Industry
Adversarial Attacks on Stochastic Bandits
Kwang-Sung Jun, Lihong Li, Yuzhe Ma, Jerry Zhu
Neural Information Processing Systems http://nips.cc/
CausalStock: Deep End-to-end Causal Discovery for News-driven Stock Movement Prediction
On the one hand, "relation discovery" is a
Adversarial vulnerability for any classifier
Alhussein Fawzi, Hamza Fawzi, Omar Fawzi
Neural Information Processing Systems http://nips.cc/
The Download: an exclusive chat with Jim O'Neill, and the surprising truth about heists
The Download: an exclusive chat with Jim O'Neill, and the surprising truth about heists Over the past year, Jim O'Neill has become one of the most powerful people in public health. As the US deputy health secretary, he holds two roles at the top of the country's federal health and science agencies. He oversees a department with a budget of over a trillion dollars. And he signed the decision memorandum on the US's deeply controversial new vaccine schedule. In an exclusive interview with earlier this month, O'Neill described his plans to increase human healthspan through longevity-focused research supported by ARPA-H, a federal agency dedicated to biomedical breakthroughs. Fellow longevity enthusiasts said they hope he will bring attention and funding to their cause.
Cross-sectional Learning of Extremal Dependence among Financial Assets
Neural Information Processing Systems http://nips.cc/
Beauty-in-averageness and its contextual modulations: A Bayesian statistical account
Neural Information Processing Systems http://nips.cc/
(Nearly) Efficient Algorithms for the Graph Matching Problem on Correlated Random Graphs
Boaz Barak, Chi-Ning Chou, Zhixian Lei, Tselil Schramm, Yueqi Sheng
Wegivethe first efficient algorithms proven to succeed in the correlated Erdös-Rényi model (Pedarsani and Grossglauser, 2011). Specifically, we give apolynomial time algorithm for thegraphsimilarity/hypothesis testingtaskwhich worksforeveryconstant level of correlation between the two graphs that can be arbitrarily close to zero. We also give a quasi-polynomial (nO(logn) time) algorithm for thegraph matching task of recovering the permutation minimizing the symmetric difference in this model.