4,497 research outputs found
On the Generation of Realistic and Robust Counterfactual Explanations for Algorithmic Recourse
This recent widespread deployment of machine learning algorithms presents many new challenges. Machine learning algorithms are usually opaque and can be particularly difficult to interpret. When humans are involved, algorithmic and automated decisions can negatively impact people’s lives. Therefore, end users would like to be insured against potential harm. One popular way to achieve this is to provide end users access to algorithmic recourse, which gives end users negatively affected by algorithmic decisions the opportunity to reverse unfavorable decisions, e.g., from a loan denial to a loan acceptance. In this thesis, we design recourse algorithms to meet various end user needs. First, we propose methods for the generation of realistic recourses. We use generative models to suggest recourses likely to occur under the data distribution. To this end, we shift the recourse action from the input space to the generative model’s latent space, allowing to generate counterfactuals that lie in regions with data support. Second, we observe that small changes applied to the recourses prescribed to end users likely invalidate the suggested recourse after being nosily implemented in practice. Motivated by this observation, we design methods for the generation of robust recourses and for assessing the robustness of recourse algorithms to data deletion requests. Third, the lack of a commonly used code-base for counterfactual explanation and algorithmic recourse algorithms and the vast array of evaluation measures in literature make it difficult to compare the per formance of different algorithms. To solve this problem, we provide an open source benchmarking library that streamlines the evaluation process and can be used for benchmarking, rapidly developing new methods, and setting up new
experiments. In summary, our work contributes to a more reliable interaction of end users and machine learned models by covering fundamental aspects of the recourse process and suggests new solutions towards generating realistic and robust counterfactual explanations for algorithmic recourse
Lessons to be learned by comparing integrated fisheries stock assessment models (SAMs) with integrated population models (IPMs)
AEP was partially funded by the Cooperative Institute for Climate, Ocean, & Ecosystem Studies (CICOES) under NOAA Cooperative Agreement NA15OAR4320063, Contribution No. 2023-1331.Integrated fisheries stock assessment models (SAMs) and integrated population models (IPMs) are used in biological and ecological systems to estimate abundance and demographic rates. The approaches are fundamentally very similar, but historically have been considered as separate endeavors, resulting in a loss of shared vision, practice and progress. We review the two approaches to identify similarities and differences, with a view to identifying key lessons that would benefit more generally the overarching topic of population ecology. We present a case study for each of SAM (snapper from the west coast of New Zealand) and IPM (woodchat shrikes from Germany) to highlight differences and similarities. The key differences between SAMs and IPMs appear to be the objectives and parameter estimates required to meet these objectives, the size and spatial scale of the populations, and the differing availability of various types of data. In addition, up to now, typical SAMs have been applied in aquatic habitats, while most IPMs stem from terrestrial habitats. SAMs generally aim to assess the level of sustainable exploitation of fish populations, so absolute abundance or biomass must be estimated, although some estimate only relative trends. Relative abundance is often sufficient to understand population dynamics and inform conservation actions, which is the main objective of IPMs. IPMs are often applied to small populations of conservation concern, where demographic uncertainty can be important, which is more conveniently implemented using Bayesian approaches. IPMs are typically applied at small to moderate spatial scales (1 to 104 km2), with the possibility of collecting detailed longitudinal individual data, whereas SAMs are typically applied to large, economically valuable fish stocks at very large spatial scales (104 to 106 km2) with limited possibility of collecting detailed individual data. There is a sense in which a SAM is more data- (or information-) hungry than an IPM because of its goal to estimate absolute biomass or abundance, and data at the individual level to inform demographic rates are more difficult to obtain in the (often marine) systems where most SAMs are applied. SAMs therefore require more 'tuning' or assumptions than IPMs, where the 'data speak for themselves', and consequently techniques such as data weighting and model evaluation are more nuanced for SAMs than for IPMs. SAMs would benefit from being fit to more disaggregated data to quantify spatial and individual variation and allow richer inference on demographic processes. IPMs would benefit from more attempts to estimate absolute abundance, for example by using unconditional models for capture-recapture data.Publisher PDFPeer reviewe
Probabilistic inverse optimal control with local linearization for non-linear partially observable systems
Inverse optimal control methods can be used to characterize behavior in
sequential decision-making tasks. Most existing work, however, requires the
control signals to be known, or is limited to fully-observable or linear
systems. This paper introduces a probabilistic approach to inverse optimal
control for stochastic non-linear systems with missing control signals and
partial observability that unifies existing approaches. By using an explicit
model of the noise characteristics of the sensory and control systems of the
agent in conjunction with local linearization techniques, we derive an
approximate likelihood for the model parameters, which can be computed within a
single forward pass. We evaluate our proposed method on stochastic and
partially observable version of classic control tasks, a navigation task, and a
manual reaching task. The proposed method has broad applicability, ranging from
imitation learning to sensorimotor neuroscience
Estimation of potential field environments from heterogeneous behaviour of sensing agents
This paper proposes a novel modelling framework for estimating the global potential field from trajectories of multiple sensing agents whose perception of the unknown field is subject to abrupt changes. We derive a parametrised formulation of the estimation problem by combining the jump Markov non-linear system (JMNLS) model of agent dynamics with a basis function decomposition of the environmental field. An approximate expectation-maximisation algorithm is employed for joint estimation of the global field and of the agent behavioural modes from observed agent trajectories. To avoid prohibitive computational costs associated with the state estimation of JMNLS, we utilise two approximation steps. First, an interacting multiple model smoother is used to account for the hybrid structure that emerges in this problem. Second, we propose two approaches to approximating the non-linear sufficient statistics during the expectation step. This results in the maximization step being exact. The performance of the developed framework is tested on simulation examples and demonstrated on an application study in which the observed movement patterns of immune cells are utilised in quantifying the underlying chemical concentration field that governs their migration. The results showcase that the proposed framework can be readily applied to problems where agents assume several behavioural modes
A Practitioner's Guide to Bayesian Inference in Pharmacometrics using Pumas
This paper provides a comprehensive tutorial for Bayesian practitioners in
pharmacometrics using Pumas workflows. We start by giving a brief motivation of
Bayesian inference for pharmacometrics highlighting limitations in existing
software that Pumas addresses. We then follow by a description of all the steps
of a standard Bayesian workflow for pharmacometrics using code snippets and
examples. This includes: model definition, prior selection, sampling from the
posterior, prior and posterior simulations and predictions, counter-factual
simulations and predictions, convergence diagnostics, visual predictive checks,
and finally model comparison with cross-validation. Finally, the background and
intuition behind many advanced concepts in Bayesian statistics are explained in
simple language. This includes many important ideas and precautions that users
need to keep in mind when performing Bayesian analysis. Many of the algorithms,
codes, and ideas presented in this paper are highly applicable to clinical
research and statistical learning at large but we chose to focus our
discussions on pharmacometrics in this paper to have a narrower scope in mind
and given the nature of Pumas as a software primarily for pharmacometricians
Improving the scalability of Gaussian-process error marginalization in gravitational-wave inference
The accuracy of Bayesian inference can be negatively affected by the use of
inaccurate forward models. In the case of gravitational-wave inference,
accurate but computationally expensive waveform models are sometimes
substituted with faster but approximate ones. The model error introduced by
this substitution can be mitigated in various ways, one of which is by
interpolating and marginalizing over the error using Gaussian process
regression. However, the use of Gaussian process regression is limited by the
curse of dimensionality, which makes it less effective for analyzing
higher-dimensional parameter spaces and longer signal durations. In this work,
to address this limitation, we focus on gravitational-wave signals from
extreme-mass-ratio inspirals as an example, and propose several significant
improvements to the base method: an improved prescription for constructing the
training set, GPU-accelerated training algorithms, and a new likelihood that
better adapts the base method to the presence of detector noise. Our results
suggest that the new method is more viable for the analysis of realistic
gravitational-wave data
A Flexible Framework for Incorporating Patient Preferences Into Q-Learning
In real-world healthcare problems, there are often multiple competing
outcomes of interest, such as treatment efficacy and side effect severity.
However, statistical methods for estimating dynamic treatment regimes (DTRs)
usually assume a single outcome of interest, and the few methods that deal with
composite outcomes suffer from important limitations. This includes
restrictions to a single time point and two outcomes, the inability to
incorporate self-reported patient preferences and limited theoretical
guarantees. To this end, we propose a new method to address these limitations,
which we dub Latent Utility Q-Learning (LUQ-Learning). LUQ-Learning uses a
latent model approach to naturally extend Q-learning to the composite outcome
setting and adopt the ideal trade-off between outcomes to each patient. Unlike
previous approaches, our framework allows for an arbitrary number of time
points and outcomes, incorporates stated preferences and achieves strong
asymptotic performance with realistic assumptions on the data. We conduct
simulation experiments based on an ongoing trial for low back pain as well as a
well-known completed trial for schizophrenia. In all experiments, our method
achieves highly competitive empirical performance compared to several
alternative baselines.Comment: Under Revie
AI: Limits and Prospects of Artificial Intelligence
The emergence of artificial intelligence has triggered enthusiasm and promise of boundless opportunities as much as uncertainty about its limits. The contributions to this volume explore the limits of AI, describe the necessary conditions for its functionality, reveal its attendant technical and social problems, and present some existing and potential solutions. At the same time, the contributors highlight the societal and attending economic hopes and fears, utopias and dystopias that are associated with the current and future development of artificial intelligence
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