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David Danks

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10 papers

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YNIMG Journal 2026 Journal Article

Combining fast and slow fMRI sampling rates can enhance predictive power in resting-state data

Joanne Wardell
Kseniya Solovyeva
David Danks
Niko Huotari
Vesa J. Kiviniemi
Vesa O. Korhonen
Thomas DeRamus
Godfrey D. Pearlson

Data collection technology in functional magnetic resonance imaging (fMRI) is rapidly developing, leading to continuous growth of spatio-temporal resolution. The need to understand brain dynamics, as it plays a crucial role in understanding brain function, continues to push innovation in this direction as limits on the frequency of data measurement limit the kinds of questions that may be asked. In parallel, researchers continue to amass large volumes of fMRI data using the highest sampling frequencies available with current technology. A common and plausible assumption is that higher measurement frequencies may lead to more informative data about the brain dynamics and help mitigate physiological noise from neurovascularly coupled signal. This assumption leads to the tendency to discard the older datasets collected with lower temporal resolution in favor of more recent collections. Moreover, as we will show, it leads to under-utilizing the current MRI technology by only collecting at the fastest available rate. A recent theoretical study demonstrated that combining high frequency data with data collected at a deliberately slower sampling rate can, in some conditions, lead to gains in information about the dynamics. We hypothesize that similar effects can be observed in fMRI datasets where data is collected at multiple timescales, as opposed to datasets created by subsampling from a single acquisition rate. A resting state fMRI dataset collected from 10 subjects at a slow (2150 ms) and fast (100 ms) repetition time (TR) is analyzed, demonstrating informative gains in predictive power by combining the two. This gain is in contrast to diminishing returns in the single TR dataset performance, where the data has been manually-undersampled to a slower sampling rate and combined with the original. Performance outcomes were also compared in gender prediction across a multi-rate dataset and single rate dataset, with multi-rate results showing gains in composite features. Our experiments demonstrate agreement with the theoretical results in showing that features formed as a combination of slow and fast sampling rates yield greater predictive power than features from either slow or fast rates alone in some settings.

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ICLR Conference 2023 Conference Paper

GRACE-C: Generalized Rate Agnostic Causal Estimation via Constraints

Mohammadsajad Abavisani
David Danks
Sergey M. Plis

Graphical structures estimated by causal learning algorithms from time series data can provide highly misleading causal information if the causal timescale of the generating process fails to match the measurement timescale of the data. Existing algorithms provide limited resources to respond to this challenge, and so researchers must either use models that they know are likely misleading, or else forego causal learning entirely. Existing methods face up-to-four distinct shortfalls, as they might a) require that the difference between causal and measurement timescales is known; b) only handle very small number of random variables when the timescale difference is unknown; c) only apply to pairs of variables (albeit with fewer assumptions about prior knowledge); or d) be unable to find a solution given statistical noise in the data. This paper aims to address these challenges. We present an approach that combines constraint programming with both theoretical insights into the problem structure and prior information about admissible causal interactions to achieve speed up of multiple orders of magnitude. The resulting system scales to significantly larger sets of random variables ($>100$) without knowledge of the timescale difference while maintaining theoretical guarantees. This method is also robust to edge misidentification and can use parametric connection strengths, while optionally finding the optimal among many possible solutions.

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IJCAI Conference 2017 Conference Paper

Algorithmic Bias in Autonomous Systems

David Danks
Alex John London

Algorithms play a key role in the functioning of autonomous systems, and so concerns have periodically been raised about the possibility of algorithmic bias. However, debates in this area have been hampered by different meanings and uses of the term, "bias. " It is sometimes used as a purely descriptive term, sometimes as a pejorative term, and such variations can promote confusion and hamper discussions about when and how to respond to algorithmic bias. In this paper, we first provide a taxonomy of different types and sources of algorithmic bias, with a focus on their different impacts on the proper functioning of autonomous systems. We then use this taxonomy to distinguish between algorithmic biases that are neutral or unobjectionable, and those that are problematic in some way and require a response. In some cases, there are technological or algorithmic adjustments that developers can use to compensate for problematic bias. In other cases, however, responses require adjustments by the agent, whether human or autonomous system, who uses the results of the algorithm. There is no "one size fits all" solution to algorithmic bias.

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IS Journal 2017 Journal Article

Regulating Autonomous Systems: Beyond Standards

David Danks
Alex John London

Autonomous systems are on the verge of widespread use, but there are significant challenges to ensuring their safe and appropriate development, deployment, and application. The authors argue that autonomous systems cannot, in general, be regulated using traditional performance standards. They contend that these systems should instead be evaluated by a staged, iterative regulatory approach similar to that for pharmaceuticals and medical devices. This article outlines some features of such a system.

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UAI Conference 2015 Conference Paper

Mesochronal Structure Learning

Sergey M. Plis
David Danks
Jianyu Yang

Standard time series structure learning algorithms assume that the measurement timescale is approximately the same as the timescale of the underlying (causal) system. In many scientific contexts, however, this assumption is violated: the measurement timescale can be substantially slower than the system timescale (so intermediate time series datapoints will be missing). This assumption violation can lead to significant learning errors. In this paper, we provide a novel learning algorithm to extract systemtimescale structure from measurement data that undersample the underlying system. We employ multiple algorithmic optimizations that exploit the problem structure in order to achieve computational tractability. The resulting algorithm is highly reliable at extracting system-timescale structure from undersampled data.

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NeurIPS Conference 2015 Conference Paper

Rate-Agnostic (Causal) Structure Learning

Sergey Plis
David Danks
Cynthia Freeman
Vince Calhoun

Causal structure learning from time series data is a major scientific challenge. Existing algorithms assume that measurements occur sufficiently quickly; more precisely, they assume that the system and measurement timescales are approximately equal. In many scientific domains, however, measurements occur at a significantly slower rate than the underlying system changes. Moreover, the size of the mismatch between timescales is often unknown. This paper provides three distinct causal structure learning algorithms, all of which discover all dynamic graphs that could explain the observed measurement data as arising from undersampling at some rate. That is, these algorithms all learn causal structure without assuming any particular relation between the measurement and system timescales; they are thus rate-agnostic. We apply these algorithms to data from simulations. The results provide insight into the challenge of undersampling.

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NeurIPS Conference 2013 Conference Paper

Tracking Time-varying Graphical Structure

Erich Kummerfeld
David Danks

Structure learning algorithms for graphical models have focused almost exclusively on stable environments in which the underlying generative process does not change; that is, they assume that the generating model is globally stationary. In real-world environments, however, such changes often occur without warning or signal. Real-world data often come from generating models that are only locally stationary. In this paper, we present LoSST, a novel, heuristic structure learning algorithm that tracks changes in graphical model structure or parameters in a dynamic, real-time manner. We show by simulation that the algorithm performs comparably to batch-mode learning when the generating graphical structure is globally stationary, and significantly better when it is only locally stationary.

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NeurIPS Conference 2008 Conference Paper

Integrating Locally Learned Causal Structures with Overlapping Variables

David Danks
Clark Glymour
Robert Tillman

In many domains, data are distributed among datasets that share only some variables; other recorded variables may occur in only one dataset. There are several asymptotically correct, informative algorithms that search for causal information given a single dataset, even with missing values and hidden variables. There are, however, no such reliable procedures for distributed data with overlapping variables, and only a single heuristic procedure (Structural EM). This paper describes an asymptotically correct procedure, ION, that provides all the information about structure obtainable from the marginal independence relations. Using simulated and real data, the accuracy of ION is compared with that of Structural EM, and with inference on complete, unified data.

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NeurIPS Conference 2002 Conference Paper

Dynamical Causal Learning

David Danks
Thomas Griffiths
Joshua Tenenbaum

theories of human causal focus primarily on long-run predictions: learning and Current psychological two by judgment estimating parameters of a causal Bayes nets (though for different parameterizations), and a third through structural learning. This short-run behavior by examining paper dynamical versions of these three theories, and comparing their predictions to a real-world dataset. focuses on people's

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UAI Conference 2001 Conference Paper

Linearity Properties of Bayes Nets with Binary Variables

David Danks
Clark Glymour

It is "well known" that in linear models: (1) testable constraints on the marginal distribution of observed variables distinguish certain cases in which an unobserved cause jointly influences several observed variables; (2) the technique of "instrumental variables" sometimes permits an estimation of the influence of one variable on another even when the association between the variables may be confounded by unobserved common causes; (3) the association (or conditional probability distribution of one variable given another) of two variables connected by a path or trek can be computed directly from the parameter values associated with each edge in the path or trek; (4) the association of two variables produced by multiple treks can be computed from the parameters associated with each trek; and (5) the independence of two variables conditional on a third implies the corresponding independence of the sums of the variables over all units conditional on the sums over all units of each of the original conditioning variables.These properties are exploited in search procedures. It is also known that properties (2)-(5) do not hold for all Bayes nets with binary variables. We show that (1) holds for all Bayes nets with binary variables and (5) holds for all singly trek-connected Bayes nets of that kind. We further show that all five properties hold for Bayes nets with any DAG and binary variables parameterized with noisy-or and noisy-and gates.

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