There were six Outstanding Paper Award winners this year in the main track, and two in the position paper track.
Outstanding Papers (Main Track)
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CollabLLM: From Passive Responders to Active Collaborators (Shirley Wu, Michel Galley, Baolin Peng, Hao Cheng, Gavin Li, Yao Dou, Weixin Cai, James Zou, Jure Leskovec, Jianfeng Gao)
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Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions (Jaeyeon Kim, Kulin Shah, Vasilis Kontonis, Sham Kakade, Sitan Chen)
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Roll the dice & look before you leap: Going beyond the creative limits of next-token prediction (Vaishnavh Nagarajan, Chen Wu, Charles Ding, Aditi Raghunathan)
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Conformal Prediction as Bayesian Quadrature (Jake Snell, Thomas Griffiths)
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Score Matching with Missing Data (Josh Givens, Song Liu, Henry Reeve)
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The Value of Prediction in Identifying the Worst-Off (Unai Fischer Abaigar, Christoph Kern, Juan Perdomo)
Outstanding Papers (Position Paper Track)
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Position: The AI Conference Peer Review Crisis Demands Author Feedback and Reviewer Rewards (Jaeho Kim, Yunseok Lee, Seulki Lee)
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Position: AI Safety should prioritize the Future of Work (Sanchaita Hazra, Bodhisattwa Prasad Majumder, Tuhin Chakrabarty)
Test of Time Award
This year’s Test of Time Award goes to Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift by Sergey Ioffe and Christian Szegedy. This paper from 2015 introduced Batch Normalization, a technique to normalise inputs to neural nets in the batch dimension, enabling the use of higher learning rates and faster training. It is a forerunner of Layer Normalization, which performs a similar operation along the feature dimension and was used in the original Transformer paper. The authors of this paper will give a talk at 8:30am Vancouver time on Wednesday.
There were two honorable mentions:
- Trust Region Policy Optimization by John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, and Philipp Moritz.
- Variational Inference with Normalizing Flows by Danilo Rezende and Shakir Mohamed.
The Trust Region Policy Optimization paper introduced the TRPO algorithm for reinforcement learning, a precursor to the now-ubiquitous Proximal Policy Optimization (PPO) algorithm.