A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Estimates disease prevalence using non-ignorable missing data in health surveys.
The paper clarifies the distinction between CATE and ITE under ignorability assumptions.
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 …
Estimates CATE under hidden confounding, accounting for bias and ignorance.
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…
Noise-ignorant empirical risk minimization achieves state-of-the-art performance on noisy data.
Bayesian neural networks ignore data in infinite units limit.
Study estimates heterogeneous principal causal effects with binary treatments and intermediate variables.
%auto-ignore This paper has been withdrawn by the author, due to a crucial error.
Thompson Sampling with bilateral uncertainty improves performance in Bayesian Optimization.
Typically, operational risk losses are reported above some threshold. This paper studies the impact of ignoring data truncation on the 0.999 quantile of the annual loss distribution for operational risk for a broad range of distribution parameters and truncation levels. Loss frequency and severity are modelled by the P…
It is a usual practice to ignore any structural information underlying classes in multi-class classification. In this paper, we propose a graph convolutional network (GCN) augmented neural network classifier to exploit a known, underlying graph structure of labels. The proposed approach resembles an (approximate) infer…
The study aims to prevent unfair content presentation in recommender systems.
Bayes predictor remains robust to ignorable missingness shifts.
Typical neural networks with external memory do not effectively separate capacity for episodic and working memory as is required for reasoning in humans. Applying knowledge gained from psychological studies, we designed a new model called Differentiable Working Memory (DWM) in order to specifically emulate human workin…
New framework compares credal sets for hypothesis testing with epistemic uncertainty.
Two methods use BART to model missing data in leaf photosynthetic trait data.
Privacy preserving mechanisms such as differential privacy inject additional randomness in the form of noise in the data, beyond the sampling mechanism. Ignoring this additional noise can lead to inaccurate and invalid inferences. In this paper, we incorporate the privacy mechanism explicitly into the likelihood functi…
Proposes a new model for handling missing data.
(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…
We speed up marginal inference by ignoring factors that do not significantly contribute to overall accuracy. In order to pick a suitable subset of factors to ignore, we propose three schemes: minimizing the number of model factors under a bound on the KL divergence between pruned and full models; minimizing the KL dive…
We note a simple mechanism that may at least partially resolve several outstanding economic puzzles, including why the cyclically adjusted price to earnings ratio of the S&P 500 index has been oddly high for the past two decades, why gains to capital have outpaced gains to wages, and the persistence of the equity premi…
For text analysis, one often resorts to a lossy representation that either completely ignores word order or embeds each word as a low-dimensional dense feature vector. In this paper, we propose convolutional Poisson factor analysis (CPFA) that directly operates on a lossless representation that processes the words in e…
A new model synthesizes population with fewer structural and sampling zeros.
Study functional confounders in causal inference, enabling estimable effects.
In this study, the authors develop a structural model that combines a macro diffusion model with a micro choice model to control for the effect of social influence on the mobile app choices of customers over app stores. Social influence refers to the density of adopters within the proximity of other customers. Using a …
Open geometry puzzles keep the author engaged.
A standard recipe for spoken language recognition is to apply a Gaussian back-end to i-vectors. This ignores the uncertainty in the i-vector extraction, which could be important especially for short utterances. A recent paper by Cumani, Plchot and Fer proposes a solution to propagate that uncertainty into the backend. …
Research shows continual learning challenges in confounded datasets.
The paper shows vector-valued risk measures ignore dependence structures.
Study online linear regression with paid noise reduction.
Fueled by massive amounts of data, models produced by machine-learning (ML) algorithms, especially deep neural networks, are being used in diverse domains where trustworthiness is a concern, including automotive systems, finance, health care, natural language processing, and malware detection. Of particular concern is …
We give a covering number bound for deep learning networks that is independent of the size of the network. The key for the simple analysis is that for linear classifiers, rotating the data doesn't affect the covering number. Thus, we can ignore the rotation part of each layer's linear transformation, and get the coveri…
New algorithm for differentially private distributed optimization of smooth, non-convex problems.
We study an asynchronous online learning setting with a network of agents. At each time step, some of the agents are activated, requested to make a prediction, and pay the corresponding loss. The loss function is then revealed to these agents and also to their neighbors in the network. Our results characterize how much…
Co-TSFA improves time series forecasting by distinguishing between short-lived and persistent anomalies.
New DL model handles missing data in biomedical datasets.
The paper examines challenges in achieving fair predictions using causal counterfactuals.
The paper proposes methods to estimate positive examples and learn classifiers from mixed data.
Causal inference often relies on the counterfactual framework, which requires that treatment assignment is independent of the outcome, known as strong ignorability. Approaches to enforcing strong ignorability in causal analyses of observational data include weighting and matching methods. Effect estimates, such as the …
Bayesian model averaging (BMA) is the state of the art approach for overcoming model uncertainty. Yet, especially on small data sets, the results yielded by BMA might be sensitive to the prior over the models. Credal Model Averaging (CMA) addresses this problem by substituting the single prior over the models by a set …
Temporal networks are ubiquitous and evolve over time by the addition, deletion, and changing of links, nodes, and attributes. Although many relational datasets contain temporal information, the majority of existing techniques in relational learning focus on static snapshots and ignore the temporal dynamics. We propose…
In this paper we construct a new class of surfaces whose geodesic flow is integrable (in the sense of Liouville). We do so by generalizing the notion of tubes about curves to 3-dimensional manifolds, and using Jacobi fields we derive conditions under which the metric of the generalized tubular sub-manifold admits an ig…
A method to improve image synthesis diversity using mutual information.
This paper studies an entropy-based multi-objective Bayesian optimization (MBO). The entropy search is successful approach to Bayesian optimization. However, for MBO, existing entropy-based methods ignore trade-off among objectives or introduce unreliable approximations. We propose a novel entropy-based MBO called Pare…
Theoretical study on how model architecture affects contrastive learning performance.
New MRI method maps tissue parameters more accurately by ignoring voxel independence.
Proposes a copula-based model for multi-view clustering with directional dependency.