LIONS @ NeurIPS 2022

Below a list of the accepted papers:

• Identifiability and generalizability from multiple experts in Inverse Reinforcement Learning by Paul Rolland, Luca Viano, Norman Schuerhoff, Boris Nikolov, Volkan Cevher
• Understanding Deep Neural Function Approximation in Reinforcement Learning via ε-Greedy Exploration by Fanghui Liu, Luca Viano, Volkan Cevher
• No-regret learning in games with noisy feedback: Faster rates and adaptivity via learning rate separation by Yu-Guan Hsieh, Kimon Antonakopoulos, Volkan Cevher, Panayotis Mertikopoulos
• On the Double Descent of Random Features Models Trained with SGDby Fanghui Liu, Johan Suykens, Volkan Cevher
• Extra Newton: A First Approach to Noise-Adaptive Accelerated Second-Order Methodsby Kimon Antonakopoulos, Ali Kavis, Volkan Cevher
• Robustness in deep learning: The good (width), the bad (depth), and the ugly (initialization)by Zhenyu Zhu, Fanghui Liu, Grigorios Chrysos, Volkan Cevher
• Sound and Complete Verification of Polynomial Networksby Elias Abad Rocamora, Mehmet Fatih Sahin, Fanghui Liu, Grigorios Chrysos, Volkan Cevher
• Proximal Point Imitation Learning by Luca Viano, Angeliki Kamoutsi, Gergely Neu, Igor Krawczuk, Volkan Cevher
• Extrapolation and Spectral Bias of Neural Nets with Hadamard Product: a Polynomial Net Study by Yongtao Wu, Zhenyu Zhu, Fanghui Liu, Grigorios Chrysos, Volkan Cevher
• Generalization Properties of NAS under Activation and Skip Connection Searchby Zhenyu Zhu, Fanghui Liu, Grigorios Chrysos, Volkan Cevher
• Adaptive Stochastic Variance Reduction for Non-convex Finite-Sum Minimizationby Ali Kavis, Efstration Panteleimon Skoulakis, Kimon Antonakopoulos, Leello Tadesse Dadi, Volkan Cevher
• Beyond Time-Average Convergence: Near-Optimal Uncoupled Online Learning via Clairvoyant Multiplicative Weights Updateby Georgios Piliouras, Ryan Simm, Stratis Skoulakis