1 citations · 3 across the 9 of their papers we have counts for
10 papers
Social Choice Foundations for Simulation-Augmented Generation
Sonja Kraiczy, Smitha Milli, Ratip Emin Berker +6
Simulation-augmented generation (SAGE) is a recent technical proposal in which models simulate individuals' viewpoints at inference time in order to provide more representative ans…
The Surprising Difficulty of Search in Model-Based Reinforcement Learning
Wei-Di Chang, Mikael Henaff, Brandon Amos +2
This paper investigates search in model-based reinforcement learning (RL). Conventional wisdom holds that long-term predictions and compounding errors are the primary obstacles for…
A Fully First-Order Layer for Differentiable Optimization
Zihao Zhao, Kai-Chia Mo, Shing-Hei Ho +2
Differentiable optimization layers enable learning systems to make decisions by solving embedded optimization problems. However, computing gradients via implicit differentiation re…
BaNEL: Exploration Posteriors for Generative Modeling Using Only Negative Rewards
Sangyun Lee, Brandon Amos, Giulia Fanti
Today's generative models thrive with large amounts of supervised data and informative reward functions characterizing the quality of the generation. They work under the assumption…
Cultivating Pluralism In Algorithmic Monoculture: The Community Alignment Dataset
Lily Hong Zhang, Smitha Milli, Karen Jusko +12
How can large language models (LLMs) serve users with varying preferences that may conflict across cultural, political, or other dimensions? To advance this challenge, this paper e…
Online Intrinsic Rewards for Decision Making Agents from Large Language Model Feedback
Qinqing Zheng, Mikael Henaff, Amy Zhang +2
Automatically synthesizing dense rewards from natural language descriptions is a promising paradigm in reinforcement learning (RL), with applications to sparse reward problems, ope…
Exact Byte-Level Probabilities from Tokenized Language Models for FIM-Tasks and Model Ensembles
Buu Phan, Brandon Amos, Itai Gat +3
Tokenization is associated with many poorly understood shortcomings in language models (LMs), yet remains an important component for long sequence scaling purposes. This work studi…
Meta Flow Matching: Integrating Vector Fields on the Wasserstein Manifold
Lazar Atanackovic, Xi Zhang, Brandon Amos +5
Numerous biological and physical processes can be modeled as systems of interacting entities evolving continuously over time, e.g. the dynamics of communicating cells or physical p…
Unlocking Tokens as Data Points for Generalization Bounds on Larger Language Models
Sanae Lotfi, Yilun Kuang, Brandon Amos +3
Large language models (LLMs) with billions of parameters excel at predicting the next token in a sequence. Recent work computes non-vacuous compression-based generalization bounds…
Sliced Multi-Marginal Optimal Transport
Samuel Cohen, Alexander Terenin, Yannik Pitcan +3
Multi-marginal optimal transport enables one to compare multiple probability measures, which increasingly finds application in multi-task learning problems. One practical limitatio…