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Why so gloomy? A Bayesian explanation of human pessimism bias in the multi-armed bandit task
Neural Information Processing Systems http://nips.cc/
Memory Augmented Policy Optimization for Program Synthesis and Semantic Parsing
Chen Liang, Mohammad Norouzi, Jonathan Berant, Quoc V. Le, Ni Lao
MAPO improves the sample efficiency and robustness of policy gradient, especially on tasks with sparse rewards.
Communication Efficient Parallel Algorithms for Optimization on Manifolds
However, the existing literature on parallel inference almost exclusively focuses on Euclidean data and parameters.
Incorporating Context into Language Encoding Models for fMRI
Language encoding models help explain language processing in the human brain by learning functions that predict brain responses from the language stimuli that elicited them.
Optimization over Continuous and Multi-dimensional Decisions with Observational Data
Dimitris Bertsimas, Christopher McCord
We consider the optimization of an uncertain objective over continuous and multidimensional decision spaces in problems in which we are only provided with observational data.
Online Structured Laplace Approximations for Overcoming Catastrophic Forgetting
Creating an agent that performs well across multiple tasks and continuously incorporates new knowledge has been a longstanding goal of research on artificial intelligence.
Fast and Effective Robustness Certification
Gagandeep Singh, Timon Gehr, Matthew Mirman, Markus Püschel, Martin Vechev
These benefits are due to carefully designed approximations tailored to the setting of neural networks.
Proximal Graphical Event Models
Debarun Bhattacharjya, Dharmashankar Subramanian, Tian Gao
Event datasets involve irregular occurrences of events over the timeline and are prevalent in numerous domains.
Differentially Private Change-Point Detection
Rachel Cummings, Sara Krehbiel, Yajun Mei, Rui Tuo, Wanrong Zhang
Neural Information Processing Systems http://nips.cc/
Embedding Logical Queries on Knowledge Graphs
Will Hamilton, Payal Bajaj, Marinka Zitnik, Dan Jurafsky, Jure Leskovec
Learning low-dimensional embeddings of knowledge graphs is a powerful approach used to predict unobserved or missing edges between entities.