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13 Results

Poster
Mon 9:00 Effective Distributed Learning with Random Features: Improved Bounds and Algorithms
Yong Liu, Jiankun Liu, Shuqiang Wang
Spotlight
Mon 12:05 Generalization bounds via distillation
Daniel Hsu, Ziwei Ji, Matus Telgarsky, Lan Wang
Poster
Tue 1:00 Learning Better Structured Representations Using Low-rank Adaptive Label Smoothing
Asish Ghoshal, Xilun Chen, Sonal Gupta, Luke Zettlemoyer, Yashar Mehdad
Poster
Tue 9:00 Sharper Generalization Bounds for Learning with Gradient-dominated Objective Functions
Yunwen Lei, Yiming Ying
Poster
Tue 9:00 Learning Robust State Abstractions for Hidden-Parameter Block MDPs
Amy Zhang, Shagun Sodhani, Khimya Khetarpal, Joelle Pineau
Poster
Tue 17:00 Knowledge Distillation as Semiparametric Inference
Tri Dao, Govinda Kamath, Vasilis Syrgkanis, Lester Mackey
Poster
Wed 9:00 For self-supervised learning, Rationality implies generalization, provably
Yamini Bansal, Gal Kaplun, Boaz Barak
Poster
Wed 17:00 A PAC-Bayesian Approach to Generalization Bounds for Graph Neural Networks
Renjie Liao, Raquel Urtasun, Richard Zemel
Poster
Wed 17:00 Estimating Lipschitz constants of monotone deep equilibrium models
Chirag Pabbaraju, Ezra Winston, Zico Kolter
Poster
Thu 9:00 Heating up decision boundaries: isocapacitory saturation, adversarial scenarios and generalization bounds
Bogdan Georgiev, Lukas Franken, Mayukh Mukherjee
Poster
Thu 17:00 Theoretical Analysis of Self-Training with Deep Networks on Unlabeled Data
Colin Wei, Kendrick Shen, Yining Chen, Tengyu Ma
Poster
Thu 17:00 Generalization bounds via distillation
Daniel Hsu, Ziwei Ji, Matus Telgarsky, Lan Wang
Oral
Thu 19:00 Theoretical Analysis of Self-Training with Deep Networks on Unlabeled Data
Colin Wei, Kendrick Shen, Yining Chen, Tengyu Ma