2 citations · 3 across the 21 of their papers we have counts for
17 papers · 1 filter
Your Temporal Link Predictor Is Blind to Who Is Active: A Missing Factor That Transfers Across Models
Ji Zhang, Zixin Liu, Yiran Ding +3
An interaction has two parts: someone decides to act, and then chooses whom to act on. Temporal link prediction has concentrated on the second, and we show that it is blind to the…
Blackboard Intelligence Can Surpass Autoregressive on Globally Constrained Problems
Woosang Jeon, Jaeyeon Kim, Sham Kakade +6
Next-token prediction has driven remarkable progress in large language models, yet a growing body of evidence suggests that they can struggle on problems governed by complex global…
Beckmann Transport Models: From Autonomous Flows to One-Step Maps
Lee Cheuk-Kit, Florentin Coeurdoux, Yuyuan Chen +5
We propose an instantiation of flow matching that relies on a time-independent velocity field (an \emph{autonomous flow}) to exactly map between two distributions, so long as the t…
From Interface to Inference: Eliciting Any-Order Inference from Any-Order Models
Seunggeun Kim, Jaeyeon Kim, Taekyun Lee +4
Many discrete reasoning tasks, such as code generation, are inherently non-causal: programmers move between high-level structure and local details, a process we call any-order infe…
Explorative Modeling: Unlocking a Third Pretraining Axis and End-to-End Generation
Alexi Gladstone, Heng Ji, Yilun Du
The deep learning revolution, kicked off by AlexNet, taught us that end-to-end training beats decomposing a problem into hand-designed stages. Generative modeling, however, has rem…
Graph Energy Matching: Transport-Aligned Energy-Based Modeling for Graph Generation
Michal Balcerak, Suprosanna Shit, Chinmay Prabhakar +4
Generative modeling of discrete data, such as graphs, underpins many scientific and industrial applications, including molecular discovery and materials design. In these domains, p…
Generative Modeling via Drifting
Mingyang Deng, He Li, Tianhong Li +2
Generative modeling can be formulated as learning a mapping f such that its pushforward distribution matches the data distribution. The pushforward behavior can be carried out iter…
Generalizable Reasoning through Compositional Energy Minimization
Alexandru Oarga, Yilun Du
Generalization is a key challenge in machine learning, specifically in reasoning tasks, where models are expected to solve problems more complex than those encountered during train…
Reasoning with Sampling: Your Base Model is Smarter Than You Think
Aayush Karan, Yilun Du
Frontier reasoning models have exhibited incredible capabilities across a wide array of disciplines, driven by posttraining large language models (LLMs) with reinforcement learning…
Equilibrium Matching: Generative Modeling with Implicit Energy-Based Models
Runqian Wang, Yilun Du
We introduce Equilibrium Matching (EqM), a generative modeling framework built from an equilibrium dynamics perspective. EqM discards the non-equilibrium, time-conditional dynamics…
Test-Time Graph Search for Goal-Conditioned Reinforcement Learning
Evgenii Opryshko, Junwei Quan, Claas Voelcker +2
Offline goal-conditioned reinforcement learning (GCRL) often struggles with long-horizon tasks, where errors in value estimation accumulate and produce unreliable policies. It is t…
Selective Underfitting in Diffusion Models
Kiwhan Song, Jaeyeon Kim, Sitan Chen +3
Diffusion models have emerged as the principal paradigm for generative modeling across various domains. During training, they learn the score function, which in turn is used to gen…
Any-Order Flexible Length Masked Diffusion
Jaeyeon Kim, Lee Cheuk-Kit, Carles Domingo-Enrich +5
Masked diffusion models (MDMs) have recently emerged as a promising alternative to autoregressive models over discrete domains. MDMs generate sequences in an any-order, parallel fa…
Energy-Based Transformers are Scalable Learners and Thinkers
Alexi Gladstone, Ganesh Nanduru, Md Mofijul Islam +7
Inference-time computation techniques, analogous to human System 2 Thinking, have recently become popular for improving model performances. However, most existing approaches suffer…
Follow the Energy, Find the Path: Riemannian Metrics from Energy-Based Models
Louis Béthune, David Vigouroux, Yilun Du +3
What is the shortest path between two data points lying in a high-dimensional space? While the answer is trivial in Euclidean geometry, it becomes significantly more complex when t…
History-Guided Video Diffusion
Kiwhan Song, Boyuan Chen, Max Simchowitz +3
Classifier-free guidance (CFG) is a key technique for improving conditional generation in diffusion models, enabling more accurate control while enhancing sample quality. It is nat…
Compositional Generative Inverse Design
Tailin Wu, Takashi Maruyama, Long Wei +4
Inverse design, where we seek to design input variables in order to optimize an underlying objective function, is an important problem that arises across fields such as mechanical…