118 citations · 200 across the 58 of their papers we have counts for
6 papers · 1 filter
Posterior Flow Matching
Chenrui Ma, Xi Xiao, Lin Zhao +4
Flow matching learns how to transport noise toward data, but its mean prediction does not directly report the endpoint uncertainty remaining at an intermediate state. This paper in…
From Positionwise Confidence to Prefix Scheduling: Verifier Skipping in Speculative Decoding
Haoxuan Luo, Jameson Sandler, Ferdinando Fioretto
Speculative decoding is a leading technique to reduce the cost of autoregressive generation by using a small drafter to propose several tokens, which are then verified in parallel…
Constrained Graph Diffusion for Mixed Integer Optimization
Vincenzo Di Vito, Yusuf Guven, Mehdi Taghizadeh +4
This paper proposes a novel learning-based approach to approximately solve instances of mixed-integer optimization problems. These problems are computationally challenging, as they…
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…
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…
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…