Industry
A Simple Remedy for Dataset Bias via Self-Influence: A Mislabeled Sample Perspective Y eonsung Jung
However, spurious correlation remains an ongoing challenge, primarily due to the difficulty in precisely detecting these samples.
Contrastive Retrospection: honing in on critical steps for rapid learning and generalization in RL Chen Sun
In real life, success is often contingent upon multiple critical steps that are distant in time from each other and from the final reward.
Coresets for Clustering with Fairness Constraints
Lingxiao Huang, Shaofeng Jiang, Nisheeth Vishnoi
Neural Information Processing Systems http://nips.cc/
Teaching Inverse Reinforcement Learners via Features and Demonstrations
Luis Haug, Sebastian Tschiatschek, Adish Singla
Weintroduceanaturalquantity,the teaching risk, which measures the potential suboptimality of policies that look optimal to the learner in this setting. We show that bounds on the teaching risk guarantee that the learner is able to find a near-optimal policy using standard algorithms basedoninversereinforcement learning. Basedonthesefindings, we suggest a teaching scheme in which the expert can decrease the teaching risk by updating the learner's worldview, and thus ultimately enable her to find a near-optimalpolicy.
A Limitation, future work, and societal impact 452 A.1 Limitation and future work
The image pairs are mined from existing datasets.
ReEvo: Large Language Models as Hyper-Heuristics with Reflective Evolution Haoran Y e
The omnipresence of NP-hard combinatorial optimization problems (COPs) compels domain experts to engage in trial-and-error heuristic design. The long-standing endeavor of design automation has gained new momentum with the rise of large language models (LLMs).
Reducing Network Agnostophobia
Akshay Raj Dhamija, Manuel Günther, Terrance Boult
Neural Information Processing Systems http://nips.cc/
3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data
Maurice Weiler, Mario Geiger, Max Welling, Wouter Boomsma, Taco S. Cohen
Neural Information Processing Systems http://nips.cc/
