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1. Catalytic prior distributions with application to generalized linear models

   https://doi.org/10.1073/pnas.1920913117  

   Huang, Dongming; Stein, Nathan; Rubin, Donald B.; Kou, S. C. (June 2020, Proceedings of the National Academy of Sciences)

   A catalytic prior distribution is designed to stabilize a high-dimensional “working model” by shrinking it toward a “simplified model.” The shrinkage is achieved by supplementing the observed data with a small amount of “synthetic data” generated from a predictive distribution under the simpler model. We apply this framework to generalized linear models, where we propose various strategies for the specification of a tuning parameter governing the degree of shrinkage and study resultant theoretical properties. In simulations, the resulting posterior estimation using such a catalytic prior outperforms maximum likelihood estimation from the working model and is generally comparable with or superior to existing competitive methods in terms of frequentist prediction accuracy of point estimation and coverage accuracy of interval estimation. The catalytic priors have simple interpretations and are easy to formulate. 

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2. Multi-Armed Bandits for Human-Machine Decision Making

   https://doi.org/10.1109/ICASSP.2018.8461843  

   Reverdy, Paul; Srivastava, Vaibhav (April 2018, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP))

   Building an integrated human-machine decision-making system requires developing effective interfaces between the human and the machine. We develop such an interface by studying the multi-armed bandit problem, a simple sequential decision-making paradigm that can model a variety of tasks. We construct Bayesian algorithms for the multi-armed ban- dit problem, prove conditions under which these algorithms achieve good performance, and empirically show that, with appropriate priors, these algorithms effectively model human choice behavior; the priors then form a principled interface from human to machine. We take a signal processing perspective on the prior estimation problem and develop methods to estimate the priors given human choice data. 

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3. MFBO-SSM: Multi-Fidelity Bayesian Optimization for Fast Inference in State-Space Models

   Imani, M; Ghoreishi, S.F.; Allaire, D.; Braga-Neto, U (July 2019, Proceedings of the ... AAAI Conference on Artificial Intelligence)

   Nonlinear state-space models are ubiquitous in modeling real-world dynamical systems. Sequential Monte Carlo (SMC) techniques, also known as particle methods, are a well-known class of parameter estimation methods for this general class of state-space models. Existing SMC-based techniques rely on excessive sampling of the parameter space, which makes their computation intractable for large systems or tall data sets. Bayesian optimization techniques have been used for fast inference in state-space models with intractable likelihoods. These techniques aim to find the maximum of the likelihood function by sequential sampling of the parameter space through a single SMC approximator. Various SMC approximators with different fidelities and computational costs are often available for sample- based likelihood approximation. In this paper, we propose a multi-fidelity Bayesian optimization algorithm for the inference of general nonlinear state-space models (MFBO-SSM), which enables simultaneous sequential selection of parameters and approximators. The accuracy and speed of the algorithm are demonstrated by numerical experiments using synthetic gene expression data from a gene regulatory network model and real data from the VIX stock price index. 

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4. Detecting and adapting to irregular distribution shifts in Bayesian online learning

   Li, Aodong; Boyd, Alex; Smyth, Padhraic; Mandt, Stephan (December 2021, Advances in neural information processing systems)

   We consider the problem of online learning in the presence of distribution shifts that occur at an unknown rate and of unknown intensity. We derive a new Bayesian online inference approach to simultaneously infer these distribution shifts and adapt the model to the detected changes by integrating ideas from change point detection, switching dynamical systems, and Bayesian online learning. Using a binary ‘change variable,’ we construct an informative prior such that--if a change is detected--the model partially erases the information of past model updates by tempering to facilitate adaptation to the new data distribution. Furthermore, the approach uses beam search to track multiple change-point hypotheses and selects the most probable one in hindsight. Our proposed method is model-agnostic, applicable in both supervised and unsupervised learning settings, suitable for an environment of concept drifts or covariate drifts, and yields improvements over state-of-the-art Bayesian online learning approaches. 

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5. Fairness-Aware Online Meta-learning

   Zhao, Chen; Chen, Feng; Thuraisingham, Bhavani (August 2021, In Proceedings of the 27th ACM SIGKDD conference on knowledge discovery and data mining (KDD 2021),)

   null (Ed.)

   Fairness in AI and Machine Learning is emerging to be a crucial research area to ensure social good. In contrast to offline working fashions, two research paradigms are devised for online learning: (1) Online Meta-Learning (OML learns good priors over model parameters (or learning to learn) in a sequential setting where tasks are revealed one after another. Although it provides a sub-linear regret bound, such techniques completely ignore the importance of learning with fairness which is a significant hallmark of human intelligence. (2) Online Fairness-Aware Learning that captures many classification problems for which fairness is a concern. But it aims to attain zero-shot generalization without any task-specific adaptation. This, therefore, limits the capability of a model to adapt to newly arrived data. To overcome such issues and bridge the gap, this paper is the first to propose a novel online meta-learning algorithm, namely FFML, which is under the setting of unfairness prevention. The key part of FFML is to learn good priors of an online fair classification model's primal and dual parameters that are associated with the model's accuracy and fairness, respectively. The problem is formulated in the form of a bi-level convex-concave optimization. The theoretic analysis provides sub-linear upper bounds for loss regret and violation of cumulative fairness constraints. The experiments demonstrate the versatility of FFML by applying it to classification on three real-world datasets and show substantial improvements over the best prior work on the tradeoff between fairness and classification accuracy. 

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