A new method uses literature constraints to improve phenotyping from EHR data.
problem Improving phenotyping from electronic health records (EHR) data.
method Constrained tensor factorization with literature constraints.
result Improved phenotyping results for hypertensive patients.
Lasso method applied to polynomial models with hierarchy constraints.
problem Estimating parameters in polynomial models with hierarchy constraints.
method Using lasso and standard quadratic programming techniques to estimate parameters.
result The proposed methodology outperforms existing techniques in terms of validation error and model size.
New method reduces total cost constraints in CBwK to sqrt(T) with fairness application.
problem Maximize rewards while adhering to total cost constraints in CBwK.
method Dual strategy based on projected-gradient-descent updates.
result Total cost constraints reduced to sqrt(T) with poly-logarithmic terms.
New conic quadratic formulations improve outlier detection in regression models.
problem Detecting outliers in regression models with corrupted data.
method Deriving stronger second-order conic relaxations without big-M constraints.
result Proposed formulations are significantly faster than existing methods.
The paper studies the properties of maps with free boundaries, focusing on the obstacle case.
problem Properties of the projected image and its regularity in maps with free boundaries.
method Dividing the map into distance and projected image parts; applying classical obstacle problem methods and proving higher regularity for the projected image.
result The projected image is at most of class C2,1 and globally of class W3,BMO, locally of C2,1 around the regular part of the free boundary. We study the supersymmetric Wilson loop as introduced by Caron-Huot, which attaches to lightlike polygons certain edge and vertex operators, whose shape is determined by supersymmetry constraints. We state explicit formulas for the vertex operators to all orders in the Graßmann expansion, thus filling a gap in the lite…
Study on adaptivity constraints in linear contextual bandits with optimal design.
problem Impact of adaptivity constraints on linear contextual bandits.
method Two models of limited adaptivity: batch learning and rare policy switches. Proposed distributional optimal design.
result Achieves minimax-optimal regret with optimal number of policy switches and batches.
As one of the most important types of (weaker) supervised information in machine learning and pattern recognition, pairwise constraint, which specifies whether a pair of data points occur together, has recently received significant attention, especially the problem of pairwise constraint propagation. At least two reaso…
Proves strict inequality for minimizers of Willmore energy under isoperimetric constraints.
problem Minimizing the Willmore energy under isoperimetric constraints.
method Connected sum approach, building on previous work by Keller-Mondino-Rivière.
result Existence of minimizers for the isoperimetric constrained Willmore problem in every genus.
A new algorithm solves constrained optimization problems with stochastic gradients.
problem Nonlinear equality constrained optimization with rank-deficient Jacobians.
method Step decomposition strategy combining normal and tangential steps.
result Convergence guarantees in rank-deficient Jacobian cases.
New loss function handles uncertain constraints in CSLO problems.
problem Handling uncertain inequality constraints in CSLO with machine learning predictions.
method Introduces SPO-RC loss and SPO-RC+ surrogate, trains on truncated datasets, corrects bias.
result SPO-RC+ effectively manages constraint uncertainty and improves performance.
Paper finds robust Λ-quantiles equal to extremal distributions.
problem Investigating robust models for Λ-quantiles with partial loss information. method Extending classical quantiles using Λ-quantiles and applying results from robust quantiles. result Robust Λ-quantiles equal to Λ-quantiles of extremal distributions. Variational calculus on a vector bundle E equipped with a structure of a general algebroid is developed, together with the corresponding analogs of Euler-Lagrange equations. Constrained systems are introduced in the variational and in the geometrical setting. The constrained Euler-Lagrange equations are derived for ana…
Develops a new method for optimizing with uncertain data.
problem Uncertainty in real-world optimization problems.
method Combines chance constraints and constraint learning for mixed-integer linear optimization.
result Data-driven solution for setting probabilistic bounds on learned constraints.
Dynamic risk constraints help limit risky behavior in financial portfolios.
problem Static risk measures fail to control tail-risk-seeking traders.
method Introduces dynamic risk constraints applied throughout the trading horizon.
result Dynamic risk constraints can effectively limit risky behavior in portfolios.
Develops an algorithm for bilevel optimization with coupled constraints.
problem Challenges in bilevel optimization with coupled constraints.
method Primal-dual-assisted penalty approach and a fully first-order algorithm (BLOCC).
result Established rigorous convergence theory and demonstrated effectiveness on real-world applications.
We extend Relative Robust Portfolio Optimisation models to allow portfolios to optimise their distance to a set of benchmarks. Portfolio managers are also given the option of computing regret in a way which is more in line with market practices than other approaches suggested in the literature. In addition, they are gi…
Three classes of algorithms to learn the structure of Bayesian networks from data are common in the literature: constraint-based algorithms, which use conditional independence tests to learn the dependence structure of the data; score-based algorithms, which use goodness-of-fit scores as objective functions to maximise…
In literature there are several studies on the performance of Bayesian network structure learning algorithms. The focus of these studies is almost always the heuristics the learning algorithms are based on, i.e. the maximisation algorithms (in score-based algorithms) or the techniques for learning the dependencies of e…
Paper solves MV portfolio selection in jump-diffusion models with no-shorting constraint.
problem Mean-variance portfolio selection in jump-diffusion model with no-shorting constraint.
method Reduces problem to LQ control and finding a maximal point of a function, constructs viscosity solution.
result Explicit viscosity solution to Hamilton-Jacobi-Bellman equation, optimal controls derived.
The majority of stylized facts of financial time series and several Value-at-Risk measures are modeled via univariate or multivariate GARCH processes. It is not rare that advanced GARCH models fail to converge for computational reasons, and a usual parsimonious approach is the GJR-GARCH model. There is a disagreement i…
New framework for distributed nonparametric estimation under slow communication.
problem Efficiently estimate nonparametric models across multiple nodes with limited communication.
method Developed a general framework for nonparametric estimation under communication constraints.
result Derived minimax lower and upper bounds for various models.
A new Dirac algebroid approach for nonholonomic systems.
problem Nonholonomic constraints in mechanical systems.
method Developed a Dirac algebroid to generate phase equations for systems with linear nonholonomic constraints.
result Unified approach to describe systems with different potentials.
Proposes a new algorithm for learning continuous-time Bayesian network structures.
problem Lack of constraint-based algorithms for continuous-time Bayesian networks.
method Develops a constraint-based algorithm using statistical tests for conditional independence.
result The proposed algorithm is more accurate with variables having more than two values.
Adversarial training improves model robustness with Voronoi constraints.
problem Adversarial examples mislead machine learning models, leading to incorrect classifications.
method Geometric framework using Voronoi cells to constrain adversarial training.
result Adversarial training with Voronoi constraints produces robust models.
This paper optimizes dividend payout rates with a drawdown constraint in a stochastic model.
problem Optimizing dividend payout rates while avoiding drawdowns in a stochastic model.
method Solving a path-dependent stochastic control problem using Hamilton-Jacobi-Bellman equations and PDE methods.
result Explicit characterization of an optimal feedback control strategy, including two free boundaries and the running maximum surplus process.
This paper considers the mean-reverting portfolio design problem arising from statistical arbitrage in the financial markets. We first propose a general problem formulation aimed at finding a portfolio of underlying component assets by optimizing a mean-reversion criterion characterizing the mean-reversion strength, ta…
Study shows how capital constraints can lead to systemic crises in financial systems.
problem Impact of regulatory capital constraints on fire sales and financial stability.
method Mean field game model with banks adjusting holdings via trading strategies under regulatory constraints.
result Capital constraints can lead to simultaneous defaults in a substantial proportion of the banking system.
This paper addresses dynamic price discrimination with fairness constraints.
problem Dynamic price discrimination with fairness constraints in online retailing.
method Nonparametric demand models, dynamic pricing policy, regret minimization.
result Optimal dynamic pricing policy with ildeO(T4/5) regret for price fairness. This paper considers the problem of minimizing an expectation function over a closed convex set, coupled with a {\color{black} functional or expectation} constraint on either decision variables or problem parameters. We first present a new stochastic approximation (SA) type algorithm, namely the cooperative SA (CSA), t…
The paper extends utility maximization by integrating partial information and robust VaR constraints.
problem Optimal investment under partial information and robust VaR-type constraints.
method Combines partial information and robust regulatory constraints (VaR) to solve the utility maximization problem.
result Optimal wealth is a decreasing function of state price density, and depends on the overall evolution of the estimated market price of risk.
Algorithm tackles clustered contextual bandits with resource constraints.
problem Maximizing reward while respecting resource limits in unknown cluster contexts.
method Combines econometrics and bandit constraints techniques for sublinear regret.
result Achieves sublinear regret without needing all arm information.
We address the problem of finding the maximizer of a nonlinear smooth function, that can only be evaluated point-wise, subject to constraints on the number of permitted function evaluations. This problem is also known as fixed-budget best arm identification in the multi-armed bandit literature. We introduce a Bayesian …
In spite of the growing consideration for optimal execution in the financial mathematics literature, numerical approximations of optimal trading curves are almost never discussed. In this article, we present a numerical method to approximate the optimal strategy of a trader willing to unwind a large portfolio. The meth…
Study optimal consumption and investment strategies with constraints in a market with random coefficients.
problem Optimal consumption and investment strategies with constraints in a regime switching market with random coefficients.
method Explicit optimal strategies provided via solutions to new BSDE systems.
result Solving new BSDEs to find optimal values and strategies.
Survey on statistical inference under memory constraints.
problem Effect of memory limitations on statistical inference performance.
method Review of state-of-the-art in several canonical problems.
result Identification of fundamental building blocks and useful techniques.
Eigen-decomposition simplifies quadratic programming with equality constraints.
problem Optimizing solutions under linear equality constraints in quadratic programming.
method Eigenvalue decomposition of the quadratic term matrix to project optimal solutions.
result Established a linear mapping between EQP formulations with and without diagonalized Q. This paper explores the nonconvexity of push-forward constraints in machine learning.
problem The nonconvexity of push-forward constraints in machine learning.
method The paper provides sufficient and necessary conditions for the (non)convexity of push-forward functions and maps.
result Push-forward constraints are generally nonconvex, which limits the design of convex optimization problems in machine learning.
Fairness constraints can improve accuracy from biased data.
problem Learning from biased training data can produce biased and suboptimal classifiers.
method Examined fairness-constrained ERM and other recovery methods.
result Equal Opportunity fairness constraint combined with ERM provably recovers Bayes Optimal Classifier under various bias models.
New policy minimizes error in finding best arm with privacy constraints.
problem Best arm identification with differential privacy constraints.
method Maximum absolute determinants principle for ε-DP policy. result Error probability decays exponentially in T with matching exponents. Financial portfolios are often optimized for maximum profit while subject to a constraint formulated in terms of the Conditional Value-at-Risk (CVaR). This amounts to solving a linear problem. However, in its original formulation this linear problem has a very large number of linear constraints, too many to be enforced…
Paper solves investment and consumption problem with unknown risk, providing explicit solutions.
problem Solving consumption-investment problem with unknown market price of risk and terminal liability constraint.
method Introduced a coupled forward-backward stochastic differential equation (FBSDE) and provided an explicit solution.
result Explicit expressions for optimal investment strategy and value function derived.
Novel evolutionary strategy solves stochastic constrained optimization problems.
problem Optimizing objective functions with stochastic constraints in reinforcement learning.
method Design of a novel optimization algorithm with a sufficient decrease mechanism for stochastic constrained problems.
result Demonstrated convergence of the algorithm on control tasks and constrained optimization problems.
Investigates optimal portfolios with risk-free assets, minimizing investment risk.
problem Investment risk minimization with budget and return constraints.
method Replica analysis and exploration of implications of a risk-free asset.
result Implications of a risk-free asset on optimal portfolio and investment risk.
New algorithms for efficient causal interventions with budget constraints and without constraints.
problem Efficiently learning best interventions in causal graphs with budget constraints.
method Developed algorithms for both budgeted and non-budgeted causal bandits, optimizing regret and side-information usage.
result Proposed algorithms minimize cumulative regret and perform better than standard methods.
Analyzes securitization impacts on monetary and fiscal policies.
problem Impact of securitization on monetary and fiscal policies.
method Develops optimal conditions, identifies constraints, introduces new decision models.
result Identifies constraints and interactions of securitization with capital-reserve requirements.
In this paper, as a first step in examining the properties of a feasible portfolio subset that is characterized by budget and risk constraints, we assess the maximum and minimum of the investment concentration using replica analysis. To do this, we apply an analytical approach of statistical mechanics. We note that the…
A new error bound improves safety in Bayesian optimization.
problem Ensuring safety in Bayesian optimization with probabilistic models.
method Introducing a novel error bound using Wiener kernel regression for Gaussian processes and noise.
result The new error bound provides larger safety regions than previous methods.