arXiv research

GACBO optimizes unknown causal graphs with interventions.

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

This paper tackles unknown causal graphs and soft interventions, establishing regret bounds and an efficient algorithm.

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

The paper tackles causal bandits with unknown SCMs and soft interventions, providing upper and lower bounds on regret.

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

Developed a new thresholding method that connects soft and hard thresholding.

Paper recovers latent causal structure and linear transformation from indirect observations.

In this article supervised learning problems are solved using soft rule ensembles. We first review the importance sampling learning ensembles (ISLE) approach that is useful for generating hard rules. The soft rules are then obtained with logistic regression from the corresponding hard rules. In order to deal with the p…

This paper tackles causal representation learning with linear and general transformations.

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

Bayesian method for inferring causal relations from mixed observational and interventional data.

Floer homology is a good example of homological invariants living in the infinite dimension. We suggest a way to construct this kind of invariants using only soft essentially finite-dimensional tools; no hard analysis or PDE is involved. This work is partially inspired by the M. Gromov's survey ``Soft and hard symplect…

Binary classification is a common statistical learning problem in which a model is estimated on a set of covariates for some outcome indicating the membership of one of two classes. In the literature, there exists a distinction between hard and soft classification. In soft classification, the conditional class probabil…

Motivated by applications in computational advertising and systems biology, we consider the problem of identifying the best out of several possible soft interventions at a source node $V$ in an acyclic causal directed graph, to maximize the expected value of a target node $Y$ (located downstream of $V$). Our setting im…

Paper relaxes faithfulness assumption for causal discovery using interventions.

OTSS learns personalized decision weights from logged decisions and outputs.

Optimal intervention in economic networks modeled as influence maximization, with hard computational problems.

New PL method improves ASR accuracy without pseudo-labels.

Study fairness in intervention to maximize outcomes.

New algorithms optimize a soft-robust criterion in reinforcement learning, reducing conservatism.

Study counterfactuals in cyclic systems with shifts and scales.

Coresets are efficient representations of data sets such that models trained on the coreset are provably competitive with models trained on the original data set. As such, they have been successfully used to scale up clustering models such as K-Means and Gaussian mixture models to massive data sets. However, until now,…

The goal of semi-supervised learning is to improve supervised classifiers by using additional unlabeled training examples. In this work we study a simple self-learning approach to semi-supervised learning applied to the least squares classifier. We show that a soft-label and a hard-label variant of self-learning can be…

New framework improves interpretability of trainable prompts.

In Domain Adaptation (DA), the category-relevant losses usually occupy a dominant position, while they are usually built with hard or soft labels in existing models. We observed that hard labels are overconfident due to hard samples existed, and soft labels are ambiguous as too many small noisy probabilities involved, …

Iterative thresholding algorithms seek to optimize a differentiable objective function over a sparsity or rank constraint by alternating between gradient steps that reduce the objective, and thresholding steps that enforce the constraint. This work examines the choice of the thresholding operator, and asks whether it i…

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

The paper tackles causal bandits for SEMs, proposing algorithms that avoid estimating $2^N$ reward distributions.

Proposes a new sampling policy for ranking and selection problems.

Proposes a new method for deep ensembles that improves accuracy and calibration.

Annealed Entropic Allocation improves ranking and selection by mitigating hard switching and improving finite-budget discrimination.

Many graph clustering quality functions suffer from a resolution limit, the inability to find small clusters in large graphs. So called resolution-limit-free quality functions do not have this limit. This property was previously introduced for hard clustering, that is, graph partitioning. We investigate the resolution-…

Memory networks are neural networks with an explicit memory component that can be both read and written to by the network. The memory is often addressed in a soft way using a softmax function, making end-to-end training with backpropagation possible. However, this is not computationally scalable for applications which …

New method disentangles mixed interventional and observational data in SEMs.

Employers actively look for talents having not only specific hard skills but also various soft skills. To analyze the soft skill demands on the job market, it is important to be able to detect soft skill phrases from job advertisements automatically. However, a naive matching of soft skill phrases can lead to false pos…

Paper establishes identifiability and achievability for causal representation learning.

Nonlinearity is crucial to the performance of a deep (neural) network (DN). To date there has been little progress understanding the menagerie of available nonlinearities, but recently progress has been made on understanding the rôle played by piecewise affine and convex nonlinearities like the ReLU and absolute value …

Tensor models improve joint EEG and fMRI analysis.

Efficient algorithms learn causal graphs with minimal interventions.

Margin maximization in the hard-margin sense, proposed as feature elimination criterion by the MFE-LO method, is combined here with data radius utilization to further aim to lower generalization error, as several published bounds and bound-related formulations pertaining to lowering misclassification risk (or error) pe…

Bayesian neural networks update beliefs with soft evidence, improving accuracy and calibration.

In this paper, we formulate the problem of inferring a Finite Mixture Model from discrete data as an optimal transport problem with entropic regularization of parameter $λ\geq 0$. Our method unifies hard and soft clustering, the Expectation-Maximization (EM) algorithm being exactly recovered for $λ=1$. The family of cl…

Learning using privileged information is an attractive problem setting that helps many learning scenarios in the real world. A state-of-the-art method of Gaussian process classification (GPC) with privileged information is GPC+, which incorporates privileged information into a noise term of the likelihood. A drawback o…

We consider the minimum cost intervention design problem: Given the essential graph of a causal graph and a cost to intervene on a variable, identify the set of interventions with minimum total cost that can learn any causal graph with the given essential graph. We first show that this problem is NP-hard. We then prove…

Paper estimates optimal classification error with soft labels and calibration.

Paper compares hard and soft EM for BN learning from incomplete data.

Paper shows identifiability of causal models with unobserved variables.