6 accepted papers
Efficient and accurate motion prediction is crucial for ensuring safety and informed decision-making in autonomous driving, particularly under dynamic real-world conditions that necessitate multi-modal forecasts. We introduce TrajFlow, a novel flow matching-based motion prediction framework that add
Financial transactions are increasingly being handled by automated programs called *smart contracts*. However, one challenge in the adaptation of smart contracts is the presence of vulnerabilities, which can cause significant monetary loss. In 2024, $247.88 M was lost in 20 smart contract exploits…
Visual Prompting (VP) has been gaining traction in the deep learning community, yet its performance often falls short when compared to traditional finetuning methods in transfer learning. In this study, we present a novel approach to enhance VP by leveraging the insights from the lottery ticket hypo…
In many game settings, the game is not explicitly given but is only accessible by playing it. While there have been impressive demonstrations in such settings, prior techniques have not offered safety guarantees, that is, guarantees on the game-theoretic exploitability of the computed strategies. I…
Computational equilibrium finding in large zero-sum extensive-form imperfect-information games has led to significant recent AI breakthroughs. The fastest algorithms for the problem are new forms of counterfactual regret minimization (Brown & Sandholm, 2019). In this paper we present a totally diffe…
Given the apparent difficulty of learning models that are robust to adversarial perturbations, we propose tackling the simpler problem of developing adversarially robust features. Specifically, given a dataset and metric of interest, the goal is to return a function (or multiple functions) that 1)…