Onno Zoeter

NeurIPS Conference 2025 Conference Paper

Conditional Forecasts and Proper Scoring Rules for Reliable and Accurate Performative Predictions

• Philip Boeken
• Onno Zoeter
• Joris Mooij

Performative predictions are forecasts which influence the outcomes they aim to predict, undermining the existence of correct forecasts and standard methods of elicitation and estimation. We show that conditioning forecasts on covariates that separate them from the outcome renders the target distribution forecast-invariant, guaranteeing well-posedness of the forecasting problem. However, even under this condition, classical proper scoring rules fail to elicit correct forecasts. We prove a general impossibility result and identify two solutions: (i) in decision-theoretic settings, elicitation of correct and incentive-compatible forecasts is possible if forecasts are separating; (ii) scoring with unbiased estimates of the divergence between the forecast and the induced distribution of the target variable yields correct forecasts. Applying these insights to parameter estimation, conditional forecasts and proper scoring rules enable performatively stable estimation of performatively correct parameters, resolving the issues raised by Perdomo et al. (2020). Our results expose fundamental limits of classical forecast evaluation and offer new tools for reliable and accurate forecasting in performative settings.

UAI Conference 2025 Conference Paper

Revisiting the Berkeley Admissions data: Statistical Tests for Causal Hypotheses

• Sourbh Bhadane
• Joris M. Mooij
• Philip A. Boeken
• Onno Zoeter

Reasoning about fairness through correlation-based notions is rife with pitfalls. The 1973 University of California, Berkeley graduate school admissions case from \citet{BickelHO75} is a classic example of one such pitfall, namely Simpson’s paradox. The discrepancy in admission rates among male and female applicants, in the aggregate data over all departments, vanishes when admission rates per department are examined. We reason about the Berkeley graduate school admissions case through a causal lens. In the process, we introduce a statistical test for causal hypothesis testing based on Pearl’s instrumental-variable inequalities \citep{Pearl95}. We compare different causal notions of fairness that are based on graphical, counterfactual and interventional queries on the causal model, and develop statistical tests for these notions that use only observational data. We study the logical relations between notions, and show that while notions may not be equivalent, their corresponding statistical tests coincide for the case at hand. We believe that a thorough case-based causal analysis helps develop a more principled understanding of both causal hypothesis testing and fairness.

EWRL Workshop 2024 Workshop Paper

Leveraging diverse offline data in POMDPs with unobserved confounders

• Oussama Azizi
• Philip Boeken
• Onno Zoeter
• Frans A Oliehoek
• Matthijs T. J. Spaan

In many Reinforcement Learning (RL) applications, offline data is readily available before an algorithm is deployed. Often, however, data-collection policies have had access to information that is not recorded in the dataset, requiring the RL agent to take unobserved confounders into account. We focus on the setting where the confounders are i. i. d. and, without additional assumptions on the strength of the confounding, we derive tight bounds for the causal effects of the actions on the observations and reward. In particular, we show that these bounds are tight when we leverage multiple datasets collected from diverse behavioral policies. We incorporate these bounds into Posterior Sampling for Reinforcement Learning (PSRL) and demonstrate their efficacy experimentally.

UAI Conference 2023 Conference Paper

Correcting for selection bias and missing response in regression using privileged information

• Philip A. Boeken
• Noud de Kroon
• Mathijs de Jong
• Joris M. Mooij
• Onno Zoeter

When estimating a regression model, we might have data where some labels are missing, or our data might be biased by a selection mechanism. When the response or selection mechanism is ignorable (i. e. , independent of the response variable given the features) one can use off-the-shelf regression methods; in the nonignorable case one typically has to adjust for bias. We observe that privileged information (i. e. information that is only available during training) might render a nonignorable selection mechanism ignorable, and we refer to this scenario as Privilegedly Missing at Random (PMAR). We propose a novel imputation-based regression method, named repeated regression, that is suitable for PMAR. We also consider an importance weighted regression method, and a doubly robust combination of the two. The proposed methods are easy to implement with most popular out-of-the-box regression algorithms. We empirically assess the performance of the proposed methods with extensive simulated experiments and on a synthetically augmented real-world dataset. We conclude that repeated regression can appropriately correct for bias, and can have considerable advantage over weighted regression, especially when extrapolating to regions of the feature space where response is never observed.

AIJ Journal 2018 Journal Article

A quality assuring, cost optimal multi-armed bandit mechanism for expertsourcing

• Shweta Jain
• Sujit Gujar
• Satyanath Bhat
• Onno Zoeter
• Y. Narahari

There are numerous situations when a service requester wishes to expertsource a series of identical but non-trivial tasks from a pool of experts so as to achieve an assured accuracy level for each task, in a cost optimal way. The experts available are typically heterogeneous with unknown but fixed qualities and different service costs. The service costs are usually private to the experts and the experts could be strategic about their costs. The problem is to select for each task an optimal subset of experts so that the outcome obtained after aggregating the opinions from the selected experts guarantees a target level of accuracy. The problem is a challenging one even in a non-strategic setting since the accuracy of an aggregated outcome depends on unknown qualities. We develop a novel multi-armed bandit (MAB) mechanism for solving this problem. First, we propose a framework, Assured Accuracy Bandit (AAB) framework, which leads to a MAB algorithm, Constrained Confidence Bound for Non-Strategic Setting (CCB-NS). We derive an upper bound on the number of time steps this algorithm chooses a sub-optimal set, which depends on the target accuracy and true qualities. A more challenging situation arises when the requester not only has to learn the qualities of the experts but has to elicit their true service costs as well. We modify the CCB-NS algorithm to obtain an adaptive exploration separated algorithm Constrained Confidence Bound for Strategic Setting (CCB-S). The CCB-S algorithm produces an ex-post monotone allocation rule that can then be transformed into an ex-post incentive compatible and ex-post individually rational mechanism. This mechanism learns the qualities of the experts and guarantees a given target accuracy level in a cost optimal way. We also provide a lower bound on the number of times any algorithm must select a sub-optimal set and we see that the lower bound matches our upper bound up to a constant factor. We provide insights on a practical implementation of this framework through an illustrative example and demonstrate the efficacy of our algorithms through simulations.

UAI Conference 2011 Conference Paper

Dynamic Mechanism Design for Markets with Strategic Resources

• Swaprava Nath
• Onno Zoeter
• Yadati Narahari
• Christopher R. Dance

The assignment of tasks to multiple resources becomes an interesting game theoretic problem, when both the task owner and the resources are strategic. In the classical, nonstrategic setting, where the states of the tasks and resources are observable by the controller, this problem is that of finding an optimal policy for a Markov decision process (MDP). When the states are held by strategic agents, the problem of an efficient task allocation extends beyond that of solving an MDP and becomes that of designing a mechanism. Motivated by this fact, we propose a general mechanism which decides on an allocation rule for the tasks and resources and a payment rule to incentivize agents' participation and truthful reports. In contrast to related dynamic strategic control problems studied in recent literature, the problem studied here has interdependent values: the benefit of an allocation to the task owner is not simply a function of the characteristics of the task itself and the allocation, but also of the state of the resources. We introduce a dynamic extension of Mezzetti's two phase mechanism for interdependent valuations. In this changed setting, the proposed dynamic mechanism is efficient, within period ex-post incentive compatible, and within period ex-post individually rational.

NeurIPS Conference 2011 Conference Paper

Sparse Bayesian Multi-Task Learning

• Shengbo Guo
• Onno Zoeter
• Cédric Archambeau

We propose a new sparse Bayesian model for multi-task regression and classification. The model is able to capture correlations between tasks, or more specifically a low-rank approximation of the covariance matrix, while being sparse in the features. We introduce a general family of group sparsity inducing priors based on matrix-variate Gaussian scale mixtures. We show the amount of sparsity can be learnt from the data by combining an approximate inference approach with type II maximum likelihood estimation of the hyperparameters. Empirical evaluations on data sets from biology and vision demonstrate the applicability of the model, where on both regression and classification tasks it achieves competitive predictive performance compared to previously proposed methods.

ICML Conference 2009 Conference Paper

Split variational inference

• Guillaume Bouchard
• Onno Zoeter

We propose a deterministic method to evaluate the integral of a positive function based on soft-binning functions that smoothly cut the integral into smaller integrals that are easier to approximate. In combination with mean-field approximations for each individual sub-part this leads to a tractable algorithm that alternates between the optimization of the bins and the approximation of the local integrals. We introduce suitable choices for the binning functions such that a standard mean field approximation can be extended to a split mean field approximation without the need for extra derivations. The method can be seen as a revival of the ideas underlying the mixture mean field approach. The latter can be obtained as a special case by taking soft-max functions for the binning.

JMLR Journal 2005 Journal Article

Change Point Problems in Linear Dynamical Systems

• Onno Zoeter
• Tom Heskes

We study the problem of learning two regimes (we have a normal and a prefault regime in mind) based on a train set of non-Markovian observation sequences. Key to the model is that we assume that once the system switches from the normal to the prefault regime it cannot restore and will eventually result in a fault. We refer to the particular setting as semi-supervised since we assume the only information given to the learner is whether a particular sequence ended with a stop (implying that the sequence was generated by the normal regime) or with a fault (implying that there was a switch from the normal to the fault regime). In the latter case the particular time point at which a switch occurred is not known. The underlying model used is a switching linear dynamical system (SLDS). The constraints in the regime transition probabilities result in an exact inference procedure that scales quadratically with the length of a sequence. Maximum aposteriori (MAP) parameter estimates can be found using an expectation maximization (EM) algorithm with this inference algorithm in the E-step. For long sequences this will not be practically feasible and an approximate inference and an approximate EM procedure is called for. We describe a flexible class of approximations corresponding to different choices of clusters in a Kikuchi free energy with weak consistency constraints. [abs] [ pdf ][ bib ] &copy JMLR 2005. ( edit, beta )

NeurIPS Conference 2003 Conference Paper

Approximate Expectation Maximization

• Tom Heskes
• Onno Zoeter
• Wim Wiegerinck

We discuss the integration of the expectation-maximization (EM) algorithm for maximum likelihood learning of Bayesian networks with belief propagation algorithms for approximate inference. Specifically we propose to combine the outer-loop step of convergent belief propagation algorithms with the M-step of the EM algorithm. This then yields an approximate EM algorithm that is essentially still double loop, with the important advantage of an inner loop that is guaranteed to converge. Simulations illustrate the merits of such an approach.

UAI Conference 2002 Conference Paper

Expectation Propogation for Approximate Inference in Dynamic Bayesian Networks

• Tom Heskes
• Onno Zoeter