2 citations · 2 across the 4 of their papers we have counts for
4 papers
Verifier-Constrained Flow Expansion for Discovery Beyond the Data
Riccardo De Santi, Kimon Protopapas, Ya-Ping Hsieh +1
Flow and diffusion models are typically pre-trained on limited available data (e.g., molecular samples), covering only a fraction of the valid design space (e.g., the full molecula…
A Unified Density Operator View of Flow Control and Merging
Riccardo De Santi, Malte Franke, Ya-Ping Hsieh +1
Recent progress in large-scale flow and diffusion models raised two fundamental algorithmic challenges: (i) control-based reward adaptation of pre-trained flows, and (ii) integrati…
Sinkhorn Flow: A Continuous-Time Framework for Understanding and Generalizing the Sinkhorn Algorithm
Mohammad Reza Karimi, Ya-Ping Hsieh, Andreas Krause
Many problems in machine learning can be formulated as solving entropy-regularized optimal transport on the space of probability measures. The canonical approach involves the Sinkh…
Riemannian stochastic optimization methods avoid strict saddle points
Ya-Ping Hsieh, Mohammad Reza Karimi, Andreas Krause +1
Many modern machine learning applications - from online principal component analysis to covariance matrix identification and dictionary learning - can be formulated as minimization…