Study robust utility maximization with uncertain continuous semimartingales.
problem Maximizing utility in continuous time under model uncertainty.
method Duality and conjugate problems for logarithmic, exponential, and power utilities.
result Existence of optimal portfolios for various utilities.
We tackle robust influence maximization in social networks with hyperparametric edge probabilities.
problem Maximizing worst-case influence in social networks with hyperparametric edge probabilities.
method Proposed a model with NP-hard proper robust optimization, using sampling and multiplicative weight updates.
result Empirically validated method outperforms state-of-the-art robust influence maximization techniques.
MMA training maximizes margins for adversarial robustness.
problem Adversarial robustness of neural networks.
method Directly maximizes margins through adaptive adversarial training.
result MMA training improves adversarial robustness compared to fixed ε adversarial training.
Investor maximizes utility from an unknown claim using robust optimization.
problem Maximizing utility from an unknown contingent claim.
method Robust optimization with quantile formulation and variational inequalities.
result Optimal trading strategy and utility indifference price determined.
Improves robustness of ReLU networks by maximizing linear regions.
problem Neural networks are not robust, raising concerns for safety-critical systems.
method Maximizes linear regions and distance to decision boundary.
result Improves robustness and minimizes adversarial perturbations.
The paper tackles adversarial robustness by maximizing worst-case mutual information.
problem Training robust machine learning models against adversarial inputs is challenging.
method Develops a notion of representation vulnerability and an unsupervised learning method to maximize worst-case mutual information.
result Proves a lower bound on minimum adversarial risk and supports robustness of representations.
Solves utility maximization with uncertainty in drift and volatility.
problem Maximizing terminal wealth with uncertainty in stock drift and volatility.
method Explicit solutions for utility maximization under Knightian uncertainty.
result Solves robust optimization problems with various utility functions.
This work proposes SDI regularization to improve adversarial robustness.
problem Improving adversarial robustness of deep neural networks.
method SDI regularization term to complement adversarial training.
result Combining SDI with AT variants enhances robustness and generalization.
Unified framework for robust submodular optimization with various constraints.
problem Robust optimization in machine learning applications.
method Unified framework for minimization and maximization under combinatorial constraints.
result Scalable approximation algorithms for various submodular optimization problems.
In this paper we study a robust expected utility maximization problem with random endowment in discrete time. We give conditions under which an optimal strategy exists and derive a dual representation for the optimal utility. Our approach is based on a general representation result for monotone convex functionals, a fu…
This paper solves robust utility maximization with unknown claim dependencies.
problem Investor optimizes utility in the presence of an intractable contingent claim.
method Quantile optimization approach, transforming dynamic problem into static concave optimization.
result Optimal payoffs depend on ambiguity attitude, market conditions, and claim characteristics.
No arbitrage holds if a Pareto solution exists for vector-valued utility maximization.
problem Existence of no arbitrage in markets with transaction costs and multiple assets.
method Prove no arbitrage condition equivalent to Pareto solution for vector-valued utility maximization.
result A consistent price process can be constructed from the Pareto maximizer.
We study the convex duality method for robust utility maximization in the presence of a random endowment. When the underlying price process is a locally bounded semimartingale, we show that the fundamental duality relation holds true for a wide class of utility functions on the whole real line and unbounded random endo…
Study on robust utility maximization with nonconcave utility functions under projective determinacy.
problem Investor's optimal investment strategy under model ambiguity and nonconcave utility.
method Projective functions of the path and sets of priors, upper-semicontinuous utility.
result Existence of optimal investment strategy under PD.
The paper assesses text classification robustness through maximal safe radius computation.
problem Vulnerability of neural network models to small input modifications.
method Maximal safe radius computation, Monte Carlo Tree Search, syntactic filtering, linear bounding techniques.
result Approximation methods for computing upper and lower bounds of maximal safe radius.
We study a robust stochastic optimization problem in the quasi-sure setting in discrete-time. We show that under a lineality-type condition the problem admits a maximizer. This condition is implied by the no-arbitrage condition in models of financial markets. As a corollary, we obtain existence of an utility maximizer …
Adaptive learning method identifies and corrects corrupted data.
problem Robust learning from corrupted training sets.
method Identifies corrupted and non-corrupted samples with latent Bernoulli variables, formulates as likelihood maximization with marginalized latent variables, solved via variational inference and Expectation-Maximization.
result Improves over state-of-the-art by automatically inferring corruption level with minimal overhead.
New models improve classification model performance, especially robust to small training sets.
problem Improving classification model performance, especially robust to small training sets.
method Distributionally robust AUC maximization models using Kantorovich metric and hinge loss function.
result The proposed DR-AUC models outperform standard models in general and worst-case out-of-sample performance.
The paper studies the robust maximization of utility of terminal wealth in the diffusion financial market model. The underlying model consists with risky tradable asset, whose price is described by diffusion process with misspecified trend and volatility coefficients, and non-tradable asset with a known parameter. The …
We study the problem of maximizing a monotone submodular function subject to a cardinality constraint k, with the added twist that a number of items τ from the returned set may be removed. We focus on the worst-case setting considered in (Orlin et al., 2016), in which a constant-factor approximation guarantee was g…
For a stochastic factor model we maximize the long-term growth rate of robust expected power utility with parameter λ∈(0,1). Using duality methods the problem is reformulated as an infinite time horizon, risk-sensitive control problem. Our results characterize the optimal growth rate, an optimal long-term trading s…
Maximizes coding rate difference for robust, discriminative features.
problem Learning robust, discriminative features from high-dimensional data.
method Maximal Coding Rate Reduction (MCR^2) principle.
result Significantly more robust to label corruptions in classification.
Paper connects contrastive learning to MI maximization and establishes robust methods for nonlinear ICA and subspace estimation.
problem Understanding and improving unsupervised representation learning and density ratio estimation.
method The paper connects contrastive learning to MI maximization, establishes new recovery conditions for nonlinear ICA, and proposes a practical outlier-robust method for nonlinear subspace estimation.
result The proposed methods can be seen as maximizing MI, performing nonlinear ICA, or estimating nonlinear subspaces, and are robust to outliers.
Robust state-space radio interferometric imaging using Stochastic Approximation Expectation Maximization
problem Improving state-space radio interferometric imaging in the presence of heavy-tailed noise
method Stochastic Approximation Expectation Maximization
result Significant improvement in reconstruction fidelity and robustness to radio-frequency interference
We consider the robust exponential utility maximization problem in discrete time: An investor maximizes the worst case expected exponential utility with respect to a family of nondominated probabilistic models of her endowment by dynamically investing in a financial market, and statically in available options. We show …
The paper tackles robust submodular maximization under matroid constraints, providing approximation algorithms for summary extraction.
problem Maximizing submodular functions while ensuring high value even after deletions.
method Constant-factor approximation algorithms for centralized and streaming settings, considering both non-monotone and monotone objectives.
result Approximation algorithms with space complexity depending on matroid rank and deleted elements, achieving improved factors in monotone cases.
Paper develops duality theory for robust utility maximization in continuous time.
problem Maximizing utility in the presence of uncertainty.
method Introduces a duality theory for continuous-time robust utility maximization problems.
result Shows duality between robust utility maximization and a conjugate problem under certain conditions.
The paper proposes effective margin regularization to improve adversarial robustness in deep neural networks.
problem Adversarial vulnerability of deep neural networks (DNNs).
method Regularization of effective weight norm during training to maximize effective margins.
result Effective margin regularization (EMR) boosts adversarial robustness in both standard and adversarial training.
Bayesian quadrature optimization tackles uncertainty in distributional samples.
problem Maximizing an expensive black-box integrand under distributional uncertainty.
method Distributionally robust optimization perspective, posterior sampling.
result Empirical effectiveness and theoretical convergence demonstrated.
Optimal financial strategies minimize risk under uncertain models.
problem Maximizing utility in financial markets with model uncertainty.
method Optimized strategies converge to those with minimal norm as uncertainty increases.
result Optimal strategies with minimal norm emerge as uncertainty grows.
Investigates optimal strategies under financial uncertainty, proving convergence as uncertainty increases.
problem Utility maximization in financial markets with model uncertainty.
method Explicit representation of optimal strategy, minimax theorem, convergence analysis.
result Optimal strategy converges to a generalized uniform diversification strategy as uncertainty increases.
The paper develops algorithms to find a robust summary of data under deletion, achieving good approximation guarantees.
problem Finding a summary of data that remains valuable even after some elements are deleted.
method Constant-factor approximation algorithms for deletion robust submodular maximization under matroid constraints.
result The algorithms provide good approximation guarantees for both centralized and streaming settings.
Deep models maximize minimum margin for high accuracy but decrease average margin, leading to poor robustness.
problem Inadequate balance between accuracy and robustness in deep model training.
method Analyzed the training process of deep models and proposed a new regularizer to promote average margin.
result Demonstrated an intrinsic trade-off between accuracy and robustness, and proposed a regularizer to improve robustness.
In hyperspectral images, some spectral bands suffer from low signal-to-noise ratio due to noisy acquisition and atmospheric effects, thus requiring robust techniques for the unmixing problem. This paper presents a robust supervised spectral unmixing approach for hyperspectral images. The robustness is achieved by writi…
Paper proposes robust methods for estimating optimal treatment rules with censored survival data.
problem Estimating optimal treatment rules for censored survival data.
method Developed two robust criteria and a sampling-based difference-of-convex algorithm for learning optimal treatment rules.
result Proposed methods show improved performance compared to existing methods in simulations and real data.
Generates confident out-of-distribution samples to improve classifier robustness.
problem Overconfidence in deep learning models on out-of-distribution inputs.
method Uses a GAN to generate out-of-distribution samples that the classifier is confident on, maximizing entropy.
result Shows effectiveness on handwritten characters and natural images datasets.
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.
Paper analyzes a generalized EM algorithm for Gaussian mixtures in control systems.
problem Parametric distribution-based clustering in unsupervised learning.
method Proposes a generalized EM (GEM) algorithm for Gaussian mixture models, analyzing its convergence properties using control theory.
result GEM algorithm can be understood as a linear time-invariant system with feedback nonlinearity.
MACER trains robust models without adversarial training, faster and more effective.
problem Learning robust models without relying on attack-dependent adversarial training.
method MACER trains provably robust smoothed classifiers by maximizing certified radius.
result MACER achieves larger average certified radius and faster training time compared to state-of-the-art methods.
New algorithm for robust Boolean matrix factorization handles noise and missing data.
problem Robust probabilistic Boolean matrix factorization in the presence of noise and missing values.
method Probabilistic Expectation Maximization algorithm without latent factor assumptions.
result Outperforms state-of-the-art probabilistic algorithms on real data.
This paper explores adversarial robustness of flow-based generative models.
problem Robustness of flow-based generative models to adversarial attacks.
method Theoretical and empirical analysis of adversarial robustness for simple and complex flow-based models.
result Flow-based generative models are highly sensitive to adversarial attacks, but robustness can be significantly improved using hybrid adversarial training.
MIRO learns robust latent spaces by maximizing mutual information with future information.
problem Robust perception in complex, unstructured environments with low sample complexity.
method MIRO maximizes mutual information in a latent space for model-based reinforcement learning.
result MIRO outperforms reconstruction objectives in cluttered scenes.
New adversarial examples from crypto generators show robust machine learning challenges.
problem Adversarial examples in machine learning due to cryptographic pseudo-random generators.
method Constructing a binary classification task with maximal robustness and proving computational hardness under cryptographic assumptions.
result Maximally robust classifiers can tolerate perturbations of size comparable to the examples themselves, highlighting computational hardness.
New ranking system balances fairness and user utility.
problem Achieving group fairness in ranking systems.
method Formulated a minimax game between a ranking player and an adversary.
result Better utility for highly fair rankings.
A new clustering algorithm fuses heat diffusion and turning angle for robustness.
problem Cluster similar elements in various fields.
method Combines heat diffusion and maximal turning angle for robust fission clustering.
result The SARFC algorithm outperforms other methods in clustering performance.
Adversarial training converges to robust neural networks.
problem Vulnerability of neural networks to adversarial examples.
method Adversarial training alternates minimization and maximization steps.
result Adversarial training finds a robust classifier close to optimal robust loss.
In this paper the robust utility maximization problem for a market model based on Lévy processes is analyzed. The interplay between the form of the utility function and the penalization function required to have a well posed problem is studied, and for a large class of utility functions it is proved that the dual probl…
This paper tackles robust growth maximization with stochastic factors, finding optimal strategies independent of the factor process.
problem Maximizing asymptotic growth under model uncertainty with stochastic factor processes.
method Combines techniques from partial differential equations, calculus of variations, and generalized Dirichlet forms.
result Optimal trading strategy is functionally generated and independent of the stochastic factor process.