bias amplification in model predictions.
problem Gender bias amplification in model predictions.
method Posterior regularization to mitigate bias.
result Almost removes gender bias amplification in model predictions.
Gradient amplification boosts deep learning model performance without increasing training time.
problem Vanishing gradients in deep neural networks.
method Gradient amplification approach to prevent vanishing gradients and training strategy to enable/disable across epochs.
result Improves performance of deep learning models with reduced training time.
Model predicts spending behavior of average consumer over short period.
problem Understanding consumer spending dynamics during economic crises.
method Simple hydrodynamical model to describe spending behavior over brief period.
result Model predicts spending behavior of average consumer over short period.
Privacy is enhanced by synthetic data release even with unlimited data.
problem Improving privacy guarantees for synthetic data release.
method Analyzing a bounded-parameter assumption to show privacy amplification persists with unlimited synthetic records.
result Privacy amplification is possible even with an unbounded number of synthetic records.
Iterated Amplification uses subproblem solutions to build training signals for complex tasks.
problem Learning complex tasks when humans can't directly evaluate performance.
method Progressively builds training signal by combining solutions to easier subproblems.
result Efficiently learns complex behaviors in algorithmic environments.
Paper improves privacy amplification by subsampling with a general method.
problem Ensuring privacy guarantees with subsampled data.
method General method leveraging divergence characterization of differential privacy.
result Recovery and improvement of prior analyses, derivation of lower bounds, and new instances.
Privacy amplification improved through contraction coefficients and Eγ-divergence.
problem Improving privacy guarantees in iterative algorithms.
method Using contraction coefficients derived from Eγ-divergence to determine differential privacy parameters. result Tighter bounds on differential privacy parameters of iterative algorithms.
ISOMORPH creates a digital twin for supply chain logistics, advancing time-series forecasting benchmarks.
problem Lack of public benchmarks for supply chain logistics time-series forecasting.
method Developed a digital twin simulator with interpretable parameters and modular topology, generating datasets and verifying conservation laws.
result Foundation models achieve MASE values exceeding public benchmarks at low-to-moderate horizons, supporting UQ.
New method amplifies privacy in decentralized learning without centralized communication.
problem Privacy amplification in decentralized federated learning.
method Random check-in protocol for DP-SGD in FL.
result Privacy/accuracy trade-offs similar to subsampling/shuffling, but without server-initiated communication.
The paper explores how mixing and diffusion mechanisms can enhance privacy in data processing.
problem Enhancing privacy guarantees of data mechanisms through post-processing.
method The study uses Markov operators and coupling arguments to analyze privacy amplification.
result The introduction of a new family of diffusion-based mechanisms that are closed under post-processing.
Structured subsampling improves privacy in deep time series forecasting.
problem Incompatible privacy guarantees with time series forecasting.
method Structured subsampling of sequential data for privacy amplification.
result Structured subsampling enables training with strong privacy guarantees.
The paper compares different fairness definitions under various worldviews.
problem Avoiding disparity amplification under different worldviews.
method Mathematical comparison of four fairness definitions using a theoretical framework.
result Different worldviews require different fairness definitions to avoid disparity amplification.
Privacy-preserving identification framework using sparse approximation and privacy amplification.
problem Protecting privacy in identification applications like biometrics and IoT.
method Sparse approximation with privacy amplification, not symmetric for data owner and user.
result Preserves privacy at low computational cost, storage, and communication burdens.
New privacy amplification technique for iterative algorithms.
problem Ensuring privacy in iterative learning algorithms.
method Demonstrates privacy amplification for contractive iterations without releasing intermediate results.
result Strong privacy amplification achieved with minimal intermediate releases.
New method for privacy amplification without sampling for matrix factorization.
problem Privacy amplification for differentially private model training with matrix factorization.
method Sampling-free bounds based on Rényi divergence and conditional composition.
result Stronger privacy guarantees for small ε, applicable to various matrices.
Unified framework for subsampling mechanisms with tighter privacy guarantees.
problem Improving privacy in machine learning models through subsampling.
method Conditional optimal transport for deriving mechanism-specific subsampling guarantees.
result Tighter privacy bounds for subsampled mechanisms compared to traditional methods.
The paper develops a theory explaining how machine learning models can amplify biases.
problem Understanding and mitigating bias in machine learning models.
method Analytical theory of ridge regression with and without random projections.
result Observations and predictions align with empirical data on machine learning bias.
New sampling scheme improves privacy in DP-SGD without sacrificing utility.
problem Suboptimal privacy amplification due to participation variance in Poisson subsampling.
method Balanced Iteration Subsampling (BIS) with structured randomness.
result BIS achieves stronger privacy amplification than Poisson subsampling and is optimal at both extremes of noise spectrum.
The paper shows how to amplify small datasets to look like they came from a known distribution.
problem How to increase dataset size when learning from an unknown distribution is impossible.
method Develops amplification procedures to output larger sets of samples that mimic the original distribution.
result Valid amplification procedures exist even when the input dataset is significantly smaller than needed for learning.
FLAME improves privacy in federated learning without trusted parties.
problem Ensuring privacy in federated learning without trusted parties.
method FLAME uses the shuffle model of differential privacy to achieve better accuracy and privacy.
result FLAME protocols improve testing accuracy by 60.7% compared to local model FL.
This study quantifies systemic importance in global banks using a continuous framework that amplifies localized shocks.
problem Analyzing financial contagion and systemic risk in global banks.
method Developed a continuous framework incorporating geographic proximity and interbank network linkages, using a master equation and Feynman-Kac representation.
result The amplification factor correctly identifies systemically important institutions and predicts crisis outcomes.
Sharp pseudospectral bounds prevent transient amplification in coupled gradient descent.
problem Transient amplification in coupled gradient descent systems.
method Developed a sharp pseudospectral theory for block-triangular Jacobians, proving Kreiss constant bounds and matching minimax lower bounds.
result Obtained a finite-horizon iteration-complexity bound of O(K(J)2log(1/δ)) for stochastic coupled descent. Missing data enhances privacy in differential privacy.
problem Privacy preservation in datasets with missing values.
method Formalized missing data as a privacy amplification mechanism within differential privacy.
result Incomplete data can yield privacy amplification for differentially private algorithms.
The study explains how market-makers' hedging affects stock volatility during gamma-squeeze events.
problem Endogenous volatility amplification in option markets during gamma-squeeze events.
method Developed a theoretical framework linking hedging behavior and market turbulence, incorporating beta-normalized volatility.
result Low-beta stocks amplify volatility more during gamma-squeeze events.
Unified approach amplifies data for distribution property estimation.
problem Estimating properties of discrete distributions efficiently.
method Unified, linear-time, competitive estimator using just 2n samples.
result Achieves performance of empirical estimator with n√log n samples using only 2n samples.
Most of the econometric and econophysics models have been borrowed from the statistical physics, and as a cosequence, a new interdisciplinary science called econophysics has emerged. In this paper we planned to extend the analogy between different economic processes or phenomena and processes and phenomena from differe…
Synthetic data can amplify privacy in linear regression models.
problem Understanding how synthetic data can enhance privacy in linear regression models.
method Investigated through the linear regression framework, analyzing synthetic data generated from random inputs and controlled inputs.
result Releasing a limited number of synthetic data points amplifies privacy beyond the model's inherent guarantees when inputs are random, but not when inputs are controlled by an adversary.
Paper analyzes tech adoption in financial networks, finding key leadership and diffusion dynamics.
problem Understanding technology adoption and network effects in financial systems.
method Developed a spatial-network framework with a master equation and Feynman-Kac representation.
result Found strong support for two-regime adoption dynamics and significant leadership in network central banks.
This work improves privacy in the shuffle model for summing real numbers.
problem Improving privacy in the shuffle model for data aggregation.
method Developed optimal single message protocol and new lower and upper bounds for privacy amplification.
result Improved accuracy and communication in summing real numbers protocol.
The study finds limited contagion of economic growth rates from many customers to a single supplier.
problem Contagion of economic growth rates across a trade credit network of firms.
method Empirical analysis of delayed payments and trade credit networks, using a large Italian bank's data.
result Limited contagion of economic growth rates from many customers to a single supplier, not in the whole network.
The paper analyzes and improves privacy in machine learning through importance sampling.
problem Ensuring privacy in machine learning while maintaining utility and efficiency.
method Individualized privacy analysis of importance sampling, proposing two approaches for constructing sampling distributions.
result Proposed approaches optimize privacy-efficiency trade-off and outperform uniform sampling.
AdaPrivate-TS: A differentially private Thompson Sampling algorithm for contextual bandits
problem Private Thompson Sampling for Contextual Bandits
method Combining Thompson Sampling with batched zCDP composition
result Achieves 93-99% of non-private performance at ε ∈ [0.5, 5] with logarithmic privacy cost
Enhances privacy in machine learning through Rényi Pufferfish mechanisms.
problem Designing general and efficient Pufferfish mechanisms that maintain privacy and utility.
method Introduces a Rényi divergence-based variant of Pufferfish, generalizes the Wasserstein mechanism, and proves privacy amplification results.
result Extends the applicability of Pufferfish framework and provides stronger privacy guarantees.
Quantum algorithm solves best arm identification problem faster.
problem Identifying the best option in a set of choices.
method Quantum amplitude amplification algorithm.
result Quadratically faster than classical methods.
Momentum SGD fails to track nonstationary optima due to drift amplification.
problem Tracking nonstationary optima in stochastic optimization.
method Theoretical analysis of SGD and momentum variants under strong convexity and smoothness.
result Momentum incurs a drift-amplification penalty that diverges as the momentum parameter approaches 1, leading to systematic lag.
Quantum method speeds up risk estimation for insurance tail risks.
problem Sample-sparsity in classical Monte Carlo methods for tail risk pricing.
method Quantum Amplitude Estimation (QAE) with Grover amplification.
result Quantum method achieves convergence approaching order reciprocal N, enabling high-resolution tail estimation within practical budgets.
Develops a framework for distilling flow models from few steps.
problem Improving few-step sampling in diffusion models for better performance.
method Local approximation errors and dynamical amplification controlled through analytical tractability.
result Deep residual compositions efficiently approximate long-horizon transport with controlled global error.
New algorithm mitigates misspecification amplification in regression models with covariate shift.
problem Distribution shift and model misspecification in regression models.
method Developed a new algorithm inspired by robust optimization to avoid misspecification amplification.
result No misspecification amplification while still achieving optimal statistical rates.
New method amplifies hidden structure in complex networks.
problem Difficulty in uncovering hidden structure in complex networks.
method Iterative weakening of dominant structure through randomization.
result Theoretical support for the effectiveness of structure amplification.
Study reduces gender bias in web data used for image recognition.
problem Gender bias in web data amplifies in machine learning models.
method Inject corpus-level constraints for calibrating structured prediction models.
result Bias amplification decreased by 47.5% and 40.5% for multilabel classification and visual semantic role labeling.