The paper tackles fairness in forecasting and learning linear dynamical systems.
problem Under-representation bias in training data for multiple subgroups.
method Introducing subgroup-fair and instant-fair learning of LDS from multiple trajectories of varying lengths, using hierarchies of convexifications of non-commutative polynomial optimisation problems.
result Empirical results show both the beneficial impact of fairness considerations on statistical performance and encouraging effects of exploiting sparsity on run time.
A new model tackles language generation issues by using discrete variational attention.
problem Information under-representation and posterior collapse in variational autoencoders.
method Proposes a discrete variational attention model with categorical distribution over attention mechanism.
result Enhances latent space for language generation and avoids posterior collapse.
FAE framework tackles fairness in machine learning by balancing data and adjusting decision boundaries.
problem Discrimination in automated decision-making based on machine learning algorithms.
method Combines pre- and post-processing fairness interventions to address group imbalance, class imbalance, and class overlap.
result Improves fairness in machine learning models by balancing data and adjusting decision boundaries.
This paper aims to determine the images of the braid group under representations afforded by the Yang Baxter equation when the solution is a nontrivial 4×4 matrix. Making the assumption that all the eigenvalues of the Yang Baxter solution are roots of unity, leads to the conclusion that all the images are fini…
The paper proves rigidity theorems for forms on reductive symmetric spaces.
problem Local rigidity of forms on reductive symmetric spaces under representations of discrete groups.
method General local rigidity theorem for pull-backs of homogeneous forms, reinterpretation of old results.
result Volume of closed manifolds is constant under deformation of G/H-structure. Improves fairness in machine learning by adding underrepresented group data.
problem Machine learning biases across subgroups due to under-representation or societal biases.
method Data augmentation via pairwise mixup across subgroups to balance subpopulations.
result Achieves fair outcomes with robust if not improved accuracy.
New methods for estimating treatment effects with missing data.
problem Missing outcome data complicates estimating treatment effects.
method Proposed two de-biased machine learning estimators (mDR-learner and mEP-learner) to address under-representation.
result Oracle efficiency of the proposed estimators under reasonable conditions.
Paper studies complex Lagrangian surfaces and their relation to SL(3,C)-representations.
problem Minimal Lagrangian surfaces in bi-complex hyperbolic space and their representations.
method Introduces bi-complex Higgs bundles and parameterizes SL(3,C)-quasi-Fuchsian representations. result Parameterization of SL(3,C)-quasi-Fuchsian representations by an open set in Teichmüller space. Recent advances in computer vision and deep learning have led to breakthroughs in the development of automated skin image analysis. In particular, skin cancer classification models have achieved performance higher than trained expert dermatologists. However, no attempt has been made to evaluate the consistency in perfo…
New method finds balanced clusters in graphs using auxiliary information.
problem Finding balanced clusters in graphs with population-level constraints.
method Proposes individual-level balancing constraint and develops spectral clustering algorithms.
result Establishes first statistical consistency result for constrained spectral clustering.
The study proposes a framework to assess sustainability of firms using fund-level classifications and portfolio holdings.
problem To capture market-based sustainability assessments of firms.
method Exploiting fund-level sustainability classifications and granular portfolio holdings to construct Market-Implied Sustainability (MIS) scores.
result MIS scores capture sustainability dimensions different from conventional ESG ratings and improve portfolio performance.