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Adaptive Concentration Inequalities for Sequential Decision Problems
Shengjia Zhao, Enze Zhou, Ashish Sabharwal, and Stefano ErmonNIPS • 2016 A key challenge in sequential decision problems is to determine how many samples are needed for an agent to make reliable decisions with good probabilistic guarantees. We introduce Hoeffding-like concentration inequalities that hold for a random, adaptively…Beyond Parity Constraints: Fourier Analysis of Hash Functions for Inference
Tudor Achim, Ashish Sabharwal, and Stefano ErmonICML • 2016 Random projections have played an important role in scaling up machine learning and data mining algorithms. Recently they have also been applied to probabilistic inference to estimate properties of high-dimensional distributions; however , they all rely on…Creating Causal Embeddings for Question Answering with Minimal Supervision
Rebecca Sharp, Mihai Surdeanu, Peter Jansen, and Peter ClarkEMNLP • 2016 A common model for question answering (QA) is that a good answer is one that is closely related to the question, where relatedness is often determined using generalpurpose lexical models such as word embeddings. We argue that a better approach is to look for…Cross-Sentence Inference for Process Knowledge
Samuel Louvan, Chetan Naik, Sadhana Kumaravel, Heeyoung Kwon, Niranjan Balasubramanian, and Peter ClarkEMNLP • 2016 For AI systems to reason about real world situations, they need to recognize which processes are at play and which entities play key roles in them. Our goal is to extract this kind of rolebased knowledge about processes, from multiple sentence-level…Examples are not enough. Learn to criticize! Criticism for Interpretability
Been Kim, Sanmi Koyejo and Rajiv KhannaNIPS • 2016 Example-based explanations are widely used in the effort to improve the interpretability of highly complex distributions. However, prototypes alone are rarely sufficient to represent the gist of the complexity. In order for users to construct better mental…IKE - An Interactive Tool for Knowledge Extraction
Bhavana Dalvi, Sumithra Bhakthavatsalam, Chris Clark, Peter Clark, Oren Etzioni, Anthony Fader, and Dirk GroeneveldAKBC • 2016 Recent work on information extraction has suggested that fast, interactive tools can be highly effective; however, creating a usable system is challenging, and few publicly available tools exist. In this paper we present IKE, a new extraction tool that…Instructable Intelligent Personal Agent
Amos Azaria, Jayant Krishnamurthy, and Tom M. MitchellAAAI • 2016 Unlike traditional machine learning methods, humans often learn from natural language instruction. As users become increasingly accustomed to interacting with mobile devices using speech, their interest in instructing these devices in natural language is…Metaphor as a Medium for Emotion: An Empirical Study
Saif M. Mohammad, Ekaterina Shutova, and Peter D. TurneySEM • 2016 It is generally believed that a metaphor tends to have a stronger emotional impact than a literal statement; however, there is no quantitative study establishing the extent to which this is true. Further, the mechanisms through which metaphors convey emotions…Moving Beyond the Turing Test with the Allen AI Science Challenge
Carissa Schoenick, Peter Clark, Oyvind Tafjord, Peter Turney, and Oren EtzioniCACM • 2016 The field of Artificial Intelligence has made great strides forward recently, for example AlphaGo's recent victory against the world champion Lee Sedol in the game of Go, leading to great optimism about the field. But are we really moving towards smarter…Probabilistic Models for Learning a Semantic Parser Lexicon
Jayant KrishnamurthyNAACL • 2016 We introduce several probabilistic models for learning the lexicon of a semantic parser. Lexicon learning is the first step of training a semantic parser for a new application domain and the quality of the learned lexicon significantly affects both the…