Country
Learning and Testing Causal Models with Interventions
Jayadev Acharya, Arnab Bhattacharyya, Constantinos Daskalakis, Saravanan Kandasamy
Neural Information Processing Systems http://nips.cc/
Learning Latent Process from High-Dimensional Event Sequences via Efficient Sampling
Qitian Wu, Zixuan Zhang, Xiaofeng Gao, Junchi Yan, Guihai Chen
There are plenty of previous studies targeting the problem from different aspects. For temporal point process, agreat number of works [3, 13, 15, 16, 28] attempt to model the intensify function from statistic views, and recent studies harness deep recurrent model [24], generative adversarial network [23] and reinforcement learning [19, 18] to learn the temporal process. These researches mainly focus on one-dimension eventsequences where eacheventpossesses thesame marker.
Revisiting $(\epsilon, \gamma, \tau)$-similarity learning for domain adaptation
Neural Information Processing Systems http://nips.cc/
The Limits of Post-Selection Generalization
Jonathan Ullman, Adam Smith, Kobbi Nissim, Uri Stemmer, Thomas Steinke
A recent line of work initiated by Dworket al. [9] posed the question: Can we designgeneralpurpose algorithms for ensuring generalization in the presence of post-selection?
a1e865a9b1065392ed6035d8ccd072d9-Paper.pdf
Unfortunately,the per-iteration cost of maintaining this adaptivedistribution for gradient estimation is more than calculating the full gradient itself, which we call the chicken-and-the-egg loop. As a result, the false impression of faster convergence in iterations, inreality,leads to slower convergence in time.
Combinatorial Group Testing with Selfish Agents
We study the Combinatorial Group Testing (CGT) problems in a novel game-theoretic framework, with a solution concept of Adversarial Equilibrium (AE).
