Papers
Position: Automatic Environment Shaping is the Next Frontier in RL
Younghyo Park, Gabriel B. Margolis, Pulkit Agrawal
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI
Theodore Papamarkou, Maria Skoularidou, Konstantina Palla et al.
Position: Benchmarking is Limited in Reinforcement Learning Research
Scott M. Jordan, Adam White, Bruno Castro Da Silva et al.
Position: Building Guardrails for Large Language Models Requires Systematic Design
Yi Dong, Ronghui Mu, Gaojie Jin et al.
Position: Categorical Deep Learning is an Algebraic Theory of All Architectures
Bruno Gavranović, Paul Lessard, Andrew Joseph Dudzik et al.
Position: Compositional Generative Modeling: A Single Model is Not All You Need
Yilun Du, Leslie Pack Kaelbling
Position: Considerations for Differentially Private Learning with Large-Scale Public Pretraining
Florian Tramèr, Gautam Kamath, Nicholas Carlini
Position: Cracking the Code of Cascading Disparity Towards Marginalized Communities
Golnoosh Farnadi, Mohammad Havaei, Negar Rostamzadeh
Position: Data Authenticity, Consent, & Provenance for AI are all broken: what will it take to fix them?
Shayne Longpre, Robert Mahari, Naana Obeng-Marnu et al.
Position: Data-driven Discovery with Large Generative Models
Bodhisattwa Prasad Majumder, Harshit Surana, Dhruv Agarwal et al.
Position: Do Not Explain Vision Models Without Context
Paulina Tomaszewska, Przemyslaw Biecek
Position: Do pretrained Transformers Learn In-Context by Gradient Descent?
Lingfeng Shen, Aayush Mishra, Daniel Khashabi
Position: Embracing Negative Results in Machine Learning
Florian Karl, Malte Kemeter, Gabriel Dax et al.
Position: Evolving AI Collectives Enhance Human Diversity and Enable Self-Regulation
Shiyang Lai, Yujin Potter, Junsol Kim et al.
Position: Explain to Question not to Justify
Przemyslaw Biecek, Wojciech Samek
Position: Exploring the Robustness of Pipeline-Parallelism-Based Decentralized Training
Lin Lu, Chenxi Dai, Wangcheng Tao et al.
Position: Foundation Agents as the Paradigm Shift for Decision Making
Xiaoqian Liu, Xingzhou Lou, Jianbin Jiao et al.
Position: Fundamental Limitations of LLM Censorship Necessitate New Approaches
David Glukhov, Ilia Shumailov, Yarin Gal et al.
Position: Future Directions in the Theory of Graph Machine Learning
Christopher Morris, Fabrizio Frasca, Nadav Dym et al.
Position: Graph Foundation Models Are Already Here
Haitao Mao, Zhikai Chen, Wenzhuo Tang et al.
Position: Insights from Survey Methodology can Improve Training Data
Stephanie Eckman, Barbara Plank, Frauke Kreuter
Position: Intent-aligned AI Systems Must Optimize for Agency Preservation
Catalin Mitelut, Benjamin Smith, Peter Vamplew
Position: Is machine learning good or bad for the natural sciences?
David W Hogg, Soledad Villar