Papers
Leveraging Approximate Symbolic Models for Reinforcement Learning via Skill Diversity
Lin Guan, Sarath Sreedharan, Subbarao Kambhampati
LIDL: Local Intrinsic Dimension Estimation Using Approximate Likelihood
Piotr Tempczyk, Rafał Michaluk, Lukasz Garncarek et al.
Lie Point Symmetry Data Augmentation for Neural PDE Solvers
Johannes Brandstetter, Max Welling, Daniel E Worrall
Lightweight Projective Derivative Codes for Compressed Asynchronous Gradient Descent
Pedro J Soto, Ilia Ilmer, Haibin Guan et al.
LIMO: Latent Inceptionism for Targeted Molecule Generation
Peter Eckmann, Kunyang Sun, Bo Zhao et al.
Linear Adversarial Concept Erasure
Shauli Ravfogel, Michael Twiton, Yoav Goldberg et al.
Linear Bandit Algorithms with Sublinear Time Complexity
Shuo Yang, Tongzheng Ren, Sanjay Shakkottai et al.
Linear Complexity Randomized Self-attention Mechanism
Lin Zheng, Chong Wang, Lingpeng Kong
Linearity Grafting: Relaxed Neuron Pruning Helps Certifiable Robustness
Tianlong Chen, Huan Zhang, Zhenyu Zhang et al.
Linear-Time Gromov Wasserstein Distances using Low Rank Couplings and Costs
Meyer Scetbon, Gabriel Peyré, Marco Cuturi
Local Augmentation for Graph Neural Networks
Songtao Liu, Rex Ying, Hanze Dong et al.
Local Linear Convergence of Douglas-Rachford for Linear Programming: a Probabilistic Analysis
Oisin Faust, Hamza Fawzi
Locally Sparse Neural Networks for Tabular Biomedical Data
Junchen Yang, Ofir Lindenbaum, Yuval Kluger
Log-Euclidean Signatures for Intrinsic Distances Between Unaligned Datasets
Tal Shnitzer, Mikhail Yurochkin, Kristjan Greenewald et al.
Loss Function Learning for Domain Generalization by Implicit Gradient
Boyan Gao, Henry Gouk, Yongxin Yang et al.
Low-Complexity Deep Convolutional Neural Networks on Fully Homomorphic Encryption Using Multiplexed Parallel Convolutions
Eunsang Lee, Joon-Woo Lee, Junghyun Lee et al.
Low-Precision Stochastic Gradient Langevin Dynamics
Ruqi Zhang, Andrew Gordon Wilson, Christopher De Sa
LSB: Local Self-Balancing MCMC in Discrete Spaces
Emanuele Sansone
LyaNet: A Lyapunov Framework for Training Neural ODEs
Ivan Dario Jimenez Rodriguez, Aaron Ames, Yisong Yue
Lyapunov Density Models: Constraining Distribution Shift in Learning-Based Control
Katie Kang, Paula Gradu, Jason J Choi et al.
MAE-DET: Revisiting Maximum Entropy Principle in Zero-Shot NAS for Efficient Object Detection
Zhenhong Sun, Ming Lin, Xiuyu Sun et al.
Making Linear MDPs Practical via Contrastive Representation Learning
Tianjun Zhang, Tongzheng Ren, Mengjiao Yang et al.
MAML and ANIL Provably Learn Representations
Liam Collins, Aryan Mokhtari, Sewoong Oh et al.
Marginal Distribution Adaptation for Discrete Sets via Module-Oriented Divergence Minimization
Hanjun Dai, Mengjiao Yang, Yuan Xue et al.
Marginal Tail-Adaptive Normalizing Flows
Mike Laszkiewicz, Johannes Lederer, Asja Fischer