Danial Dervovic — accepted AI-conference papers

2026

MAFE: Enabling Equitable Algorithm Design in Multi-Agent Multi-Stage Decision-Making Systems

Zachary Lazri, Anirudh Nakra, Ivan Brugere, Danial Dervovic, Antigoni Polychroniadou, Furong Huang +2

ICML 2026poster

Algorithmic fairness is often studied in static or single-agent settings, yet many real-world decision-making systems involve multiple interacting entities whose multi-stage actions jointly influence long-term outcomes. Existing fairness methods applied at isolated decision points frequently fail to…

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2026

Revisiting ML Training under Fully Homomorphic Encryption: Convergence Guarantees, Differential Privacy, and Efficient Algorithms

Yvonne Zhou, Mingyu Liang, Ivan Brugere, Danial Dervovic, Yue Guo, Antigoni Polychroniadou +2

ICML 2026poster

We present the first theoretical convergence analysis of machine learning training under fully homomorphic encryption (FHE), combined with a differentially private (DP) training algorithm tailored to encrypted computation. Our approach improves computational efficiency over standard differentially p…

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2025

Cross-Domain Graph Data Scaling: A Showcase with Diffusion Models

Wenzhuo Tang, Haitao Mao, Danial Dervovic, Ivan Brugere, Saumitra Mishra, Yuying Xie +1

NeurIPS 2025poster

Models for natural language and images benefit from data scaling behavior: the more data fed into the model, the better they perform. This 'better with more' phenomenon enables the effectiveness of large-scale pre-training on vast amounts of data. However, current graph pre-training methods struggle…

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2025

Model Evaluation in the Dark: Robust Classifier Metrics with Missing Labels

Danial Dervovic, Michael Cashmore

AISTATS 2025poster

Missing data in supervised learning is well-studied, but the specific issue of missing labels during model evaluation has been overlooked. Ignoring samples with missing values, a common solution, can introduce bias, especially when data is Missing Not At Random (MNAR). We propose a multiple imputati…

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2024

Are Logistic Models Really Interpretable?

Danial Dervovic, Freddy Lecue, Nicolas Marchesotti, Daniele Magazzeni

IJCAI 2024poster

The demand for open and trustworthy AI models points towards widespread publishing of model weights. Consumers of these model weights must be able to act accordingly with the information provided. That said, one of the simplest AI classification models, Logistic Regression (LR), has an unwieldy inte…

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2024

Bounding the Excess Risk for Linear Models Trained on Marginal-Preserving, Differentially-Private, Synthetic Data

Yvonne Zhou, Mingyu Liang, Ivan Brugere, Danial Dervovic, Antigoni Polychroniadou, Min Wu +1

ICML 2024poster

The growing use of machine learning (ML) has raised concerns that an ML model may reveal private information about an individual who has contributed to the training dataset. To prevent leakage of sensitive data, we consider using differentially- private (DP), synthetic training data instead of real…

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2022

Optimal Admission Control for Multiclass Queues with Time-Varying Arrival Rates via State Abstraction

Marc Rigter, Danial Dervovic, Parisa Hassanzadeh, Jason Long, Parisa Zehtabi, Daniele Magazzeni

AAAI 2022technical

We consider a novel queuing problem where the decision-maker must choose to accept or reject randomly arriving tasks into a no buffer queue which are processed by N identical servers. Each task has a price, which is a positive real number, and a class. Each class of task has a different price distri…

Cited by 6 Source ↗PDF ↗Scholar ↗

2021

Non-Parametric Stochastic Sequential Assignment With Random Arrival Times

Danial Dervovic, Parisa Hassanzadeh, Samuel Assefa, Prashant Reddy

IJCAI 2021poster

We consider a problem wherein jobs arrive at random times and assume random values. Upon each job arrival, the decision-maker must decide immediately whether or not to accept the job and gain the value on offer as a reward, with the constraint that they may only accept at most n jobs over some refer…

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