Timezone: »
Panel
Panel 4A-2: Adaptively Exploiting d-Separators… & On the Complexity…
Blair Bilodeau · Ayush Sekhari
Author Information
Blair Bilodeau (University of Toronto)
Ayush Sekhari (Cornell University)
More from the Same Authors
-
2021 : Relaxing the I.I.D. Assumption: Adaptively Minimax Optimal Regret via Root-Entropic Regularization »
Blair Bilodeau · Jeffrey Negrea · Dan Roy -
2022 : Hybrid RL: Using both offline and online data can make RL efficient »
Yuda Song · Yifei Zhou · Ayush Sekhari · J. Bagnell · Akshay Krishnamurthy · Wen Sun -
2022 : Hidden Poison: Machine Unlearning Enables Camouflaged Poisoning Attacks »
Jimmy Di · Jack Douglas · Jayadev Acharya · Gautam Kamath · Ayush Sekhari -
2022 : Hidden Poison: Machine unlearning enables camouflaged poisoning attacks »
Jimmy Di · Jack Douglas · Jayadev Acharya · Gautam Kamath · Ayush Sekhari -
2022 Poster: Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems »
Masatoshi Uehara · Ayush Sekhari · Jason Lee · Nathan Kallus · Wen Sun -
2022 Poster: Adaptively Exploiting d-Separators with Causal Bandits »
Blair Bilodeau · Linbo Wang · Dan Roy -
2022 Poster: From Gradient Flow on Population Loss to Learning with Stochastic Gradient Descent »
Christopher De Sa · Satyen Kale · Jason Lee · Ayush Sekhari · Karthik Sridharan -
2022 Poster: On the Complexity of Adversarial Decision Making »
Dylan J Foster · Alexander Rakhlin · Ayush Sekhari · Karthik Sridharan -
2021 Poster: Minimax Optimal Quantile and Semi-Adversarial Regret via Root-Logarithmic Regularizers »
Jeffrey Negrea · Blair Bilodeau · Nicolò Campolongo · Francesco Orabona · Dan Roy