Measures financial resilience using BSDEs and their properties.
problem Measuring financial resilience in dynamic risk environments.
method Developed stochastic calculus for BSDEs with jumps, revealing resilience rate as expectation of generator.
result Resilience rate can be represented as expectation of BSDE generator, revealing properties of dynamic risk measures.
New model shows negative resilience can improve trading efficiency.
problem Optimal trade execution in limit order books with negative resilience.
method Stochastic order book model with negative resilience.
result Negative resilience can lead to more efficient trading.
Study optimal trade execution in markets with price impact and resilience.
problem Optimal trade execution in illiquid markets with price impact and resilience.
method Modelled as a three-dimensional system of BSDEs with singular terminal condition, proving existence and uniqueness of solution.
result Characterized optimal strategy and value function in terms of BSDE solution.
This paper develops a stochastic learning-optimization model for resilient automotive supply chains.
problem Supply chain disruptions and volatile demand pose challenges to the UK automotive industry.
method Integrates Bayesian inference with inventory optimization for a two-echelon system subject to stochastic demand and disruptions.
result The integrated approach achieves significant cost reductions and improved resilience during disruptions.
Study optimal stock purchases under fluctuating market resilience.
problem Optimal stock purchases over time with varying market resilience.
method Model market resilience and liquidity variations, solve optimization problem.
result Unique optimal execution strategy found.
Resilience ensures robust computation of dataset properties in the presence of outliers.
problem Robust estimation of dataset properties in the presence of arbitrary outliers.
method Introduces resilience as a criterion for robust computation, weaker than existing conditions.
result Resilience enables robust estimation in a broader variety of settings.
We solve a complex trade execution problem by simplifying it into a known LQ control problem.
problem Optimal trade execution with stochastic price impact and resilience.
method Extending the problem to progressively measurable processes and reducing it to a LQ stochastic control problem.
result The solution to the LQ problem traces back to the solution of the original trade execution problem.
This paper uses robust optimization to analyze supply chain resilience.
problem Supply chain resilience analysis of multi-modal logistics networks.
method Robust optimization with budget-of-uncertainty.
result Interactive effects of network size, disruption scale, and degree on resilience.
Phocas improves robustness in SGD against Byzantine failures.
problem Byzantine failures in distributed SGD.
method Proposes a novel aggregation rule for Byzantine-resilient SGD.
result Proves Byzantine resilience of the proposed aggregation rules.
New model predicts financial contagion in networks with block structures.
problem Understanding contagion in networks with complex block structures.
method Generalized stochastic block model with varying edge probabilities and exposures.
result Explicit computation of systemic damage and complete resilience characterization.
Secure federated learning framework resists adversarial users.
problem Resilience against adversarial (Byzantine) users in federated learning.
method Integrated stochastic quantization, verifiable outlier detection, and secure model aggregation.
result First single-server Byzantine-resilient secure aggregation framework (BREA) for secure federated learning.
Byrd-SAGA reduces variance to robustify SGD against Byzantine attacks.
problem Learning over networks with malicious Byzantine attacks.
method Byrd-SAGA uses geometric median for robust aggregation of corrected stochastic gradients.
result Byrd-SAGA achieves provably linear convergence to optimal solution in the presence of Byzantine workers.
Two algorithms improve federated learning efficiency and resilience.
problem Scalability issues in federated learning due to communication, privacy, and Byzantine attacks.
method Proposes two algorithms, Ada-StoSign and β β β -StoSign, that compress gradients into bit vectors to reduce communication. result Ada-StoSign converges with a rate of O ( log T / T + 1 / M ) O(\log T/\sqrt{T} + 1/\sqrt{M}) O ( log T / T + 1/ M ) and outperforms existing methods. Proposes Stochastic-Sign SGD for federated learning with theoretical guarantees.
problem Developing efficient, private, and resilient parameter estimation methods for federated learning.
method Introduces Stochastic-Sign SGD, a novel method based on SIGNSGD, which addresses convergence and communication efficiency.
result Demonstrates the effectiveness of Stochastic-Sign SGD through experiments on MNIST and CIFAR-10 datasets.
ScieNet improves deep learning resilience to input perturbations.
problem Deep learning's poor resilience to input perturbations in real-world scenarios.
method Hybrid architecture combining SNN for contextual info extraction and DNN for classification.
result Significant improvement in accuracy on noisy and rainy images without prior training.
New PAC-Bayesian bounds for deep networks without stochastic or compressed parameters.
problem Generalization of overparameterized deep networks.
method PAC-Bayesian framework that leverages noise-resilience of flat minima.
result Generalization guarantee for deterministic, uncompressed networks.
Method creates resilient classifiers by focusing on highly predictive features.
problem Adversarial attacks exploit weakly correlated features learned during training.
method Resilient feature engineering to prioritize highly predictive features.
result Serial and Parallel Composition Resilience theorems support the design of resilient classifiers.
Paper introduces a new index to measure financial and workplace resilience of firms.
problem Corporate resilience and its types in turbulent markets.
method Quantitative analysis of earnings expectations and implied discount rates.
result Evidence of workplace resilience amplification by financial status in the COVID-19 era.
Byzantine-resilient federated learning with local iterations and robust mean estimation.
problem Byzantine clients disrupt federated learning with local iterations.
method Local SGD iterations, robust mean estimation, and matrix concentration result.
result Convergence analysis for strongly-convex and non-convex smooth objectives in heterogeneous data settings.
Python tool assesses European agricultural production resilience.
problem Estimating resilience of European agricultural production systems.
method Python software to compute annual production resilience indicator.
result Demonstrates resilience of European agricultural production.
This paper measures financial market resilience in China and identifies key uncertainties.
problem Measuring financial market resilience in China.
method Quantitative analysis of total financial market and sub-markets, Diebold-Yilmaz connectedness approach.
result Financial market resilience in China is event-driven and influenced by geopolitical risks, economic and trade policy uncertainty, and U.S.-China tensions.
Study proposes a new resilience metric for stock market performance analysis.
problem Quantifying resilience cycles in stock market performance.
method Systems-oriented approach with Robustness Range and Elasticity Threshold.
result New metric quantifies non-linear resilience cycles in stock markets.
The resilience of low-degree Rademacher chaos is studied, providing probabilistic lower bounds.
problem Understanding how much a Rademacher chaos can withstand adversarial sign-flips without significant probability changes.
method Probabilistic lower-bound guarantees for the resilience of Rademacher chaos of arbitrary degree.
result Probabilistic lower-bound guarantees for the resilience of Rademacher chaos of arbitrary degree, especially meaningful for constant degree.
Noise-resilient optimization on noisy quantum computers.
problem Noise's impact on hybrid quantum-classical optimization.
method Iterative quantum circuit with noise consideration, using Quantum Fisher Information bound.
result Algorithm robustness against different noise strengths.
Proposes resilience metrics for large blackout costs with logarithmic resilience.
problem Large variations in blackout costs make estimating risk impractical.
method Uses mean of log of large blackout costs, tail slope index, and frequency.
result Solves problems of heavy tail and large variations in blackout costs.
Fault-tolerant federated learning for non-uniform data.
problem Faulty workers corrupting data in federated learning.
method Fault-resilient proximal gradient (FRPG) algorithm with Nesterov's acceleration and local FRPG for reduced communication.
result FRPG and LFRPG converge faster than robust stochastic aggregation.
Stocks of more resilient firms outperformed during the pandemic, reflecting disaster risk.
problem The impact of social distancing on firms' operations and stock performance.
method Cross-sectional analysis of firms' resilience and stock performance, controlling for risk factors.
result Stocks of more resilient firms are expected to yield significantly lower returns than less resilient ones, reflecting disaster risk.
Optimal trade execution in a fluctuating market with stochastic liquidity.
problem Minimizing costs in a market with unpredictable liquidity.
method Developed a recursion to find the least costly trade execution strategy.
result Explicit recursion characterizes the least costly trade execution.
ResiliNet improves distributed neural network inference resilience.
problem Physical node failures in distributed neural networks cause performance drops.
method Skip hyperconnection and failout technique.
result ResiliNet provides inference resiliency for distributed neural networks.
Paper benchmarks DRL policies' resilience to state transitions.
problem Measuring DRL policies' resilience to state perturbations.
method Disentangled representation learning and RL-based techniques.
result Demonstrated feasibility of resilience benchmarking in DQN, A2C, and PPO2.
New method quantifies resilience of electric distribution systems from historical data.
problem Large blackouts caused by extreme winds have significant costs and impacts.
method Formulate large event risk from utility outage data, quantify resilience improvements through investments.
result Investments in wind hardening and faster restoration can reduce the probability of large cost events.
This study develops a dynamic inverse optimization framework to recover hidden, time-varying preferences from observed allocation trajectories.
problem The gap between classical optimization theory and real-world practice, especially in the presence of drift and shocks.
method Dynamic inverse optimization framework using a drift-aware estimator grounded in convex analysis and online learning theory.
result Sharp static and dynamic regret bounds for the framework, demonstrating its responsiveness to gradual drift and sudden shocks.
New algorithm resists up to half of Byzantine workers in distributed learning.
problem Resilience of distributed SGD in the presence of Byzantine attackers.
method Lipschitz-inspired coordinate-wise median approach (LICM-SGD).
result LICM-SGD can resist up to half of Byzantine workers in non-convex settings.
The paper tackles robustness of machine learning to Byzantine failures.
problem The need for scalability and complexity in modern machine learning calls for distributed implementations, but they are vulnerable to Byzantine failures.
method The authors study stochastic gradient descent (SGD) and propose Krum, an update rule that tolerates up to f f f Byzantine workers. result Krum, the proposed update rule, satisfies a resilience property and guarantees convergence despite f f f Byzantine workers. Algorithm learns decision trees from noisy data.
problem Learning stochastic decision trees from corrupted samples.
method Quasipolynomial-time algorithm for adversarial noise.
result Returns a hypothesis with error within 2 η + ε 2η + \varepsilon 2 η + ε of optimal. Ray tracing sampler improves neural network sampling efficiency and resilience.
problem Sampling neural network posterior distributions efficiently and robustly.
method Markov Chain Monte Carlo using ray tracing through likelihood space.
result Significantly higher resilience to gradient heating compared to HMC.
New framework for resilient bi-criteria optimization under noisy feedback.
problem Bi-criteria combinatorial optimization with noisy function evaluations.
method Introducing ( α , β , δ , e x t t t N ) (α,β,δ, exttt{N}) ( α , β , δ , e x ttt N ) -resilience and developing a black-box framework. result Achieves sublinear regret and constraint violation for bi-criteria bandit problems.
Kardam handles Byzantine workers in asynchronous SGD, ensuring convergence and performance.
problem Byzantine behavior in distributed machine learning workers.
method Two complementary components: a filtering and a dampening component.
result Kardam guarantees almost sure convergence in the presence of Byzantine workers.
Paper tackles Byzantine resilience in distributed multi-task learning.
problem Resilience of distributed algorithms in the presence of Byzantine agents.
method Online weight assignment rule based on accumulated loss and filtering.
result Aggregation with proposed weight assignment rule improves expected regret.
We solve the SLV model calibration problem using inverse-problem techniques.
problem Calibrating the SLV model given a local volatility surface and stochastic volatility parameters.
method Regularization techniques from inverse-problem theory.
result Stable and robust algorithm for SLV model calibration.
We solve explicitly a two-dimensional singular control problem of finite fuel type for infinite time horizon. The problem stems from the optimal liquidation of an asset position in a financial market with multiplicative and transient price impact. Liquidity is stochastic in that the volume effect process, which determi…
Model uses Navier-Stokes equations to assess liquidity and systemic risk.
problem Traditional models fail to capture real market fluctuations and extreme events.
method Develops and validates a mathematical model based on Navier-Stokes equations, incorporating 13 macroeconomic and financial parameters.
result Model effectively describes liquidity dynamics, systemic risk, and extreme scenarios.
Small stocks drive market crashes by suppressing resilience.
problem The single-security price limit exacerbates market panic during crashes.
method Simplified dynamic model on networks of investors and stocks, empirical verification.
result Unexpected linear association between price limit and critical market confidence.
Study reveals resilience of Chinese guarantee network during financial crisis and stimulus.
problem Limited knowledge about guarantee network dynamics during financial downturn.
method Analyzed comprehensive bank loan dataset covering 80% of total loans in China.
result Guarantee network became smaller, less connected, and more stable during financial crisis.
Improved deep neural network generalization through noise resilience.
problem Understanding and predicting generalization error of deep neural networks.
method Noise resilience measures to predict generalization error.
result Secured 5th position in the PGDL competition at NeurIPS 2020.
CyBeR-0 optimizes federated learning with Byzantine resilience and reduced communication costs.
problem Byzantine attacks and communication inefficiency in federated learning.
method Transformed robust aggregation for zero-order optimization under client heterogeneity.
result CyBeR-0 achieves stable performance with minimal communication costs and reduced memory usage.
A methodology for resilience analysis of Capsule Networks under approximation errors.
problem Resilience of Capsule Networks under approximation errors.
method Modeling and analyzing approximation errors in Capsule Networks' inference.
result Capsule Networks are more resilient to errors during dynamic routing than other stages.
Firms' collaboration networks can decline but remain resilient.
problem Resilience of firms' collaboration networks during decline.
method Analysis of 21,500 R&D collaborations over 25 years, simulating drop-out cascades.
result Firms' collaboration networks can adapt to mitigate decline and recover.