Search Results for author:
Found 52 papers, 5 papers with code
Metric Learning to Accelerate Convergence of Operator Splitting Methods for Differentiable Parametric Programming
Recent work has shown a variety of ways in which machine learning can be used to accelerate the solution of constrained optimization problems.
Learning Constrained Optimization with Deep Augmented Lagrangian Methods
Learning to Optimize (LtO) is a problem setting in which a machine learning (ML) model is trained to emulate a constrained optimization solver.
End-to-End Learning for Fair Multiobjective Optimization Under Uncertainty
Many decision processes in artificial intelligence and operations research are modeled by parametric optimization problems whose defining parameters are unknown and must be inferred from observable data.
Learning Fair Ranking Policies via Differentiable Optimization of Ordered Weighted Averages
Learning to Rank (LTR) is one of the most widely used machine learning applications.
Disparate Impact on Group Accuracy of Linearization for Private Inference
Ensuring privacy-preserving inference on cryptographically secure data is a well-known computational challenge.
Projected Generative Diffusion Models for Constraint Satisfaction
Generative diffusion models excel at robustly synthesizing coherent content from raw noise through a sequential process.
Analyzing and Enhancing the Backward-Pass Convergence of Unrolled Optimization
The integration of constrained optimization models as components in deep networks has led to promising advances on many specialized learning tasks.
On The Fairness Impacts of Hardware Selection in Machine Learning
In the machine learning ecosystem, hardware selection is often regarded as a mere utility, overshadowed by the spotlight on algorithms and data.
Predict-Then-Optimize by Proxy: Learning Joint Models of Prediction and Optimization
This paper proposes an alternative method, in which optimal solutions are learned directly from the observable features by predictive models.
Price-Aware Deep Learning for Electricity Markets
While deep learning gradually penetrates operational planning, its inherent prediction errors may significantly affect electricity prices.
Decision-Focused Learning: Foundations, State of the Art, Benchmark and Future Opportunities
Decision-focused learning (DFL) is an emerging paradigm in machine learning which trains a model to optimize decisions, integrating prediction and optimization in an end-to-end system.
FairDP: Certified Fairness with Differential Privacy
This paper introduces FairDP, a novel mechanism designed to achieve certified fairness with differential privacy (DP).
On the Fairness Impacts of Private Ensembles Models
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 and a "student" model.
Personalized Privacy Auditing and Optimization at Test Time
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' information to carry out inference.
Privacy and Bias Analysis of Disclosure Avoidance Systems
The results show that, contrary to popular beliefs, traditional differential privacy techniques may be superior in terms of accuracy and fairness to differential private counterparts of widely used DA mechanisms.
Backpropagation of Unrolled Solvers with Folded Optimization
A central challenge in this setting is backpropagation through the solution of an optimization problem, which typically lacks a closed form.
Context-Aware Differential Privacy for Language Modeling
The remarkable ability of language models (LMs) has also brought challenges at the interface of AI and security.
Fairness Increases Adversarial Vulnerability
The remarkable performance of deep learning models and their applications in consequential domains (e. g., facial recognition) introduces important challenges at the intersection of equity and security.
Differentiable Model Selection for Ensemble Learning
Model selection is a strategy aimed at creating accurate and robust models.
Gradient-Enhanced Physics-Informed Neural Networks for Power Systems Operational Support
The application of deep learning methods to speed up the resolution of challenging power flow problems has recently shown very encouraging results.
Pruning has a disparate impact on model accuracy
Network pruning is a widely-used compression technique that is able to significantly scale down overparameterized models with minimal loss of accuracy.
SF-PATE: Scalable, Fair, and Private Aggregation of Teacher Ensembles
A key characteristic of the proposed model is to enable the adoption of off-the-selves and non-private fair models to create a privacy-preserving and fair model.
Differential Privacy and Fairness in Decisions and Learning Tasks: A Survey
This paper surveys recent work in the intersection of differential privacy (DP) and fairness.
Deadwooding: Robust Global Pruning for Deep Neural Networks
The ability of Deep Neural Networks to approximate highly complex functions is key to their success.
Differentially-Private Heat and Electricity Markets Coordination
Sector coordination between heat and electricity systems has been identified has an energy-efficient and cost-effective way to transition towards a more sustainable energy system.
Post-processing of Differentially Private Data: A Fairness Perspective
Post-processing immunity is a fundamental property of differential privacy: it enables arbitrary data-independent transformations to differentially private outputs without affecting their privacy guarantees.
Differentially Private Empirical Risk Minimization under the Fairness Lens
However, it was recently observed that DP learning systems may exacerbate bias and unfairness for different groups of individuals.
Towards Understanding the Unreasonable Effectiveness of Learning AC-OPF Solutions
Optimal Power Flow (OPF) is a fundamental problem in power systems.
End-to-end Learning for Fair Ranking Systems
The end-to-end SPOFR framework includes a constrained optimization sub-model and produces ranking policies that are guaranteed to satisfy fairness constraints while allowing for fine control of the fairness-utility tradeoff.
Fast Approximations for Job Shop Scheduling: A Lagrangian Dual Deep Learning Method
The Jobs shop Scheduling Problem (JSP) is a canonical combinatorial optimization problem that is routinely solved for a variety of industrial purposes.
A Fairness Analysis on Private Aggregation of Teacher Ensembles
The Private Aggregation of Teacher Ensembles (PATE) is an important private machine learning framework.
Differentially Empirical Risk Minimization under the Fairness Lens
However, it was recently observed that DP learning systems may exacerbate bias and unfairness for different groups of individuals.
Learning Hard Optimization Problems: A Data Generation Perspective
Optimization problems are ubiquitous in our societies and are present in almost every segment of the economy.
A Privacy-Preserving and Trustable Multi-agent Learning Framework
Distributed multi-agent learning enables agents to cooperatively train a model without requiring to share their datasets.
Decision Making with Differential Privacy under a Fairness Lens
Agencies, such as the U. S. Census Bureau, release data sets and statistics about groups of individuals that are used as input to a number of critical decision processes.
End-to-End Constrained Optimization Learning: A Survey
This paper surveys the recent attempts at leveraging machine learning to solve constrained optimization problems.
Load Encoding for Learning AC-OPF
The AC Optimal Power Flow (AC-OPF) problem is a core building block in electrical transmission system.
Bias and Variance of Post-processing in Differential Privacy
Moreover, when the feasible region is convex, a widely adopted class of post-processing steps is also guaranteed to improve accuracy.
Differentially Private and Fair Deep Learning: A Lagrangian Dual Approach
A critical concern in data-driven decision making is to build models whose outcomes do not discriminate against some demographic groups, including gender, ethnicity, or age.
High-Fidelity Machine Learning Approximations of Large-Scale Optimal Power Flow
It is non-convex and NP-hard, and computationally challenging for large-scale power systems.
BIG-bench Machine Learning
Vocal Bursts Intensity Prediction
Differential Privacy of Hierarchical Census Data: An Optimization Approach
To address them, this paper presents a novel differential-privacy mechanism for releasing hierarchical counts of individuals.
Differentially Private Convex Optimization with Feasibility Guarantees
This paper develops a novel differentially private framework to solve convex optimization problems with sensitive optimization data and complex physical or operational constraints.
Bilevel Optimization for Differentially Private Optimization in Energy Systems
This paper studies how to apply differential privacy to constrained optimization problems whose inputs are sensitive.
Lagrangian Duality for Constrained Deep Learning
In energy domains, the combination of Lagrangian duality and deep learning can be used to obtain state-of-the-art results to predict optimal power flows, in energy systems, and optimal compressor settings, in gas networks.
Predicting AC Optimal Power Flows: Combining Deep Learning and Lagrangian Dual Methods
The Optimal Power Flow (OPF) problem is a fundamental building block for the optimization of electrical power systems.
Privacy-Preserving Obfuscation of Critical Infrastructure Networks
The paper studies how to release data about a critical infrastructure network (e. g., the power network or a transportation network) without disclosing sensitive information that can be exploited by malevolent agents, while preserving the realism of the network.
Differential Privacy for Power Grid Obfuscation
To address these concerns, this paper presents a novel differential privacy mechanism that guarantees AC-feasibility and largely preserves the fidelity of the obfuscated network.
OptStream: Releasing Time Series Privately
Then, the perturbation module adds noise to the sampled data points to guarantee privacy.
Solving DCOPs with Distributed Large Neighborhood Search
The field of Distributed Constraint Optimization has gained momentum in recent years, thanks to its ability to address various applications related to multi-agent cooperation.
A Realistic Dataset for the Smart Home Device Scheduling Problem for DCOPs
The field of Distributed Constraint Optimization has gained momentum in recent years thanks to its ability to address various applications related to multi-agent cooperation.
Accelerating Exact and Approximate Inference for (Distributed) Discrete Optimization with GPUs
Discrete optimization is a central problem in artificial intelligence.
Distributed Constraint Optimization Problems and Applications: A Survey
The field of Multi-Agent System (MAS) is an active area of research within Artificial Intelligence, with an increasingly important impact in industrial and other real-world applications.
