Developed a Particle-Gibbs sampler for Bayesian feature allocation models.
problem Intractable exact inference in Bayesian feature allocation models.
method Particle-Gibbs sampler for feature allocation matrix updates.
result PG sampler improves performance of feature allocation models.
New model allocates features sublinearly, improving model fit and performance.
problem Linear growth of shared features limits model flexibility and performance.
method Developed non-exchangeable feature allocation models with sublinear feature sharing.
result Sublinear feature sharing leads to better model fit and predictive performance.
Proposes a flexible feature allocation model for sparse factor analysis.
problem Sparse data and rigid assumptions in traditional exploratory tools.
method Adaptive latent feature sharing with control over feature sparsity.
result Derives a novel adaptive Factor analysis (aFA) and aPPCA for flexible dimensionality reduction.
We characterize the class of exchangeable feature allocations assigning probability Vn,k∏l=1kWmlUn−ml to a feature allocation of n individuals, displaying k features with counts (m1,…,mk) for these features. Each element of this class is parametrized by a countable matrix V…
We study classification problems where features are corrupted by noise and where the magnitude of the noise in each feature is influenced by the resources allocated to its acquisition. This is the case, for example, when multiple sensors share a common resource (power, bandwidth, attention, etc.). We develop a method f…
Infinite mixture models are commonly used for clustering. One can sample from the posterior of mixture assignments by Monte Carlo methods or find its maximum a posteriori solution by optimization. However, in some problems the posterior is diffuse and it is hard to interpret the sampled partitionings. In this paper, we…
ACGAN improves portfolio allocation by learning trends and uncertainty.
problem Markowitz framework's overemphasis on market uncertainty.
method Autoencoding CGAN (ACGAN) that learns trends and uncertainty.
result ACGAN leads to better portfolio allocation and more accurate series.
Paper uses surprisal to dynamically allocate computation between fast and slow models.
problem Dynamic allocation of computation in neural networks.
method Surprisal-based dynamic model selection.
result Model can match baseline performance with 15% fewer FLOPs.
The financial crisis of 2008 generated interest in more transparent, rules-based strategies for portfolio construction, with Smart beta strategies emerging as a trend among institutional investors. While they perform well in the long run, these strategies often suffer from severe short-term drawdown (peak-to-trough dec…
Paper proposes integrating wavelet transform, channel attention, and LSTM for better stock price prediction.
problem Inherently difficult stock price prediction due to low signal-to-noise ratio.
method Wavelet transform convolution, channel attention, and LSTM integration.
result Robust performance in post-pandemic market conditions.
Uber optimizes marketplace levers using machine learning to improve resource allocation efficiency.
problem Optimizing budget allocation for drivers and riders to maximize business value.
method End-to-end machine learning and optimization procedure using feature store, model training, and ADMM.
result Substantially improved Uber's resource allocation efficiency through high-dimensional optimization.
Paper learns data-driven organ matching rules from observational data.
problem Tackles organ transplantation compatibility using observational data.
method Representation learning to cluster donors and apply recipient transformations.
result Model outperforms human experts in predicting transplant outcomes.
New method for interpreting financial model risks.
problem Fairly allocating risk in financial models.
method Extending Shapley value framework for axiomatic risk attribution.
result Risk can be well allocated in financial models.
Transformer model improves asset allocation by unifying forecasting and optimization.
problem Separation of forecasting and optimization leads to suboptimal portfolios.
method Signature Informed Transformer using path signatures and specialized attention.
result Direct minimization of Conditional Value at Risk improves performance.
Feature extraction has gained increasing attention in the field of machine learning, as in order to detect patterns, extract information, or predict future observations from big data, the urge of informative features is crucial. The process of extracting features is highly linked to dimensionality reduction as it impli…
Enhances portfolio construction with tailored regime forecasts for individual assets.
problem Traditional portfolio construction methods fail to account for asset-specific market conditions.
method Hybrid framework combining unsupervised and supervised learning for regime identification and forecasting.
result Outperforms traditional portfolio models across various asset classes.
We investigate a class of feature allocation models that generalize the Indian buffet process and are parameterized by Gibbs-type random measures. Two existing classes are contained as special cases: the original two-parameter Indian buffet process, corresponding to the Dirichlet process, and the stable (or three-param…
We present a consensus Monte Carlo algorithm that scales existing Bayesian nonparametric models for clustering and feature allocation to big data. The algorithm is valid for any prior on random subsets such as partitions and latent feature allocation, under essentially any sampling model. Motivated by three case studie…
Dynamic model considers private asset markets' complexities.
problem Understanding and optimizing private asset allocation.
method State-of-the-art dynamic model with machine learning.
result Optimal investment policies quantified over fund life.
We show that, in a resource allocation problem, the ex ante aggregate utility of players with cumulative-prospect-theoretic preferences can be increased over deterministic allocations by implementing lotteries. We formulate an optimization problem, called the system problem, to find the optimal lottery allocation. The …
A novel federated learning framework resolves structural misalignment in model fusion.
problem Structural misalignment in model fusion due to chaotic information distribution.
method Feature-oriented regulation method (Ψ-Net) to ensure feature information allocation and dedicated collaboration schemes. result Effective enhancement of federated learning applicability to heterogeneous settings with improved convergence speed, accuracy, and efficiency.
This paper explains CART random forests using stochastic control theory.
problem Understanding the inner workings of CART random forests.
method Developed a stochastic-control perspective on CART random forests, interpreting feature subsampling as a random feasible action set and the split rule as a policy.
result Established that the CART policy is locally stabilizing but globally suboptimal for the forest objective.
Modified CTGAN-Plus-Features method optimizes asset allocation with CVaR constraint.
problem Optimizing portfolio weights in asset allocation problems.
method Combines synthetic data generation with CVaR-constraint optimization.
result Synthetic data captures key characteristics of original data and outperforms conventional strategies.
Modeling dynamic groundwater markets with price formation and trading strategies.
problem Understanding competitive effects in environmental markets with groundwater banking.
method Stochastic models and game theory with machine learning algorithms.
result Sub-game perfect Nash equilibria characterized by groundwater price processes.
The paper addresses risk sharing and variability measures among agents with general risk preferences.
problem Risk sharing and variability measures among agents with general risk preferences.
method Characterizes Pareto-optimal allocations using Gini deviation, mean-median deviation, and inter-quantile difference as variability measures.
result Optimal allocations are not comonotonic and feature a mixture of pairwise counter-monotonic structures.
We advocate the use of Agnostic Allocation for the construction of long-only portfolios of stocks. We show that Agnostic Allocation Portfolios (AAPs) are a special member of a family of risk-based portfolios that are able to mitigate certain extreme features (excess concentration, high turnover, strong exposure to low-…
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.
Optimizes trade execution with reinforcement learning for limit orders.
problem Maximizing revenue in a limit order book with market and limit orders.
method Formulated as a dynamic allocation task, uses multivariate logistic-normal distributions for efficient training.
result Outperforms traditional strategies in simulated environments.
Exclusive Group Lasso improves feature selection in correlated biological data.
problem Correlated features hinder Lasso performance in biological classification problems.
method Proposes and solves the exclusive group Lasso, combining stability selection and random group allocation.
result Exclusive Group Lasso outperforms Lasso in comprehensive selection of informative features.
Paper explores alternative cooperative game theory methods for machine learning feature attribution.
problem Debate over Shapley values' relevance in feature attribution.
method Introduces Weber and Harsanyi sets as alternative allocation schemes.
result Provides a coherent framework for designing robust feature attributions.
New method for risk allocation under multimodality of loss distribution.
problem Risk assessment under multimodal conditional loss distribution.
method Maximum Likelihood Allocation (MLA) and multimodality adjustment.
result Multimodality adjustment improves soundness of risk allocations.
Federated Machine Learning (FML) creates an ecosystem for multiple parties to collaborate on building models while protecting data privacy for the participants. A measure of the contribution for each party in FML enables fair credits allocation. In this paper we develop simple but powerful techniques to fairly calculat…
End-to-end neural network optimizes portfolios by directly learning allocations from features.
problem Error maximization in two-step portfolio optimization.
method Single feed-forward neural network combining prediction and optimization.
result Model-based end-to-end framework achieves Sharpe ratio of 1.16.
Investigates MAD-RP portfolios for asset allocation.
problem Finding optimal asset allocation strategies.
method Uses MAD as risk measure and proposes computational formulations for MAD-RP portfolios.
result MAD-RP portfolios offer balanced risk and profitability.
clusterBMA combines clustering results from multiple models using Bayesian model averaging.
problem Uncertainty in model selection for clustering.
method Bayesian model averaging to combine results from multiple clustering algorithms.
result ClusterBMA offers probabilistic cluster allocations and quantifies model-based uncertainty.
The article uses dynamic factor allocation to improve portfolio performance by integrating regime-switching signals.
problem Improving portfolio performance through dynamic factor allocation.
method The authors apply the sparse jump model (SJM) to identify bull and bear market regimes for individual factors, then fine-tune hyperparameters using a hypothetical single-factor long-short strategy. These regime inferences are incorporated into the Black-Litterman framework to dynamically adjust allocations among indices.
result The constructed multi-factor portfolio significantly improves the information ratio (IR) relative to the market, raising it from 0.05 to approximately 0.4.
New pricing framework allocates costs of operating reserves and transmission.
problem Allocating costs of operating reserves and transmission efficiently.
method Causation-based framework using contingency-constrained scheduling models.
result More comprehensive and efficient cost-reflective market operations.
We define the beta diffusion tree, a random tree structure with a set of leaves that defines a collection of overlapping subsets of objects, known as a feature allocation. A generative process for the tree structure is defined in terms of particles (representing the objects) diffusing in some continuous space, analogou…
The paper optimizes DIA purchase policies using lifecycle models and asset allocation.
problem Determining the optimal allocation to Deferred Income Annuities (DIAs).
method Employed a lifecycle model with utility of consumption and bequest, formalized optimization process, analyzed results, and extended model to include asset allocation.
result Optimal DIA allocation varies based on refundability, asset allocation, and perceived longevity.
We present the Wright-Fisher Indian buffet process (WF-IBP), a probabilistic model for time-dependent data assumed to have been generated by an unknown number of latent features. This model is suitable as a prior in Bayesian nonparametric feature allocation models in which the features underlying the observed data exhi…
OOMP selects features online for sparse linear regression.
problem Feature selection in high-dimensional sparse linear models.
method Online algorithm that alternates between feature selection and coefficient estimation.
result Theoretical guarantees and computational complexity analysis of OOMP.
Research develops a DSS for stock selection and asset allocation using fundamental data.
problem Complex financial markets and limited use of fundamental data analysis.
method Data gathering, cleaning, and modeling of fundamental data; integration with macroeconomic conditions.
result Enhanced predictive model for mid- to long-term stock returns.
Enhances topic models to better handle polysemous words.
problem Lack of polysemy handling in Gaussian latent Dirichlet allocation.
method Introduces a hierarchical structure to capture polysemy in Gaussian latent Dirichlet allocation.
result Significantly improves polysemy detection and provides more parsimonious topic representations.
The paper uses SHAP for interpreting machine learning models in hospital data.
problem Interpreting machine learning models in healthcare.
method SHAP for feature importance and feature packing techniques.
result SHAP provides better interpretability of machine learning models in healthcare.
Model predicts global financial market risks and asset allocation.
problem Predicting downside risk and market regime shifts.
method Dynamic regime switching model based on GARCH-DCC-Copula.
result Significantly improves risk and alpha-based asset allocation strategies.
This paper proposes a nonparametric Bayesian method for exploratory data analysis and feature construction in continuous time series. Our method focuses on understanding shared features in a set of time series that exhibit significant individual variability. Our method builds on the framework of latent Diricihlet alloc…
Optimal allocation between explainable and black box models for high performance and explainability.
problem Balancing explainability and performance in model ensembles.
method Optimal allocation of observations between explainable and black box models to maximize ensemble performance and explainability.
result Learned allocations maintain high ensemble performance and explainability, sometimes outperforming individual models.
The financial crisis showed the importance of measuring, allocating and regulating systemic risk. Recently, the systemic risk measures that can be decomposed into an aggregation function and a scalar measure of risk, received a lot of attention. In this framework, capital allocations are added after aggregation and can…