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Julia Stoyanovich

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8 papers

2 author rows

NeurIPS Conference 2025 Conference Paper

Do You Really Need Public Data? Surrogate Public Data for Differential Privacy on Tabular Data

Shlomi Hod
Lucas Rosenblatt
Julia Stoyanovich

Differentially private (DP) machine learning often relies on the availability of public data for tasks like privacy-utility trade-off estimation, hyperparameter tuning, and pretraining. While public data assumptions may be reasonable in text and image data, they are less likely to hold for tabular data due to tabular data heterogeneity across domains. We propose leveraging powerful priors to address this limitation; specifically, we synthesize realistic tabular data directly from schema-level specifications -- such as variable names, types, and permissible ranges -- without ever accessing sensitive records. To that end, this work introduces the notion of ``surrogate'' public data -- datasets generated independently of sensitive data, which consume no privacy loss budget and are constructed solely from publicly available schema or metadata. Surrogate public data are intended to encode plausible statistical assumptions (informed by publicly available information) into a dataset with many downstream uses in private mechanisms. We automate the process of generating surrogate public data with large language models (LLMs); in particular, we propose two methods: direct record generation as CSV files, and automated structural causal model (SCM) construction for sampling records. Through extensive experiments, we demonstrate that surrogate public tabular data can effectively replace traditional public data when pretraining differentially private tabular classifiers. To a lesser extent, surrogate public data are also useful for hyperparameter tuning of DP synthetic data generators, and for estimating the privacy-utility tradeoff.

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ECAI Conference 2025 Conference Paper

From Benchmarking to Understanding FairML

Mykola Pechenizkiy
Hilde J. P. Weerts
Cassio de Campos
Yuya Sasaki 0001
Julia Stoyanovich

Benchmarks play a central role in machine learning (ML), offering standardized datasets and metrics that enable comparison and drive progress. In fairness-aware ML (fairML), however, benchmarks pose distinctive challenges. Fairness is not a purely technical property but a socio-technical concept, shaped by normative choices and institutional context. Benchmarks strip away this context: they reduce fairness to intrinsic metrics, obscure what is comparable, and collapse distinct notions of justice—from distributive allocation in credit scoring to basic rights in criminal justice—into a single optimization task. Moreover, when used as measures of progress, benchmarks risk enshrining oversimplified metrics as community standards, assuming an exception to Goodhart’s law. We argue that while benchmarking has value for building baselines and organizing competition, responsible evaluation of fairML requires complementary frameworks: ones that combine intrinsic with extrinsic, context-sensitive assessments, and that make explicit the normative assumptions underlying fairness interventions.

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IJCAI Conference 2024 Conference Paper

A New Paradigm for Counterfactual Reasoning in Fairness and Recourse

Lucius E. J. Bynum
Joshua R. Loftus
Julia Stoyanovich

Counterfactuals underpin numerous techniques for auditing and understanding artificial intelligence (AI) systems. The traditional paradigm for counterfactual reasoning in this literature is the interventional counterfactual, where hypothetical interventions are imagined and simulated. For this reason, the starting point for causal reasoning about legal protections and demographic data in AI is an imagined intervention on a legally-protected characteristic, such as ethnicity, race, gender, disability, age, etc. We ask, for example, what would have happened had your race been different? An inherent limitation of this paradigm is that some demographic interventions — like interventions on race — may not be well-defined or translate into the formalisms of interventional counterfactuals. In this work, we explore a new paradigm based instead on the backtracking counterfactual, where rather than imagine hypothetical interventions on legally-protected characteristics, we imagine alternate initial conditions while holding these characteristics fixed. We ask instead, what would explain a counterfactual outcome for you as you actually are or could be? This alternate framework allows us to address many of the same social concerns, but to do so while asking fundamentally different questions that do not rely on demographic interventions.

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AAAI Conference 2024 Conference Paper

A Simple and Practical Method for Reducing the Disparate Impact of Differential Privacy

Lucas Rosenblatt
Julia Stoyanovich
Christopher Musco

Differentially private (DP) mechanisms have been deployed in a variety of high-impact social settings (perhaps most notably by the U.S. Census). Since all DP mechanisms involve adding noise to results of statistical queries, they are expected to impact our ability to accurately analyze and learn from data, in effect trading off privacy with utility. Alarmingly, the impact of DP on utility can vary significantly among different sub-populations. A simple way to reduce this disparity is with stratification. First compute an independent private estimate for each group in the data set (which may be the intersection of several protected classes), then, to compute estimates of global statistics, appropriately recombine these group estimates. Our main observation is that naive stratification often yields high-accuracy estimates of population-level statistics, without the need for additional privacy budget. We support this observation theoretically and empirically. Our theoretical results center on the private mean estimation problem, while our empirical results center on extensive experiments on private data synthesis to demonstrate the effectiveness of stratification on a variety of private mechanisms. Overall, we argue that this straightforward approach provides a strong baseline against which future work on reducing utility disparities of DP mechanisms should be compared.

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AAAI Conference 2023 Conference Paper

Counterfactuals for the Future

Lucius E. J. Bynum
Joshua R. Loftus
Julia Stoyanovich

Counterfactuals are often described as 'retrospective,' focusing on hypothetical alternatives to a realized past. This description relates to an often implicit assumption about the structure and stability of exogenous variables in the system being modeled --- an assumption that is reasonable in many settings where counterfactuals are used. In this work, we consider cases where we might reasonably make a different assumption about exogenous variables; namely, that the exogenous noise terms of each unit do exhibit some unit-specific structure and/or stability. This leads us to a different use of counterfactuals --- a forward-looking rather than retrospective counterfactual. We introduce "counterfactual treatment choice," a type of treatment choice problem that motivates using forward-looking counterfactuals. We then explore how mismatches between interventional versus forward-looking counterfactual approaches to treatment choice, consistent with different assumptions about exogenous noise, can lead to counterintuitive results.

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IJCAI Conference 2019 Conference Paper

Balanced Ranking with Diversity Constraints

Ke Yang
Vasilis Gkatzelis
Julia Stoyanovich

Many set selection and ranking algorithms have recently been enhanced with diversity constraints that aim to explicitly increase representation of historically disadvantaged populations, or to improve the over-all representativeness of the selected set. An unintended consequence of these constraints, however, is reduced in-group fairness: the selected candidates from a given group may not be the best ones, and this unfairness may not be well-balanced across groups. In this paper we study this phenomenon using datasets that comprise multiple sensitive attributes. We then introduce additional constraints, aimed at balancing the in-group fairness across groups, and formalize the induced optimization problems as integer linear programs. Using these programs, we conduct an experimental evaluation with real datasets, and quantify the feasible trade-offs between balance and overall performance in the presence of diversity constraints.

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IJCAI Conference 2018 Conference Paper

Computational Social Choice Meets Databases

Benny Kimelfeld
Phokion G. Kolaitis
Julia Stoyanovich

We develop a novel framework that aims to create bridges between the computational social choice and the database management communities. This framework enriches the tasks currently supported in computational social choice with relational database context, thus making it possible to formulate sophisticated queries about voting rules, candidates, voters, issues, and positions. At the conceptual level, we give rigorous semantics to queries in this framework by introducing the notions of necessary answers and possible answers to queries. At the technical level, we embark on an investigation of the computational complexity of the necessary answers. In particular, we establish a number of results about the complexity of the necessary answers of conjunctive queries involving the plurality rule that contrast sharply with earlier results about the complexity of the necessary winners under the plurality rule.

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AAAI Conference 2018 Conference Paper

Probabilistic Inference Over Repeated Insertion Models

Batya Kenig
Lovro Ilijasić
Haoyue Ping
Benny Kimelfeld
Julia Stoyanovich

Distributions over rankings are used to model user preferences in various settings including political elections and electronic commerce. The Repeated Insertion Model (RIM) gives rise to various known probability distributions over rankings, in particular to the popular Mallows model. However, probabilistic inference on RIM is computationally challenging, and provably intractable in the general case. In this paper we propose an algorithm for computing the marginal probability of an arbitrary partially ordered set over RIM. We analyze the complexity of the algorithm in terms of properties of the model and the partial order, captured by a novel measure termed the “cover width”. We also conduct an experimental study of the algorithm over serial and parallelized implementations. Building upon the relationship between inference with rank distributions and counting linear extensions, we investigate the inference problem when restricted to partial orders that lend themselves to efﬁcient counting of their linear extensions.

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