arXiv research

Paper establishes identifiability and achievability for causal representation learning.

Dirac-harmonic maps are uncoupled under certain conditions.

Study uncoupled solutions to Dirac-Yang-Mills equations on spin manifolds.

Uncoupled regression is the problem to learn a model from unlabeled data and the set of target values while the correspondence between them is unknown. Such a situation arises in predicting anonymized targets that involve sensitive information, e.g., one's annual income. Since existing methods for uncoupled regression …

New algorithms converge faster to Nash equilibrium in zero-sum games with bandit feedback.

Efficient algorithm converges to Nash equilibrium in bilinear problems with bandit feedback.

New method learns functions without paired data using mediating variables.

Isotonic regression is a standard problem in shape-constrained estimation where the goal is to estimate an unknown nondecreasing regression function $f$ from independent pairs $(x_i, y_i)$ where $\mathbb{E}[y_i]=f(x_i), i=1, \ldots n$. While this problem is well understood both statistically and computationally, much l…

We study Dirac-harmonic maps from surfaces to manifolds with torsion, which is motivated from the superstring action considered in theoretical physics. We discuss analytic and geometric properties of such maps and outline an existence result for uncoupled solutions.

Multi-domain translation seeks to learn a probabilistic coupling between marginal distributions that reflects the correspondence between different domains. We assume that data from different domains are generated from a shared latent representation based on a structural equation model. Under this assumption, we show th…

We prove existence results for Dirac-harmonic maps using index theoretical tools. They are mainly interesting if the source manifold has dimension 1 or 2 modulo 8. Our solutions are uncoupled in the sense that the underlying map between the source and target manifolds is a harmonic map.

Node2Grids uncouples GCN training for large graphs, saving memory and computation.

A new model for simulating cloth manipulation in robots, accurate to within 1cm.

We classify quasilinear systems in Riemann invariants whose characteristic webs are linearizable on every solution. Although the linearizability of an individual web is a rather nontrivial differential constraint, the requirement of linearizability of characteristic webs on all solutions imposes simple second-order con…

Paper proves existence of Dirac-harmonic maps with trivial index.

In the present report, by using the Stokes-Helmholtz decomposition theorem the 3-dimensional Navier-Stokes equation (NSE) is uncoupled and transformed into a scalar equation for the velocity potential when the flow field is toroidal. The dynamics of the velocity potential is independent of the vector potential. The red…

The continuous-time random walk (CTRW) is a pure-jump stochastic process with several applications in physics, but also in insurance, finance and economics. A definition is given for a class of stochastic integrals driven by a CTRW, that includes the Ito and Stratonovich cases. An uncoupled CTRW with zero-mean jumps is…

Motivated by the sigma model limit of multicomponent Ginzburg-Landau theory, a version of the Faddeev-Skyrme model is considered in which the scalar field is coupled dynamically to a one-form field called the supercurrent. This coupled model is investigated in the general setting where physical space is an oriented Rie…

Estimates joint causal effects using single-variable interventions on nonlinear models.

This paper tackles CRL for multi-node interventions, achieving identifiability guarantees.

This work tackles causal graph discovery with stochastic interventions to minimize the number of interventions.

Causal diagrams based on do intervention are useful tools to formalize, process and understand causal relationship among variables. However, the do intervention has controversial interpretation of causal questions for non-manipulable variables, and it also lacks the power to check the conditions related to counterfactu…

Our goal is to identify beneficial interventions from observational data. We consider interventions that are narrowly focused (impacting few covariates) and may be tailored to each individual or globally enacted over a population. For applications where harmful intervention is drastically worse than proposing no change…

Paper proposes scalable algorithm to estimate intervention targets in linear models.

New method disentangles mixed interventional and observational data in SEMs.

The paper presents a method to estimate joint interventional distributions from marginal interventional data.

Bayesian method for causal discovery from unknown general interventions.

Interventional data helps identify latent factors without distributional assumptions.

Optimistic Hedge achieves optimal regret bounds in two-player zero-sum games.

IntDC framework uncovers causal relationships from non-interventional data.

Algorithm detects causal change points quickly with adaptive interventions.

In this article, we show how the scaling symmetry of the SABR model can be utilized to efficiently price European options. For special kinds of payoffs, the complexity of the problem is reduced by one dimension. For more generic payoffs, instead of solving the 1+2 dimensional SABR PDE, it is sufficient to solve $N_V$ u…

Method learns causal effects from multiple interventions in presence of unobserved confounders.

New algorithms improve causal graph discovery with adaptive interventions, even under worst-case interventional costs.

We address the problem of optimal Central Bank intervention in the exchange rate market when interventions create feedback in the rate dynamics. In particular, we extend the work done on optimal impulse control by Cadenillas and Zapatero to incorporate temporary market reactions, of random duration and level, to Bank i…

GIT uses gradient estimators to target interventions for causal discovery.

Paper relaxes faithfulness assumption for causal discovery using interventions.

FMI uses matching to mimic interventions for causal feature learning.

BaCaDI discovers causal structures from unknown interventions.

Graph-coupled causal Bayesian optimization transfers information across related interventions.

The paper tackles matching a desired mean in causal systems through shift interventions.

First sample-efficient algorithm for learning EFCE in bandit feedback settings.

Intervention loss stabilizes GAN training and reduces mode collapse.

Model identifies causal structure from paired observational and interventional data with unknown soft interventions.

Introduces CStrees for modeling context-specific causal models from observational and interventional data.

New algorithm reduces interventional strategy complexity for causal graph discovery.

New approach to counterfactual reasoning avoids demographic interventions.

Paper characterizes causal graphs from hard interventions and proposes a learning algorithm.