Corrects XVA framework for derivative pricing, including credit and funding costs.
problem Inaccuracies in XVA valuation adjustments for costs of capital and initial margin.
method Clarifies and rectifies XVA framework, presenting corrected formulae for KVA and MVA.
result Corrected formulae for KVA and MVA accounting for costs of capital and initial margin.
This article prices OTC derivatives with either an exogenously determined initial margin profile or endogenously approximated initial margin. In the former case, margin valuation adjustment (MVA) is defined as the liability-side discounted expected margin profile, while in the latter, an extended partial differential e…
Initial margin requirements are becoming an increasingly common feature of derivative markets. However, while the valuation of derivatives under collateralisation (Piterbarg 2010, Piterbarg2012), under counterparty risk with unsecured funding costs (FVA) (Burgard2011, Burgard2011, Burgard2013) and in the presence of re…
Bank behavior affects XVA pricing by influencing counterparty independence assumptions.
problem XVA pricing assumes counterparty independence, but bank behavior complicates this assumption.
method Developed a theoretical framework considering bank behavior and anonymous counterparties.
result Bank behavior requires inclusion of multiple CVA costs and affects KVA and FVA.
Alternative method proposed to avoid Procrustes in regularized MVA.
problem Optimizing regularized MVA solutions while preserving feature uncorrelation.
method Solves an eigenvalue problem instead of the Procrustes approach.
result Ensures preservation of feature uncorrelation in regularized MVA.
The Inverse Bagging Algorithm detects anomalies by identifying sub-samples rich in known data.
problem Detecting anomalies in data sets with a well-modeled process and an unknown PDF.
method Uses inverse bootstrap aggregating to identify sub-samples rich in the known process and classify events.
result The method avoids modifying the kinematic distributions of the well-modeled process.
MEVA aggregates model predictions to improve accuracy without needing model details.
problem Improving model accuracy by combining multiple models.
method Non-intrusive, data-driven framework that treats models as black boxes and optimizes aggregation methods.
result MVA outperforms MEA in estimating aggregated predictions, enhancing robustness and accuracy.
The paper tackles mean-variance analysis in Bayesian optimization under uncertainty.
problem Optimizing decisions in uncertain environments considering trade-offs between average and variance of risk.
method Developed bounds for mean and variance risk measures in Gaussian Process models and proposed AL algorithms for multi-task, multi-objective, and constrained optimization scenarios.
result Proposed AL algorithms effectively address the mean-variance trade-off in uncertain optimization scenarios.
Paper calculates robust FVA for OTC derivatives under distributional uncertainty.
problem Distributional uncertainty in over the counter derivatives valuation.
method Wasserstein distance as ambiguity measure, dual formulation of robust FVA optimization.
result Additional FVA charge due to distributional uncertainty measured under various configurations.
This paper evaluates how XVA affects option exercise decisions.
problem Ignoring XVA in exercise decisions can lead to incorrect valuation.
method Regression techniques to evaluate XVA's impact on exercise decisions.
result XVA significantly impacts the exercise decision at the boundary.
Differential ML combines AAD with ML for fast, accurate financial derivatives pricing and risk management.
problem Computational bottlenecks in financial derivatives risk management.
method Novel algorithms using automatic adjoint differentiation (AAD) for training fast, accurate approximations in real-time.
result Convergence guarantees for fast, accurate pricing and risk approximations for arbitrary derivatives instruments.
This paper develops an XVA (costs) analysis of centrally cleared trading, parallel to the one that has been developed in the last years for bilateral transactions. We introduce a dynamic framework that incorporates the sequence of cash-flows involved in the waterfall of resources of a clearing house. The total cost of …
Feature extraction and dimensionality reduction are important tasks in many fields of science dealing with signal processing and analysis. The relevance of these techniques is increasing as current sensory devices are developed with ever higher resolution, and problems involving multimodal data sources become more comm…
The paper tackles fVaR prediction methods in finance.
problem Predicting future values at risk (fVaR) in finance.
method Various methods including Nested MC-empirical quantile, percentiles from distributions, quantile regressions, and limited inner simulations.
result Improved methods for predicting fVaRs, including those that are computationally efficient.
Proposes a multi-objective variational autoencoder for smart infrastructure damage detection.
problem Detecting and diagnosing damage in smart infrastructure using multi-way data.
method Multi-objective variational autoencoder (MVA) method for smart infrastructure damage detection and diagnosis.
result The method accurately detects structural damage, estimates severity, and captures damage locations.
A deep BSDE approach tackles multi-layered xVA calculations for portfolio valuation.
problem Computational intractability in nested simulations for multi-layered xVA calculations.
method Iterative deep BSDE approach, change-of-measure method, quantile regression for margin computation.
result Reduces computational demands and successfully scales to high-dimensional portfolios.
This paper shows hedging algorithms improve performance in repeated matrix games.
problem Improving multi-agent learning algorithms in repeated matrix games.
method Develops and experiments with hedging algorithms combining a top-level and a set of basic algorithms.
result Well-selected hedging algorithms outperform previous MAL algorithms on repeated matrix games.
Examines algorithmic modeling across three cultures.
problem Tackles algorithmic modeling in different cultural contexts.
method Uses parametric regressions, interpretable algorithms, and complex algorithms.
result Extension of Leo Breiman's thesis to include cultural differences.
Meta-algorithm selection aims to choose the best algorithm selector for a given problem instance.
problem Selecting the best algorithm selector for a specific problem instance.
method Apply algorithm selection to the selection of other algorithms (meta-algorithm selection).
result Meta-algorithm selection can be beneficial in some cases but faces challenges in solving the meta-level problem.
Proposes CLRS benchmark to evaluate algorithmic reasoning.
problem Difficulty in transferring results across publications due to targeted algorithmic data.
method Develops a comprehensive benchmark covering various algorithmic tasks.
result Demonstrates performance of algorithmic reasoning baselines on the CLRS benchmark.
Combines multiple bandit algorithms to create a nearly optimal single algorithm.
problem Designing a single bandit algorithm that performs nearly as well as the best individual algorithm in a stochastic environment.
method Develops two general corralling algorithms that achieve favorable regret guarantees.
result The regret of the corralling algorithms is no worse than the best individual algorithm's performance.
New algorithms improve stochastic optimization and online learning efficiency.
problem Efficient optimization and online learning algorithms for stochastic problems.
method Accelerated randomized coordinate descent algorithms.
result Significantly less per-iteration complexity and better regret performance.
Automates algorithm design using reinforcement learning.
problem Manual algorithm design is time-consuming and error-prone.
method Guided policy search for optimizing policies representing algorithms.
result Learned algorithm outperforms existing hand-engineered algorithms.
The exchange algorithm is studied for its convergence and asymptotic variance.
problem Theoretical limitations of the exchange algorithm in sampling from doubly-intractable distributions.
method Theoretical analysis of the exchange algorithm's convergence speed and asymptotic variance.
result The exchange algorithm converges at a geometric rate and satisfies a Central Limit Theorem.
New algorithms optimize algorithm parameters in online settings with reduced computational costs.
problem Optimizing algorithm parameters in online settings with volatile and discontinuous losses.
method Developed semi-bandit optimization algorithms that leverage extra information to reduce computational costs.
result Achieved regret bounds as good as full-information feedback with significantly less computational effort.
Bayesian networks (BN) are used in a big range of applications but they have one issue concerning parameter learning. In real application, training data are always incomplete or some nodes are hidden. To deal with this problem many learning parameter algorithms are suggested foreground EM, Gibbs sampling and RBE algori…
Parallel algorithm finds sparse solutions for nonconvex problems.
problem Nonconvex sparsity-regularized rank minimization.
method Parallel best-response algorithm with exact line search.
result Guaranteed convergence to a stationary point.
No algorithm outperforms uniform sampling in A/B testing.
problem Identifying the best arm in A/B testing with fixed budget.
method Introducing consistent and stable algorithms, deriving lower bounds, and proving optimality of uniform sampling.
result No algorithm performs better than uniform sampling in A/B testing.
Improves algorithm selection for thousands of candidates using dyadic features.
problem Selecting the best algorithm from a large set of candidates for specific problems.
method Proposes extreme algorithm selection (XAS) with dyadic feature representation.
result Improves significantly over current state of the art in various metrics.
AIDE measures the accuracy of probabilistic inference algorithms.
problem Measuring the accuracy of approximate inference algorithms on specific data sets.
method AIDE is an algorithm based on viewing inference algorithms as probabilistic models and auxiliary variables.
result AIDE captures the qualitative behavior of inference algorithms and detects failure modes.
New algorithms decode Markov chains with near-optimal performance, even with small latency.
problem Online decoding of n t h n^{th} n t h order ergodic Markov chains with latency constraints. method Deterministic and randomized algorithms using dynamic programs, with lower bounds established.
result Near-optimal performance of algorithms with minimal latency, outperforming existing methods.
Combines online learning algorithms to achieve better performance.
problem Improving online learning algorithms with varying guarantees.
method Adding iterates of two parameter-free algorithms to create a new algorithm with improved regret.
result Generates efficient algorithms that adapt to multiple norms and maintain dimension-free guarantees.
New ELM algorithms reduce computation time and complexity.
problem Efficient computation of extreme learning machine (ELM) algorithms.
method Developed inverse-free ELM algorithms using recursive matrix inverse and inverse LDL' factorization.
result Proposed algorithms significantly reduce computational complexity.
This review article surveys data augmentation MCMC algorithms.
problem Sampling from intractable probability distributions.
method Comprehensive study of DA MCMC algorithms, their convergence properties, and acceleration strategies.
result Synthesizes recent developments and provides insights for researchers.
Bayesian learning rule unifies and generalizes various machine learning algorithms.
problem Machine learning algorithms are diverse and not always understood.
method Bayesian principles and natural gradients are used to derive algorithms.
result Derives a wide range of algorithms including classical and modern ones.
This review summarizes five Lasso optimization algorithms.
problem Optimizing the Lasso objective function.
method Five representative algorithms: ISTA, FISTA, CGDA, SLA, PFA.
result Comparison of convergence rates and strengths/weaknesses.
Neural networks mimic algorithms to solve complex problems.
problem Current machine learning methods struggle with generalisation and efficiency.
method Representing algorithms in a continuous space and adapting them to real-world problems.
result Neural networks can execute classical algorithms more efficiently.
Paper proposes a reinforcement learning framework for efficient hyper-parameter tuning of stochastic optimization algorithms.
problem Efficient tuning of hyper-parameters for stochastic optimization algorithms.
method Modeling hyper-parameter tuning as a Markov decision process and using policy gradient algorithms.
result The proposed framework significantly reduces the time required for hyper-parameter tuning compared to Bayesian optimization.
Describes MLC search spaces in MEKA and WEKA software.
problem Understanding MLC algorithms and their transformations into SLC problems.
method Overviewed 26 MLC algorithms and 28 SLC algorithms, proposed a context-free grammar.
result Formal description of MLC search spaces and their transformations.
New algorithms reduce bilevel optimization complexity to ε^(-1.5).
problem Efficiently solving bilevel optimization problems in machine learning.
method Proposed two new algorithms: one using momentum-based recursive iterations, the other using recursive gradient estimations.
result Achieved computational complexity of ε^(-1.5), significantly faster than previous methods.
Researchers analyze how algorithmic and implementation choices affect RL performance.
problem Difficulty in separating algorithmic and implementation differences in RL performance.
method Unified derivations through a single control-as-inference objective, categorizing algorithms as EM or KL minimization.
result Implementation details are co-adapted with algorithmic choices, some transferable across algorithms.
Study on selecting between base algorithms in stochastic bandit problems.
problem Model selection in stochastic environments with contextual information.
method Developed a meta-algorithm-base algorithm abstraction with a smoothing transformation for optimal O ( T ) O(\sqrt{T}) O ( T ) guarantees. result Optimal O ( T ) O(\sqrt{T}) O ( T ) model selection guarantees for stochastic contextual bandit problems. New bounds derived for KG algorithm's performance in finite time.
problem Best arm identification problem in multi-armed bandit.
method Theoretical analysis of finite-time performance, deriving bounds for sample allocation, error probability, and regret.
result Upper and lower bounds for the probability of error and simple regret of the KG algorithm.
Paper proves linear convergence of SCMS algorithm for directional data.
problem Identifying density ridges in directional data.
method Generalized SCMS algorithm to directional data, derived from SCGA with adaptive step size.
result Linear convergence of the proposed directional SCMS algorithm.
MLE and CVE are equivalent under exponential families, leading to faster and more stable EM algorithms.
problem Finding maximum likelihood estimators (MLE) efficiently and stably.
method Proved equivalence between MLE and CVE under exponential families, leading to an EM algorithm.
result EM algorithm achieves the same asymptotic variance as MLE and is faster and more stable.
EB algorithm improves matrix completion accuracy and efficiency.
problem Matrix completion problems, especially when rows and columns differ significantly.
method Empirical Bayes (EB) algorithm based on singular value shrinkage.
result EB algorithm outperforms existing methods in accuracy and efficiency.
The paper introduces stability of learning algorithms and bounds their generalization error.
problem Understanding and bounding the generalization error of learning algorithms.
method Introduces argument stability and uses martingale inequalities in Banach spaces.
result Bounds the generalization error of learning algorithms in terms of their argument stability.
New algorithm speeds up learning of graphical models.
problem Learning graphical models with sparse structure efficiently.
method Vertex-greedy score-based algorithm for learning DAGs.
result Polynomial runtime for learning DAG models.