arXiv research

Detects causal scenarios with inequality constraints among classical correlations.

A framework for analyzing financial systems under scenario constraints.

Proposes a constraint for deep clustering to handle both simple and complex topologies.

Consider an agent who enters a financial market on day t = 0 with an initial capital amount x. He invests this amount on stocks and the money market, and by day t = T, has generated a wealth W . He is given a convex class of probability measures (called scenarios) and a real-valued function (or floors) corresponding to…

New method optimizes experiments under constraints.

New algorithm solves minimax games with linear constraints.

The paper improves robust optimization by introducing margin theory.

LEMs extend transformer-based architectures for complex execution problems.

We develop randomized (block) coordinate descent (CD) methods for linearly constrained convex optimization. Unlike most CD methods, we do not assume the constraints to be separable, but let them be coupled linearly. To our knowledge, ours is the first CD method that allows linear coupling constraints, without making th…

Optimizes power systems with energy storage under uncertainty using scenario-based method.

DePAint solves MARL for agents with local constraints, privacy, and no central controller.

As machine learning transitions increasingly towards real world applications controlling the test-time cost of algorithms becomes more and more crucial. Recent work, such as the Greedy Miser and Speedboost, incorporate test-time budget constraints into the training procedure and learn classifiers that provably stay wit…

New framework improves generative models with prediction and consistency constraints.

Logarithmic regret strategies for safe multi-armed bandits with safety risk constraints.

CPRA efficiently finds diverse solutions in CO problems using UL and parallelization.

Study proposes a neural network approach for high inflation investment portfolios with leverage constraints.

Nonholonomic mechanical systems have been attracting more interest in recent years because of their rich geometric properties and their applications in Engineering. In all generality, we discuss the reduction of a Hamilton-Jacobi theory for systems subject to nonholonomic constraints and that are invariant under the ac…

MARCD uses generative scenarios to improve portfolio decisions during regime shifts.

New algorithms optimize actions under time-varying constraints without projecting.

ACOL learns constraints from human preferences in driving simulations.

In this paper, we investigate the common scenario where every candidate item for recommendation is characterized by a maximum capacity, i.e., number of seats in a Point-of-Interest (POI) or size of an item's inventory. Despite the prevalence of the task of recommending items under capacity constraints in a variety of s…

Paper proposes a modified fairness constraint to address shortcomings of counterfactual fairness.

Develops an algorithm for bilevel optimization with coupled constraints.

New method optimizes processes under constraints using bivariate Gaussian models.

Study Federated RL with diverse constraints, proposing new optimization methods.

Efficient learning of minimax risk classifiers in high dimensions.

In this paper we propose a problem-driven scenario generation approach to the single-period portfolio selection problem which use tail risk measures such as conditional value-at-risk. Tail risk measures are useful for quantifying potential losses in worst cases. However, for scenario-based problems these are problemati…

The cardinality constraint is an intrinsic way to restrict the solution structure in many domains, for example, sparse learning, feature selection, and compressed sensing. To solve a cardinality constrained problem, the key challenge is to solve the projection onto the cardinality constraint set, which is NP-hard in ge…

Bayesian Entropy Neural Networks enforce constraints on deep learning predictions.

Unified framework for tractable inference scenarios in machine learning models.

In this paper, the extension of the framework of Learning from Constraints (LfC) to a distributed setting where multiple parties, connected over the network, contribute to the learning process is studied. LfC relies on the generic notion of "constraint" to inject knowledge into the learning problem and, due to its gene…

Study best arm identification with safety constraints in bandit problems.

A novel approach for safe offline RL using latent safety constraints.

Bayesian method estimates dynamics from near-optimal trajectories.

We are interested in estimating individual labels given only coarse, aggregated signal over the data points. In our setting, we receive sets ("bags") of unlabeled instances with constraints on label proportions. We relax the unrealistic assumption of known label proportions, made in previous work; instead, we assume on…

Model explains money creation under regulatory constraints.

Investors face constraints in Heston's model; optimal allocation differs from naive capped strategy.

In this paper, we study how to solve resource allocation problems in ultra-reliable and low-latency communications by unsupervised deep learning, which often yield functional optimization problems with quality-of-service (QoS) constraints. We take a joint power and bandwidth allocation problem as an example, which mini…

Most existing distance metric learning methods assume perfect side information that is usually given in pairwise or triplet constraints. Instead, in many real-world applications, the constraints are derived from side information, such as users' implicit feedbacks and citations among articles. As a result, these constra…

A novel one-class classifier fusion method for robust anomaly detection.

A novel nonstationary permanental process relaxes kernel constraints and captures complex data patterns.

We consider recommendation systems that need to operate under wireless bandwidth constraints, measured as number of broadcast transmissions, and demonstrate a (tight for some instances) tradeoff between regret and bandwidth for two scenarios: the case of multi-armed bandit with context, and the case where there is a la…

New approach tackles resource constraints in bandit problems with weakly adaptive algorithms.

We study a model of a corporation which has the possibility to choose various production/business policies with different expected profits and risks. In the model there are restrictions on the dividend distribution rates as well as restrictions on the risk the company can undertake. The objective is to maximize the exp…

Multi-label classification has attracted an increasing amount of attention in recent years. To this end, many algorithms have been developed to classify multi-label data in an effective manner. However, they usually do not consider the pairwise relations indicated by sample labels, which actually play important roles i…

Quantum channels' contraction under privacy constraints studied.

CoRe method improves time series forecasting coherency without strict constraints.

Study compares adaptive vs fixed query learning methods.