1 citations · 1 across the 8 of their papers we have counts for
21 papers · 1 filter
Learning Discrete Decisions for MIPs with Constraint-Aware Diffusion
Vincenzo Di Vito, Mehdi Taghizadeh, Deepjyoti Deka +2
This paper proposes a novel learning-based approach to approximately solve instances of mixed-integer optimization problems. These problems are computationally challenging, as they…
Simple Self-Conditioning Adaptation for Masked Diffusion Models
Michael Cardei, Huu Binh Ta, Ferdinando Fioretto
Masked diffusion models (MDMs) generate discrete sequences by iterative denoising under an absorbing masking process. In standard masked diffusion, if a token remains masked after…
Constraint-Aware Flow Matching: Decision Aligned End-to-End Training for Constrained Sampling
Jacob K. Christopher, James E. Warner, Ferdinando Fioretto
Deep generative models provide state-of-the-art performance across a wide array of applications, with recent studies showing increasing applicability for science and engineering. D…
Gen-DFL: Decision-Focused Generative Learning for Robust Decision Making
Prince Zizhuang Wang, Shuyi Chen, Jinhao Liang +2
Decision-focused learning (DFL) integrates predictive models with downstream optimization, directly training machine learning models to minimize decision errors. While DFL has been…
SoK: Data Minimization in Machine Learning
Robin Staab, Nikola JovanoviÄ, Kimberly Mai +4
Data minimization (DM) describes the principle of collecting only the data strictly necessary for a given task. It is a foundational principle across major data protection regulati…
Search-Augmented Masked Diffusion Models for Constrained Generation
Huu Binh Ta, Michael Cardei, Alvaro Velasquez +1
Discrete diffusion models generate sequences by iteratively denoising samples corrupted by categorical noise, offering an appealing alternative to autoregressive decoding for struc…