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Eric Frankel

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2 papers

1 author row

ICML Conference 2025 Conference Paper

Finite-Time Convergence Rates in Stochastic Stackelberg Games with Smooth Algorithmic Agents

Eric Frankel
Kshitij Kulkarni
Dmitriy Drusvyatskiy
Sewoong Oh
Lillian J. Ratliff

Decision-makers often adaptively influence downstream competitive agents’ behavior to minimize their cost, yet in doing so face critical challenges: $(i)$ decision-makers might not a priori know the agents’ objectives; $(ii)$ agents might learn their responses, introducing stochasticity and non-stationarity into the decision-making process; and $(iii)$ there may be additional non-strategic environmental stochasticity. Characterizing convergence of this complex system is contingent on how the decision-maker controls for the tradeoff between the induced drift and additional noise from the learning agent behavior and environmental stochasticity. To understand how the learning agents’ behavior is influenced by the decision-maker’s actions, we first consider a decision-maker that deploys an arbitrary sequence of actions which induces a sequence of games and corresponding equilibria. We characterize how the drift and noise in the agents’ stochastic algorithms decouples from their optimization error. Leveraging this decoupling and accompanying finite-time efficiency estimates, we design decision-maker algorithms that control the induced drift relative to the agent noise. This enables efficient finite-time tracking of game theoretic equilibrium concepts that adhere to the incentives of the players’ collective learning processes.

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ICML Conference 2025 Conference Paper

S4S: Solving for a Fast Diffusion Model Solver

Eric Frankel
Sitan Chen
Jerry Li 0001
Pang Wei Koh
Lillian J. Ratliff
Sewoong Oh

Diffusion models (DMs) create samples from a data distribution by starting from random noise and iteratively solving a reverse-time ordinary differential equation (ODE). Because each step in the iterative solution requires an expensive neural function evaluation (NFE), there has been significant interest in approximately solving these diffusion ODEs with only a few NFEs without modifying the underlying model. However, in the few NFE regime, we observe that tracking the true ODE evolution is fundamentally impossible using traditional ODE solvers. In this work, we propose a new method that learns a good solver for the DM, which we call S olving for the S olver ( S4S ). S4S directly optimizes a solver to obtain good generation quality by learning to match the output of a strong teacher solver. We evaluate S4S on six different pre-trained DMs, including pixel-space and latent-space DMs for both conditional and unconditional sampling. In all settings, S4S uniformly improves the sample quality relative to traditional ODE solvers. Moreover, our method is lightweight, data-free, and can be plugged in black-box on top of any discretization schedule or architecture to improve performance. Building on top of this, we also propose S4S-Alt, which optimizes both the solver and the discretization schedule. By exploiting the full design space of DM solvers, with 5 NFEs, we achieve an FID of 3. 73 on CIFAR10 and 13. 26 on MS-COCO, representing a $1. 5\times$ improvement over previous training-free ODE methods.

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