Artificial Intelligence
Pursuing computing advances to create intelligent machines that complement human reasoning to augment and enrich our experience and competencies.
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NIPS: Oral Session 9 – Adrian Weller
It was recently proved using graph covers (Ruozzi, 2012) that the Bethe partition function is upper bounded by the true partition function for a binary pairwise model that is attractive. Here we provide a new,…
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NIPS: Oral Session 9 – Cynthia Dwork
Privacy-preserving data analysis has a large literature that spans several disciplines. Many early attempts have proved problematic either in practice or on paper. A new approach, “differential privacy” — a notion tailored to situations in…
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NIPS: Spotlight Session 7: Reinforcement Learning Spotlights
B. Piot, M. Geist, O. Pietquin Difference of Convex Functions Programming for Reinforcement Learning S. Levine, P. Abbeel Learning Neural Network Policies with Guided Policy Search under Unknown Dynamics I. Osband, B. Van Roy Near-optimal…
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NIPS: Oral Session 7 – Odalric-Ambryn Maillard
In Reinforcement Learning (RL), state-of-the-art algorithms require a large number of samples per state-action pair to estimate the transition kernel p. In many problems, a good approximation of p is not needed. For instance, if…
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NIPS: Spotlight Session 6 – Learning Theory Spotlights
C. Zhang, K. Chaudhuri Beyond Disagreement-Based Agnostic Active Learning A. Sani, G. Neu, A. Lazaric Exploiting easy data in online optimization P. Awasthi, A. Blum, O. Sheffet, A. Vijayaraghavan Learning Mixtures of Ranking Models M.…
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NIPS: Oral Session 6 – Nishant A. Mehta
From Stochastic Mixability to Fast Rates Empirical risk minimization (ERM) is a fundamental learning rule for statistical learning problems where the data is generated according to some unknown distribution P and returns a hypothesis f…
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NIPS: Spotlight Session 8 – GP, Kernal, Sampling, and Classification Spotlights
G. Patrini, R. Nock, T. Caetano, P. Rivera (Almost) No Label No Cry O. Koyejo, N. Natarajan, P. Ravikumar, I. Dhillon Consistent Binary Classification with Generalized Performance Metrics D. Steinberg, E. Bonilla Extended and Unscented…
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NIPS: Oral Session 5 – John Carlos Baez
Networks in Climate Science The El Niño is a powerful but irregular climate cycle that has huge consequences for agriculture and perhaps global warming. Predicting its arrival more than 6 months ahead of time has…
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A Wild Bootstrap for Degenerate Kernel Tests
A wild bootstrap method for nonparametric hypothesis tests based on kernel distribution embeddings is proposed. This bootstrap method is used to construct provably consistent tests that apply to random processes, for which the naive permutation-based…
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NIPS: Oral Session 5 – Alexandros G. Dimakis
Sparse Polynomial Learning and Graph Sketching Let f:{−1,1}n→R be a polynomial with at most s non-zero real coefficients. We give an algorithm for exactly reconstructing f given random examples from the uniform distribution on {−1,1}n…
