Papers
Position: The Causal Revolution Needs Scientific Pragmatism
Joshua R. Loftus
Position: The No Free Lunch Theorem, Kolmogorov Complexity, and the Role of Inductive Biases in Machine Learning
Micah Goldblum, Marc Anton Finzi, Keefer Rowan et al.
Position: The Platonic Representation Hypothesis
Minyoung Huh, Brian Cheung, Tongzhou Wang et al.
Position: The Reasonable Person Standard for AI
Sunayana Rane
Position: Topological Deep Learning is the New Frontier for Relational Learning
Theodore Papamarkou, Tolga Birdal, Michael M. Bronstein et al.
Position: Towards Implicit Prompt For Text-To-Image Models
Yue Yang, Yuqi Lin, Hong Liu et al.
Position: Towards Unified Alignment Between Agents, Humans, and Environment
Zonghan Yang, An Liu, Zijun Liu et al.
Position: TrustLLM: Trustworthiness in Large Language Models
Yue Huang, Lichao Sun, Haoran Wang et al.
Position: Understanding LLMs Requires More Than Statistical Generalization
Patrik Reizinger, Szilvia Ujváry, Anna Mészáros et al.
Position: Video as the New Language for Real-World Decision Making
Sherry Yang, Jacob C Walker, Jack Parker-Holder et al.
Position: What Can Large Language Models Tell Us about Time Series Analysis
Ming Jin, Yifan Zhang, Wei Chen et al.
Position: What makes an image realistic?
Lucas Theis
Position: Why Tabular Foundation Models Should Be a Research Priority
Boris Van Breugel, Mihaela Van Der Schaar
Position: Why We Must Rethink Empirical Research in Machine Learning
Moritz Herrmann, F. Julian D. Lange, Katharina Eggensperger et al.
Position: Will we run out of data? Limits of LLM scaling based on human-generated data
Pablo Villalobos, Anson Ho, Jaime Sevilla et al.
Positive and Unlabeled Learning with Controlled Probability Boundary Fence
Changchun Li, Yuanchao Dai, Lei Feng et al.
Positive Concave Deep Equilibrium Models
Mateusz Gabor, Tomasz Piotrowski, Renato L. G. Cavalcante
Posterior Sampling-Based Bayesian Optimization with Tighter Bayesian Regret Bounds
Shion Takeno, Yu Inatsu, Masayuki Karasuyama et al.
Post-hoc Part-Prototype Networks
Andong Tan, Fengtao Zhou, Hao Chen
Potential Based Diffusion Motion Planning
Yunhao Luo, Chen Sun, Joshua B. Tenenbaum et al.
PPFLOW: Target-Aware Peptide Design with Torsional Flow Matching
Haitao Lin, Odin Zhang, Huifeng Zhao et al.
Practical Performance Guarantees for Pipelined DNN Inference
Aaron Archer, Matthew Fahrbach, Kuikui Liu et al.
Pragmatic Feature Preferences: Learning Reward-Relevant Preferences from Human Input
Andi Peng, Yuying Sun, Tianmin Shu et al.
Precise Accuracy / Robustness Tradeoffs in Regression: Case of General Norms
Elvis Dohmatob, Meyer Scetbon