arXiv research

The paper clarifies the distinction between CATE and ITE under ignorability assumptions.

Estimates disease prevalence using non-ignorable missing data in health surveys.

Thompson Sampling with bilateral uncertainty improves performance in Bayesian Optimization.

Estimates CATE under hidden confounding, accounting for bias and ignorance.

(This comment has been updated to respond to Wang and Blei's rejoinder [arXiv:1910.07320].) The premise of the deconfounder method proposed in "Blessings of Multiple Causes" by Wang and Blei [arXiv:1805.06826], namely that a variable that renders multiple causes conditionally independent also controls for unmeasured mu…

Study estimates heterogeneous principal causal effects with binary treatments and intermediate variables.

The study revisits portfolio diversification by relaxing assumptions for skewed, multi-regime, and leptokurtic asset returns.

Existing generalization theories analyze the generalization performance mainly based on the model complexity and training process. The ignorance of the task properties, which results from the widely used IID assumption, makes these theories fail to interpret many generalization phenomena or guide practical learning tas…

New MRI method maps tissue parameters more accurately by ignoring voxel independence.

Proposes a new model for handling missing data.

This paper introduces PM and PMLP to enhance SSL by considering probability density and cluster assumptions.

Many applied decision-making problems have a dynamic component: The policymaker needs not only to choose whom to treat, but also when to start which treatment. For example, a medical doctor may choose between postponing treatment (watchful waiting) and prescribing one of several available treatments during the many vis…

Develops scalable methods to assess sensitivity and uncertainty in continuous treatment effects.

Many machine learning algorithms are based on the assumption that training examples are drawn independently. However, this assumption does not hold anymore when learning from a networked sample because two or more training examples may share some common objects, and hence share the features of these shared objects. We …

Two methods use BART to model missing data in leaf photosynthetic trait data.

Study functional confounders in causal inference, enabling estimable effects.

Given the potential difficulties in obtaining large quantities of labelled data, many works have explored the use of deep semi-supervised learning, which uses both labelled and unlabelled data to train a neural network architecture. The vast majority of SSL approaches focus on implementing the low-density separation as…

Operating with ignorance is an important concern of the Machine Learning research, especially when the objective is to discover knowledge from the imperfect data. Data mining (driven by appropriate knowledge discovery tools) is about processing available (observed, known and understood) samples of data aiming to build …

Causal inference from observational data often assumes "ignorability," that all confounders are observed. This assumption is standard yet untestable. However, many scientific studies involve multiple causes, different variables whose effects are simultaneously of interest. We propose the deconfounder, an algorithm that…

By elaborating on the notion of linear belief functions (Dempster 1990; Liu 1996), we propose an elementary approach to knowledge representation for expert systems using linear belief functions. We show how to use basic matrices to represent market information and financial knowledge, including complete ignorance, stat…

Proposes a new model to identify unknown counterfactual outcomes for continuous variables.

New method for valid inference from ML-predicted data.

Classic Topic Models are built under the Bag Of Words assumption, in which word position is ignored for simplicity. Besides, symmetric priors are typically used in most applications. In order to easily learn topics with different properties among the same corpus, we propose a new line of work in which the paragraph str…

Study causal inference under specific sampling methods with monotonicity assumptions.

As a consequence of the strong and usually violated conditional independence assumption (CIA) of naive Bayes (NB) classifier, the performance of NB becomes less and less favorable compared to sophisticated classifiers when the sample size increases. We learn from this phenomenon that when the size of the training data …

The paper examines challenges in achieving fair predictions using causal counterfactuals.

The paper tackles performative risk optimization under weak convexity assumptions.

This paper evaluates knowledge graph completion models under the open-world assumption, revealing unexpected behavior of metrics.

Ranked data appear in many different applications, including voting and consumer surveys. There often exhibits a situation in which data are partially ranked. Partially ranked data is thought of as missing data. This paper addresses parameter estimation for partially ranked data under a (possibly) non-ignorable missing…

Metabolic flux balance analyses are a standard tool in analysing metabolic reaction rates compatible with measurements, steady-state and the metabolic reaction network stoichiometry. Flux analysis methods commonly place unrealistic assumptions on fluxes due to the convenience of formulating the problem as a linear prog…

Stochastic gradient descent algorithms for training linear and kernel predictors are gaining more and more importance, thanks to their scalability. While various methods have been proposed to speed up their convergence, the model selection phase is often ignored. In fact, in theoretical works most of the time assumptio…

SMRL uses score matching for efficient RL with exponential family models.

Latent feature models are attractive for image modeling, since images generally contain multiple objects. However, many latent feature models ignore that objects can appear at different locations or require pre-segmentation of images. While the transformed Indian buffet process (tIBP) provides a method for modeling tra…

Proposes new method to handle hidden confounders in causal mediation analysis.

New framework for network regression models accounting for community structure.

Noise-ignorant empirical risk minimization achieves state-of-the-art performance on noisy data.

Proposes a new method for reliable treatment effect interval estimates.

Proposes a differentiable structure learning framework for general binary data.

This paper improves uncertainty quantification in ELM models.

Inverse reinforcement learning (IRL) enables an agent to learn complex behavior by observing demonstrations from a (near-)optimal policy. The typical assumption is that the learner's goal is to match the teacher's demonstrated behavior. In this paper, we consider the setting where the learner has its own preferences th…

Paper establishes fast convergence theory for diffusion models under minimal assumptions.

CgNN uses network structure as IVs to estimate causal effects in networks.

Bayesian neural networks ignore data in infinite units limit.

Develops a calibration prediction interval for non-parametric regression and neural networks.

New monitoring method detects ML risk models' performance changes in medical interventions.

This paper improves matrix completion by leveraging element importance and non-uniform sampling.

Alpha signals for statistical arbitrage strategies are often driven by latent factors. This paper analyses how to optimally trade with latent factors that cause prices to jump and diffuse. Moreover, we account for the effect of the trader's actions on quoted prices and the prices they receive from trading. Under fairly…

We analyze the performance of RiskMetrics, a widely used methodology for measuring market risk. Based on the assumption of normally distributed returns, the RiskMetrics model completely ignores the presence of fat tails in the distribution function, which is an important feature of financial data. Nevertheless, it was …