Hierarchical models of goal-directed and automatic actions

Decision-making processes behind instrumental actions can be divided into two categories: goal-directed actions, and automatic actions. The structure of automatic actions, their interaction with goal-directed actions, and their behavioral and computational properties are the topics of the current thesis. We conceptualize the structure of automatic actions as sequences of actions that form a single response unit and are integrated within goal-directed processes in a hierarchical manner. We represent this hypothesis using the computational framework of reinforcement learning and develop a new normative computational model for the acquisition of action sequences, and their hierarchical interaction with goal-directed processes. We develop a neurally plausible hypothesis for the role of neuromodulator dopamine as a teaching signal for the acquisition of action sequences. We further explore the predictions of the proposed model in a two-stage decision-making task in humans and we show that the proposed model has higher explanatory power than its alternatives. Finally, we translate the two-stage decision-making task to an experimental protocol in rats and show that, similar to humans, rats also use action sequences and engage in hierarchical decision-making. The results provide a new theoretical and experimental paradigm for conceptualizing and measuring the operation and interaction of goal-directed and automatic actions

Hierarchical models of goal-directed and automatic actions

Decision-making processes behind instrumental actions can be divided into two categories: goal-directed actions, and automatic actions. The structure of automatic actions, their interaction with goal-directed actions, and their behavioral and computational properties are the topics of the current thesis. We conceptualize the structure of automatic actions as sequences of actions that form a single response unit and are integrated within goal-directed processes in a hierarchical manner. We represent this hypothesis using the computational framework of reinforcement learning and develop a new normative computational model for the acquisition of action sequences, and their hierarchical interaction with goal-directed processes. We develop a neurally plausible hypothesis for the role of neuromodulator dopamine as a teaching signal for the acquisition of action sequences. We further explore the predictions of the proposed model in a two-stage decision-making task in humans and we show that the proposed model has higher explanatory power than its alternatives. Finally, we translate the two-stage decision-making task to an experimental protocol in rats and show that, similar to humans, rats also use action sequences and engage in hierarchical decision-making. The results provide a new theoretical and experimental paradigm for conceptualizing and measuring the operation and interaction of goal-directed and automatic actions

The contextual lasso: Sparse linear models via deep neural networks

Sparse linear models are a gold standard tool for interpretable machine learning, a field of emerging importance as predictive models permeate decision-making in many domains. Unfortunately, sparse linear models are far less flexible as functions of their input features than black-box models like deep neural networks. With this capability gap in mind, we study a not-uncommon situation where the input features dichotomize into two groups: explanatory features, which are candidates for inclusion as variables in an interpretable model, and contextual features, which select from the candidate variables and determine their effects. This dichotomy leads us to the contextual lasso, a new statistical estimator that fits a sparse linear model to the explanatory features such that the sparsity pattern and coefficients vary as a function of the contextual features. The fitting process learns this function nonparametrically via a deep neural network. To attain sparse coefficients, we train the network with a novel lasso regularizer in the form of a projection layer that maps the network's output onto the space of $\ell_1$-constrained linear models. An extensive suite of experiments on real and synthetic data suggests that the learned models, which remain highly transparent, can be sparser than the regular lasso without sacrificing the predictive power of a standard deep neural network

Cross-Entropy Estimators for Sequential Experiment Design with Reinforcement Learning

Reinforcement learning can effectively learn amortised design policies for designing sequences of experiments. However, current methods rely on contrastive estimators of expected information gain, which require an exponential number of contrastive samples to achieve an unbiased estimation. We propose an alternative lower bound estimator, based on the cross-entropy of the joint model distribution and a flexible proposal distribution. This proposal distribution approximates the true posterior of the model parameters given the experimental history and the design policy. Our estimator requires no contrastive samples, can achieve more accurate estimates of high information gains, allows learning of superior design policies, and is compatible with implicit probabilistic models. We assess our algorithm's performance in various tasks, including continuous and discrete designs and explicit and implicit likelihoods

Gray-box inference for structured Gaussian process models

We develop an automated variational infer- ence method for Bayesian structured prediction problems with Gaussian process (gp) priors and linear-chain likelihoods. Our approach does not need to know the details of the structured likelihood model and can scale up to a large number of observations. Furthermore, we show that the required expected likelihood term and its gradients in the variational objective (ELBO) can be estimated efficiently by using expectations over very low-dimensional Gaussian distributions. Optimization of the ELBO is fully parallelizable over sequences and amenable to stochastic optimization, which we use along with control variate techniques to make our framework useful in practice. Results on a set of natural language processing tasks show that our method can be as good as (and sometimes better than, in particular with respect to expected log-likelihood) hard-coded approaches including svm-struct and crfs, and overcomes the scalability limitations of previous inference algorithms based on sampling. Overall, this is a fundamental step to developing automated inference methods for Bayesian structured prediction

Transformed Distribution Matching for Missing Value Imputation

We study the problem of imputing missing values in a dataset, which has important applications in many domains. The key to missing value imputation is to capture the data distribution with incomplete samples and impute the missing values accordingly. In this paper, by leveraging the fact that any two batches of data with missing values come from the same data distribution, we propose to impute the missing values of two batches of samples by transforming them into a latent space through deep invertible functions and matching them distributionally. To learn the transformations and impute the missing values simultaneously, a simple and well-motivated algorithm is proposed. Our algorithm has fewer hyperparameters to fine-tune and generates high-quality imputations regardless of how missing values are generated. Extensive experiments over a large number of datasets and competing benchmark algorithms show that our method achieves state-of-the-art performance.Comment: ICML 2023 camera-ready version, https://openreview.net/forum?id=WBWb1FU8i