Asia
Improved Bayesian Regret Bounds for Thompson Sampling in Reinforcement Learning
This work was supported in part by the National Science Foundation under grant CCF-2149588 and Cisco, Inc.
Interior-Point Methods Strike Back: Solving the Wasserstein Barycenter Problem
DongDong Ge, Haoyue Wang, Zikai Xiong, Yinyu Ye
Neural Information Processing Systems http://nips.cc/
Self-Supervised Normalization for Non-autoregressive Speech-to-speech Translation
Non-autoregressive Transformers (NA Ts) are recently applied in direct speech-to-speech translation systems, which convert speech across different languages without intermediate text data.
Parameter-freeDynamicGraphEmbedding forLinkPrediction
Dynamic interaction graphs havebeenwidely adopted tomodeltheevolution of user-item interactions over time.
Reward is Enough for Convex MDPs
Maximising a cumulative reward function that is Markov and stationary, i.e., defined over state-action pairs and independent of time, is sufficient to capture many
Concentration of risk measures: A Wasserstein distance approach
Sanjay P. Bhat, Prashanth L.A.
Using a different proof technique, the results are extended to the class of spectral risk measures having a bounded risk spectrum.