Papers
Sampling in Unit Time with Kernel Fisher-Rao Flow
Aimee Maurais, Youssef Marzouk
Sampling is as easy as keeping the consistency: convergence guarantee for Consistency Models
Junlong Lyu, Zhitang Chen, Shoubo Feng
SAPG: Split and Aggregate Policy Gradients
Jayesh Singla, Ananye Agarwal, Deepak Pathak
Sarah Frank-Wolfe: Methods for Constrained Optimization with Best Rates and Practical Features
Aleksandr Beznosikov, David Dobre, Gauthier Gidel
SaVeR: Optimal Data Collection Strategy for Safe Policy Evaluation in Tabular MDP
Subhojyoti Mukherjee, Josiah P. Hanna, Robert D Nowak
Scalable AI Safety via Doubly-Efficient Debate
Jonah Brown-Cohen, Geoffrey Irving, Georgios Piliouras
Scalable and Flexible Causal Discovery with an Efficient Test for Adjacency
Alan Nawzad Amin, Andrew Gordon Wilson
Scalable High-Resolution Pixel-Space Image Synthesis with Hourglass Diffusion Transformers
Katherine Crowson, Stefan Andreas Baumann, Alex Birch et al.
Scalable Multiple Kernel Clustering: Learning Clustering Structure from Expectation
Weixuan Liang, En Zhu, Shengju Yu et al.
Scalable Online Exploration via Coverability
Philip Amortila, Dylan J Foster, Akshay Krishnamurthy
Scalable Pre-training of Large Autoregressive Image Models
Alaaeldin El-Nouby, Michal Klein, Shuangfei Zhai et al.
Scalable Safe Policy Improvement for Factored Multi-Agent MDPs
Federico Bianchi, Edoardo Zorzi, Alberto Castellini et al.
Scalable Wasserstein Gradient Flow for Generative Modeling through Unbalanced Optimal Transport
Jaemoo Choi, Jaewoong Choi, Myungjoo Kang
Scale-Free Image Keypoints Using Differentiable Persistent Homology
Giovanni Barbarani, Francesco Vaccarino, Gabriele Trivigno et al.
Scaling Beyond the GPU Memory Limit for Large Mixture-of-Experts Model Training
Yechan Kim, Hwijoon Lim, Dongsu Han
Scaling Down Deep Learning with MNIST-1D
Samuel James Greydanus, Dmitry Kobak
Scaling Exponents Across Parameterizations and Optimizers
Katie E Everett, Lechao Xiao, Mitchell Wortsman et al.
Scaling Laws for Fine-Grained Mixture of Experts
Jan Ludziejewski, Jakub Krajewski, Kamil Adamczewski et al.
Scaling Laws for the Value of Individual Data Points in Machine Learning
Ian Connick Covert, Wenlong Ji, Tatsunori Hashimoto et al.
Scaling Rectified Flow Transformers for High-Resolution Image Synthesis
Patrick Esser, Sumith Kulal, Andreas Blattmann et al.
Scaling Tractable Probabilistic Circuits: A Systems Perspective
Anji Liu, Kareem Ahmed, Guy Van Den Broeck
SceneCraft: An LLM Agent for Synthesizing 3D Scenes as Blender Code
Ziniu Hu, Ahmet Iscen, Aashi Jain et al.
Scene Graph Generation Strategy with Co-occurrence Knowledge and Learnable Term Frequency
Hyeongjin Kim, Sangwon Kim, Dasom Ahn et al.
SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Models
Xiaoxuan Wang, Ziniu Hu, Pan Lu et al.
Score-Based Causal Discovery of Latent Variable Causal Models
Ignavier Ng, Xinshuai Dong, Haoyue Dai et al.