Education
Online Reciprocal Recommendation with Theoretical Performance Guarantees
Claudio Gentile, Nikos Parotsidis, Fabio Vitale
Neural Information Processing Systems http://nips.cc/
HIQL: Offline Goal-Conditioned RL with Latent States as Actions Seohong Park
We observe that part of this difficulty stems from the "signal-to-noise"
Efficient Online Portfolio with Logarithmic Regret
Haipeng Luo, Chen-Yu Wei, Kai Zheng
Neural Information Processing Systems http://nips.cc/
Adaptive Methods for Nonconvex Optimization
Manzil Zaheer, Sashank Reddi, Devendra Sachan, Satyen Kale, Sanjiv Kumar
The first prominent algorithms in this line of research isADAGRAD [7,22], which uses a per-dimension learning rate based on squared pastgradients.ADAGRADachievedsignificant performance gainsincomparison toSGDwhenthe gradientsaresparse.
Online Control with Adversarial Disturbance for Continuous-time Linear Systems
A major challenge in robotics is to deploy simulated controllers into real-world.
No Pressure! Addressing the Problem of Local Minima in Manifold Learning Algorithms
Neural Information Processing Systems http://nips.cc/
Reward learning from human preferences and demonstrations in Atari
Borja Ibarz, Jan Leike, Tobias Pohlen, Geoffrey Irving, Shane Legg, Dario Amodei
Neural Information Processing Systems http://nips.cc/
Counterfactual Fairness by Combining Factual and Counterfactual Predictions
Counterfactual Fairness (CF), which posits that an ML model's outcome on any individual should remain unchanged if they had belonged to a different demographic