Papers
Matrix Inference and Estimation in Multi-Layer Models
Parthe Pandit, Mojtaba Sahraee Ardakan, Sundeep Rangan et al.
PLANS: Neuro-Symbolic Program Learning from Videos
Raphaël Dang-Nhu
Multiparameter Persistence Image for Topological Machine Learning
Mathieu Carrière, Andrew Blumberg
De-Anonymizing Text by Fingerprinting Language Generation
Zhen Sun, Roei Schuster, Vitaly Shmatikov
Faster DBSCAN via subsampled similarity queries
Heinrich Jiang, Jennifer Jang, Jakub Lacki
Learning to Decode: Reinforcement Learning for Decoding of Sparse Graph-Based Channel Codes
Salman Habib, Allison Beemer, Joerg Kliewer
Risk-Sensitive Reinforcement Learning: Near-Optimal Risk-Sample Tradeoff in Regret
Yingjie Fei, Zhuoran Yang, Yudong Chen et al.
Firefly Neural Architecture Descent: a General Approach for Growing Neural Networks
Lemeng Wu, Bo Liu, Peter Stone et al.
Scalable Belief Propagation via Relaxed Scheduling
Vitalii Aksenov, Dan Alistarh, Janne H. Korhonen
Synthesizing Tasks for Block-based Programming
Umair Ahmed, Maria Christakis, Aleksandr Efremov et al.
Learning to Adapt to Evolving Domains
Hong Liu, Mingsheng Long, Jianmin Wang et al.
Agnostic Q-learning with Function Approximation in Deterministic Systems: Near-Optimal Bounds on Approximation Error and Sample Complexity
Simon S Du, Jason Lee, Gaurav Mahajan et al.
Follow the Perturbed Leader: Optimism and Fast Parallel Algorithms for Smooth Minimax Games
Arun Suggala, Praneeth Netrapalli
Model Class Reliance for Random Forests
Gavin Smith, Roberto Mansilla, James Goulding
Confounding-Robust Policy Evaluation in Infinite-Horizon Reinforcement Learning
Nathan Kallus, Angela Zhou
Self-Adaptively Learning to Demoiré from Focused and Defocused Image Pairs
Lin Liu, Shanxin Yuan, Jianzhuang Liu et al.
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization
Geoff Pleiss, Martin Jankowiak, David Eriksson et al.
Learning from Label Proportions: A Mutual Contamination Framework
Clayton Scott, Jianxin Zhang
Big Self-Supervised Models are Strong Semi-Supervised Learners
Ting Chen, Simon Kornblith, Kevin Swersky et al.
Measuring Systematic Generalization in Neural Proof Generation with Transformers
Nicolas Gontier, Koustuv Sinha, Siva Reddy et al.
A Dynamical Central Limit Theorem for Shallow Neural Networks
Zhengdao Chen, Grant Rotskoff, Joan Bruna et al.
Auditing Differentially Private Machine Learning: How Private is Private SGD?
Matthew Jagielski, Jonathan Ullman, Alina Oprea
Focus of Attention Improves Information Transfer in Visual Features
Matteo Tiezzi, Stefano Melacci, Alessandro Betti et al.
Implicit Bias in Deep Linear Classification: Initialization Scale vs Training Accuracy
Edward Moroshko, Blake E Woodworth, Suriya Gunasekar et al.
MuSCLE: Multi Sweep Compression of LiDAR using Deep Entropy Models
Sourav Biswas, Jerry Liu, Kelvin Wong et al.