118 citations · 200 across the 58 of their papers we have counts for
16 papers · 1 filter
SpecDiff-2: Scaling Diffusion Drafter Alignment For Faster Speculative Decoding
Jameson Sandler, Jacob K. Christopher, Thomas Hartvigsen +1
Speculative decoding has become the standard approach for accelerating Large Language Model (LLM) inference. It exploits a lossless draft-then-verify procedure to circumvent the la…
The Disparate Impacts of Speculative Decoding
Jameson Sandler, Ahmet Üstün, Marco Romanelli +2
The practice of speculative decoding, whereby inference is probabilistically supported by a smaller, cheaper, ``drafter'' model, has become a standard technique for systematically…
Constrained Diffusion for Protein Design with Hard Structural Constraints
Jacob K. Christopher, Austin Seamann, Jingyi Cui +2
Diffusion models offer a powerful means of capturing the manifold of realistic protein structures, enabling rapid design for protein engineering tasks. However, existing approaches…
Chance-constrained Flow Matching for High-Fidelity Constraint-aware Generation
Jinhao Liang, Yixuan Sun, Anirban Samaddar +2
Generative models excel at synthesizing high-fidelity samples from complex data distributions, but they often violate hard constraints arising from physical laws or task specificat…
Learning to Solve Optimization Problems Constrained with Partial Differential Equations
Yusuf Guven, Vincenzo Di Vito, Ferdinando Fioretto
Partial differential equation (PDE)-constrained optimization arises in many scientific and engineering domains, such as energy systems, fluid dynamics and material design. In these…
Discrete-Guided Diffusion for Scalable and Safe Multi-Robot Motion Planning
Jinhao Liang, Sven Koenig, Ferdinando Fioretto
Multi-Robot Motion Planning (MRMP) involves generating collision-free trajectories for multiple robots operating in a shared continuous workspace. While discrete multi-agent path f…
SoK: Data Minimization in Machine Learning
Robin Staab, Nikola Jovanović, 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…
Stability-Constrained AC Optimal Power Flow--A Gaussian Process-Based Approach
Vincenzo Di Vito, Kaarthik Sundar, Ferdinando Fioretto +1
The Alternating Current Optimal Power Flow (ACOPF) problem is a core task in power system operations, aimed at determining cost-effective generation dispatch while satisfying physi…
Learning to Solve Constrained Bilevel Control Co-Design Problems
James Kotary, Himanshu Sharma, Ethan King +3
Learning to Optimize (L2O) is a subfield of machine learning (ML) in which ML models are trained to solve parametric optimization problems. The general goal is to learn a fast appr…
Beyond Jailbreaking: Auditing Contextual Privacy in LLM Agents
Saswat Das, Jameson Sandler, Ferdinando Fioretto
LLM agents have begun to appear as personal assistants, customer service bots, and clinical aides. While these applications deliver substantial operational benefits, they also requ…
Neuro-Symbolic Generative Diffusion Models for Physically Grounded, Robust, and Safe Generation
Jacob K. Christopher, Michael Cardei, Jinhao Liang +1
Despite the remarkable generative capabilities of diffusion models, their integration into safety-critical or scientifically rigorous applications remains hindered by the need to e…
Constrained Discrete Diffusion
Michael Cardei, Jacob K Christopher, Thomas Hartvigsen +2
Discrete diffusion models are a class of generative models that construct sequences by progressively denoising samples from a categorical noise distribution. Beyond their rapidly g…
Global-Decision-Focused Neural ODEs for Proactive Grid Resilience Management
Shuyi Chen, Ferdinando Fioretto, Feng Qiu +1
Extreme hazard events such as wildfires and hurricanes increasingly threaten power systems, causing widespread outages and disrupting critical services. Recently, predict-then-opti…
Training-Free Constrained Generation With Stable Diffusion Models
Stefano Zampini, Jacob K. Christopher, Luca Oneto +2
Stable diffusion models represent the state-of-the-art in data synthesis across diverse domains and hold transformative potential for applications in science and engineering, e.g.,…
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…
Simultaneous Multi-Robot Motion Planning with Projected Diffusion Models
Jinhao Liang, Jacob K Christopher, Sven Koenig +1
Recent advances in diffusion models hold significant potential in robotics, enabling the generation of diverse and smooth trajectories directly from raw representations of the envi…