Papers
Latent World Models For Intrinsically Motivated Exploration
Aleksandr Ermolov, Nicu Sebe
Leap-Of-Thought: Teaching Pre-Trained Models to Systematically Reason Over Implicit Knowledge
Alon Talmor, Oyvind Tafjord, Peter Clark et al.
Learnability with Indirect Supervision Signals
Kaifu Wang, Qiang Ning, Dan Roth
Learning About Objects by Learning to Interact with Them
Martin Lohmann, Jordi Salvador, Aniruddha Kembhavi et al.
Learning abstract structure for drawing by efficient motor program induction
Lucas Tian, Kevin Ellis, Marta Kryven et al.
Learning Affordance Landscapes for Interaction Exploration in 3D Environments
Tushar Nagarajan, Kristen Grauman
Learning Agent Representations for Ice Hockey
Guiliang Liu, Oliver Schulte, Pascal Poupart et al.
Learning Augmented Energy Minimization via Speed Scaling
Etienne Bamas, Andreas Maggiori, Lars Rohwedder et al.
Learning Black-Box Attackers with Transferable Priors and Query Feedback
Jiancheng YANG, Yangzhou Jiang, Xiaoyang Huang et al.
Learning Bounds for Risk-sensitive Learning
Jaeho Lee, Sejun Park, Jinwoo Shin
Learning by Minimizing the Sum of Ranked Range
Shu Hu, Yiming Ying, xin wang et al.
Learning Causal Effects via Weighted Empirical Risk Minimization
Yonghan Jung, Jin Tian, Elias Bareinboim
Learning Certified Individually Fair Representations
Anian Ruoss, Mislav Balunovic, Marc Fischer et al.
Learning Composable Energy Surrogates for PDE Order Reduction
Alex Beatson, Jordan Ash, Geoffrey Roeder et al.
Learning compositional functions via multiplicative weight updates
Jeremy Bernstein, Jiawei Zhao, Markus Meister et al.
Learning Compositional Rules via Neural Program Synthesis
Maxwell Nye, Armando Solar-Lezama, Josh Tenenbaum et al.
Learning Continuous System Dynamics from Irregularly-Sampled Partial Observations
Zijie Huang, Yizhou Sun, Wei Wang
Learning Deep Attribution Priors Based On Prior Knowledge
Ethan Weinberger, Joseph Janizek, Su-In Lee
Learning Deformable Tetrahedral Meshes for 3D Reconstruction
Jun Gao, Wenzheng Chen, Tommy Xiang et al.
Learning Differentiable Programs with Admissible Neural Heuristics
Ameesh Shah, Eric Zhan, Jennifer Sun et al.
Learning Differential Equations that are Easy to Solve
Jacob Kelly, Jesse Bettencourt, Matthew J Johnson et al.
Learning discrete distributions: user vs item-level privacy
Yuhan Liu, Ananda Theertha Suresh, Felix Xinnan X Yu et al.
Learning discrete distributions with infinite support
Doron Cohen, Aryeh Kontorovich, Geoffrey Wolfer
Learning Discrete Energy-based Models via Auxiliary-variable Local Exploration
Hanjun Dai, Rishabh Singh, Bo Dai et al.
Learning Disentangled Representations and Group Structure of Dynamical Environments
Robin Quessard, Thomas Barrett, William Clements