118 citations · 200 across the 58 of their papers we have counts for
12 papers · 1 filter
Analyzing and Enhancing the Backward-Pass Convergence of Unrolled Optimization
James Kotary, Jacob Christopher, My H Dinh +1
The integration of constrained optimization models as components in deep networks has led to promising advances on many specialized learning tasks. A central challenge in this sett…
On The Fairness Impacts of Hardware Selection in Machine Learning
Sree Harsha Nelaturu, Nishaanth Kanna Ravichandran, Cuong Tran +2
In the machine learning ecosystem, hardware selection is often regarded as a mere utility, overshadowed by the spotlight on algorithms and data. This oversight is particularly prob…
Predict-Then-Optimize by Proxy: Learning Joint Models of Prediction and Optimization
James Kotary, Vincenzo Di Vito, Jacob Christopher +2
Many real-world decision processes are modeled by optimization problems whose defining parameters are unknown and must be inferred from observable data. The Predict-Then-Optimize f…
Price-Aware Deep Learning for Electricity Markets
Vladimir Dvorkin, Ferdinando Fioretto
While deep learning gradually penetrates operational planning, its inherent prediction errors may significantly affect electricity prices. This letter examines how prediction error…
Decision-Focused Learning: Foundations, State of the Art, Benchmark and Future Opportunities
Jayanta Mandi, James Kotary, Senne Berden +4
Decision-focused learning (DFL) is an emerging paradigm that integrates machine learning (ML) and constrained optimization to enhance decision quality by training ML models in an e…
Data Minimization at Inference Time
Cuong Tran, Ferdinando Fioretto
In domains with high stakes such as law, recruitment, and healthcare, learning models frequently rely on sensitive user data for inference, necessitating the complete set of featur…
On the Fairness Impacts of Private Ensembles Models
Cuong Tran, Ferdinando Fioretto
The Private Aggregation of Teacher Ensembles (PATE) is a machine learning framework that enables the creation of private models through the combination of multiple "teacher" models…
FairDP: Certified Fairness with Differential Privacy
Khang Tran, Ferdinando Fioretto, Issa Khalil +2
This paper introduces FairDP, a novel training mechanism designed to provide group fairness certification for the trained model's decisions, along with a differential privacy (DP)…
Personalized Privacy Auditing and Optimization at Test Time
Cuong Tran, Ferdinando Fioretto
A number of learning models used in consequential domains, such as to assist in legal, banking, hiring, and healthcare decisions, make use of potentially sensitive users' informati…
Context-Aware Differential Privacy for Language Modeling
My H. Dinh, Ferdinando Fioretto
The remarkable ability of language models (LMs) has also brought challenges at the interface of AI and security. A critical challenge pertains to how much information these models…
Privacy and Bias Analysis of Disclosure Avoidance Systems
Keyu Zhu, Ferdinando Fioretto, Pascal Van Hentenryck +2
Disclosure avoidance (DA) systems are used to safeguard the confidentiality of data while allowing it to be analyzed and disseminated for analytic purposes. These methods, e.g., ce…
Backpropagation of Unrolled Solvers with Folded Optimization
James Kotary, My H. Dinh, Ferdinando Fioretto
The integration of constrained optimization models as components in deep networks has led to promising advances on many specialized learning tasks. A central challenge in this sett…