New approach optimizes expensive black-box systems with uncertain outputs.
problem Optimizing expensive black-box systems with limited data and uncertainty.
method Flexible non-interpolating surrogate model (TK-MARS) and Smart-Replication approach.
result TK-MARS outperforms original MARS and detects important variables.
Adaptive replication improves stochastic function optimization.
problem Challenges in accurately estimating functions with high variance.
method Trust-region-based Bayesian optimization with adaptive replication.
result Adaptive replication substantially improves solution accuracy and efficiency.
New algorithm ensures consistent results in constrained MAB problems.
problem Achieving consistent results in constrained MAB problems.
method Developed replicable algorithms for constrained MAB problems using the optimism principle.
result Regret and constraint violation of replicable algorithms match those of non-replicable ones.
By the classical Martingale Representation Theorem, replication of random vectors can be achieved via stochastic integrals or solutions of stochastic differential equations. We introduce a new approach to replication of random vectors via adapted differentiable processes generated by a controlled ordinary differential …
We price financial models using optimization and probability theory.
problem Financial model pricing under risk-averse investors.
method Infinite dimensional optimization, probabilistic and functional analytic tools.
result Existence of optimal strategies and convergence of reservation prices.
In this paper we propose a new robust algorithm to find the optimal static replicating portfolios for general nonlinear payoff functions and give the estimate of the rate of convergence that is absent in the literature. We choose the static replication by minimizing the error bound between the nonlinear payoff function…
Buy-and-hold strategy optimal for incomplete markets.
problem Optimal super-replication of Markovian claims in fully incomplete markets.
method Analyzes fully incomplete markets with stochastic volatility and rough volatility models.
result Super-replication of Markovian claims is of buy-and-hold type in fully incomplete markets.
Continuous-time model shows how trading affects asset prices and optimizes investment strategies.
problem Modeling financial markets with transient price impact and optimal trading strategies.
method Establishes a continuous-time duality involving measures with martingales and a liquidity weighted norm.
result Optimality of buy-and-hold strategies for call options and utility maximizing investment strategies proved.
New RL algorithm ensures stable, replicable policies.
problem Stability and replicability issues in RL algorithms.
method Introduced weak and strong forms of list replicability, developed a novel planning strategy, and tested state reachability.
result Proved efficient tabular RL algorithm with polynomial list complexity.
Study replicability in high-dimensional statistics, resolving open problems.
problem Ensuring consistent results in high-dimensional statistical tasks.
method Introduced replicable learning algorithms and established computational and statistical equivalence with high-dimensional isoperimetric tilings.
result Matching sample complexity upper and lower bounds for replicable mean estimation and coin problem.
New model prices corporate bonds by accounting for non-hedgeable risk.
problem Non-hedgeable risk in corporate bond pricing.
method Introduces a new model that drops liquidity assumption and uses a correlated liquid asset.
result Shows arbitrage-free formula for corporate bond pricing with non-hedgeable risk.
The paper proposes an efficient nested simulation design using likelihood ratio method.
problem Designing nested simulations with fixed outer scenarios and minimizing simulation effort.
method Proposes a bi-level optimization problem to decide inner replications and pooling strategies.
result Optimized design achieves $\cO(Γ^{-1})$ mean squared error of estimators.
Efficient algorithms improve learning of large-margin halfspaces.
problem Learning large-margin halfspaces efficiently and reproducibly.
method Design of efficient, dimension-independent, polynomial-time algorithms; SGD-based approach; DP-to-Replicability reduction.
result Improved sample complexity compared to previous algorithms, with optimal sample complexity for one algorithm.
New algorithm ensures replicable results in multi-armed bandits with minimal extra regret.
problem Ensuring consistent results in multi-armed bandit studies.
method Incorporates randomness into decision-making to ensure replicability while maintaining minimal extra regret.
result For large time horizons, proposed algorithm suffers only K2/ρ2 times smaller amount of exploration than existing algorithms. Study on replicability in reinforcement learning algorithms.
problem Ensuring consistent policy outputs in reinforcement learning.
method Mathematical study focusing on replicability in discounted tabular MDPs with a generative model.
result Design of efficient replicable and TV indistinguishable algorithms for policy estimation.
Study optimizes insurance liability cash flows with regulatory capital requirements.
problem Valuation of insurance liabilities under regulatory capital constraints.
method Multiple-prior optimal stopping theory applied to insurance liabilities, considering hypothetical transfer and repeated capital requirements.
result Proposes a valuation functional for non-replicable cash flows, incorporating a margin for regulatory capital considerations.
Method infers basic features of composite data using replicated autoencoders.
problem Inferring hidden features of composite data.
method Optimization of local entropy through interacting replicated autoencoders.
result Robust inference of hidden features without prior knowledge.
Paper examines financial engineering problems and introduces AlphaZero for better replication strategies.
problem Replication portfolio construction in incomplete markets with non-convex constraints.
method Introduces AlphaZero-based system to compare with deep hedging method.
result AlphaZero outperforms deep hedging in non-convex environments, finding near-optimal strategies.
The study models mortgage prepayment risk, accounting for behavioral uncertainty, and provides replication strategies.
problem Modeling and replicating the prepayment option of mortgages with behavioral uncertainty.
method Modeling behavioral uncertainty as a non-hedgeable risk factor, proving its impact on exposure value, and using IRSs and swaptions for replication.
result Including behavioral uncertainty reduces the exposure's value, and swaptions are necessary for optimal replication.
Proves FR-NGD optimally approximates evolutionary dynamics and continuous Bayesian inference.
problem Optimizing continuous time replicator equations and continuous Bayesian inference.
method Fisher-Rao natural gradient descent (FR-NGD) and its correspondence with evolutionary dynamics.
result FR-NGD optimally approximates continuous time replicator equations and continuous Bayesian inference.
Unified framework for fixed-income pricing and liability replication.
problem Static arbitrage and discount curve construction.
method Model-free framework for static fixed-income pricing and liability replication.
result Existence of strictly positive discount curves reproducing market prices and least-cost super-replicating portfolios.
New study on replicability and stability in machine learning algorithms.
problem Ensuring consistent results in machine learning models without fixing randomness.
method Introduced global stability and list replicability concepts, proving their equivalence and boosting list replicability.
result Global stability can only be achieved weakly, while list replicability can be boosted to achieve high probability of consistent results.
Characterizes super-replication prices in a financial market model.
problem Characterizing prices in a financial market model.
method Characterizes prices as the supremum of mono-prior super-replication prices through extreme priors and martingale measures.
result Super-replication prices are the supremum of mono-prior super-replication prices.
Study on computational aspects of replicable learning, bridging statistical and algorithmic perspectives.
problem Understanding the computational connections between replicability and various learning paradigms.
method Design of replicable learners, lifting framework, and transformation techniques.
result Efficient replicable learners for specific learning problems under various distributions.
The paper classifies self-replicating 3D shapes using algebraic models.
problem Understanding self-replicating 3D shapes.
method Using idempotents in the (2+1)-cobordism category to classify 3-manifolds.
result A classification theorem for self-replicating 3-manifolds.
Study dynamic trading in options to improve price bounds for exotic derivatives.
problem Improving price bounds for exotic derivatives through dynamic option trading.
method Extend semi-static trading strategies to include dynamic option trading, analyze duality results and pricing rules.
result Improved price bounds for exotic derivatives compared to conventional methods.
New algorithm prevents strategic replication in multi-armed bandit problems.
problem Strategic replication by agents can exploit bandit algorithms' balance.
method Designs Hierarchical UCB (H-UCB) and Robust Hierarchical UCB (RH-UCB) algorithms.
result Achieves O(lnT)-regret and sublinear regret in realistic scenarios. RL methods applied to option pricing using modified QLBS and RLOP models.
problem Applying reinforcement learning to price options accurately.
method Developed modified QLBS and RLOP models, implemented RL learning algorithm with neural networks.
result Optimal hedging strategies learned by RL outperform baseline models.
Extends super-replication theorem with dynamic strategies and transaction costs.
problem Dynamic super-replication under proportional transaction costs.
method Generalizes admissible strategies and defines a well-defined super-replication price process.
result Well-defined super-replication price process in dynamic setting.
Study reveals statistical bias in dataset replication, reducing accuracy drop from 11-14% to 3.6%.
problem Statistical bias in dataset replication affects model generalization accuracy.
method Analyzed ImageNet-v2, identified and corrected for bias, and compared results.
result Correcting bias reduces accuracy drop from 11-14% to 3.6%.
New uniformity tester ensures consistent results across different samples.
problem Non-replicable behavior of uniformity testing algorithms.
method Develops a replicable uniformity tester with improved sample complexity.
result Achieves nearly linear dependence on replicability factor ρ. Paper proposes a new method to detect node replication attacks in Vehicular Ad-hoc Networks.
problem Node replication attacks in Vehicular Ad-hoc Networks.
method Adapted statistical methodology for detecting node replication attacks.
result New method outperforms state-of-the-art techniques in detecting node replication attacks.
The paper improves generative models to avoid replicating observed examples.
problem Improving generative models to avoid replicating observed examples.
method Theoretical insights into the Wasserstein GAN, constrained to left-invertible push-forward maps, generating distributions that avoid replication and significantly deviate from the empirical distribution.
result Left-invertibility achieves this without compromising statistical optimality.
Deep learning predicts market sensitivities for cost-effective index tracking.
problem Costly and impractical replication of index funds.
method Learning to predict market sensitivities using deep learning models.
result Significant reduction in prediction errors compared to historical methods.
ERICA assesses replicability of cluster analysis results.
problem Lack of quantitative scrutiny for clustering results.
method ERICA: a framework to assess replicability of cluster analysis.
result Clusters are found to be replicable in synthetic data but not in real-world datasets.
In this work we introduce the notion of fully incomplete markets. We prove that for these markets the super-replication price coincide with the model free super-replication price. Namely, the knowledge of the model does not reduce the super-replication price. We provide two families of fully incomplete models: stochast…
Model predicts cash accumulation for assets with unknown prices.
problem Cash accumulation for assets with unpredictable future prices.
method Discretized Wiener Process matched using ordinary integrals.
result Model efficiently predicts cash accumulation for various asset scenarios.
We study super--replication of contingent claims in markets with fixed transaction costs. This can be viewed as a stochastic impulse control problem with a terminal state constraint. The first result in this paper reveals that in reasonable continuous time financial market models the super--replication price is prohibi…
Replicable clustering algorithms for k-medians, k-means, and k-centers are proposed.
problem Designing clustering algorithms that produce the same partition on repeated runs under the same distribution.
method Utilizing approximation routines for combinatorial clustering problems in a black-box manner.
result Replicable algorithms for statistical k-medians, k-means, and k-centers with specified approximation and sample complexities. Survey and benchmark high-dimensional Bayesian optimization of discrete sequences.
problem Heterogeneous experimental set-ups and technical barriers in high-dimensional Bayesian optimization of discrete sequences.
method Unified framework and software libraries to test and benchmark methods.
result Unified framework and software libraries for testing and benchmarking high-dimensional Bayesian optimization methods.
TF-Replicator simplifies distributed ML for researchers.
problem Complex distributed ML code for researchers.
method Abstraction over TensorFlow, data-parallel and model-parallel.
result Strong scalability without distributed systems expertise.
Unified valuation theory for credit risk, defaults, and funding costs.
problem Valuation under credit risk, defaults, and funding costs.
method Unified valuation theory expanding replication approach to incorporate credit risk, defaults, and funding costs.
result Clarifies the relationship between the adjusted cash flows approach and the replication approach.
The paper prices long-term options with a reflecting barrier model.
problem Pricing long-term options with asset price limits.
method Model asset price as geometric Brownian motion with a lower reflecting barrier, pricing options using compound options.
result Option prices can be determined using standard risk-neutral arguments, and hedging strategies are available.
Study insider trading benefits in a market with high transaction costs.
problem Super--replication of European contingent claims in illiquid markets.
method Model insider information and quadratic transaction costs, analyze scaling limits.
result Scaling limit gives the value of insider information.
New gradient coding schemes reduce decoding error in both random and adversarial straggler settings.
problem Creating efficient approximate gradient coding schemes for distributed optimization.
method Introduced novel approximate gradient codes based on expander graphs, achieving optimal decoding coefficients.
result Achieved nearly optimal error in random setting and nearly half the error in adversarial setting compared to existing codes.
Replicates and improves Uniswap V3 model using DDQN and Mamba.
problem Improving liquidity provision in Uniswap V3 with reinforcement learning.
method Combines DDQN with Mamba and introduces a new reward function.
result Shows stronger theoretical support and better performance than original model.
Study market delay effects on contingent claims pricing.
problem Delayed market information impacts contingent claims pricing.
method Analyzes Black-Scholes and binomial models with delay.
result Scaling limit of super-replication prices equals G-expectation.
This paper studies robust payoff allocation in submodular games, especially against replication.
problem Payoff allocation in submodular games, especially robustness against replication.
method Systematically studied replication manipulation in submodular games, introduced replication robustness metric, and validated with empirical ML data market.
result Conditions characterizing robustness of semivalues in submodular games.