Fix link to qEHVI paper in readme and tutorial (#471)

Summary: Pull Request resolved: #471

Differential Revision: D22391017

Pulled By: Balandat

fbshipit-source-id: bf137d9201549bf9baf3119aba46f5128a416a56

Author: Balandat

README.md

@@ -162,7 +162,7 @@ If you use BoTorch, please cite the following paper:
 ```
 
 If you use q-Expected Hypervolume Improvement, q-ParEGO, or analytic Expected Hypervolume Improvement with auto-differentiation, please also cite the following paper:
-> [S. Daulton, M. Balandat, E. Bakshy. Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization. ArXiv e-prints, 2020.](http://arxiv.org/abs/1910.06403)
+> [S. Daulton, M. Balandat, E. Bakshy. Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization. ArXiv e-prints, 2020.](https://arxiv.org/abs/2006.05078)
 
 ```
 @article{daulton2020differentiable,

tutorials/multi_objective_bo.ipynb

@@ -18,7 +18,7 @@
         "\n",
         "Since botorch assumes a maximization of all objectives, we seek to find the pareto frontier, the set of optimal trade-offs where improving one metric means deteriorating another.\n",
         "\n",
-        "[1] [Samuel Daulton, Maximillian Balandat, Eytan Bakshy. Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization. ArXiv e-prints, 2020.](http://arxiv.org/abs/1910.06403)"
+        "[1] [Samuel Daulton, Maximillian Balandat, Eytan Bakshy. Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization. ArXiv e-prints, 2020.](https://arxiv.org/abs/2006.05078)"
       ]
     },
     {