List of Accepted Papers

There were 74 submissions for ALT 2017 and out of these, 33 papers were accepted. The accepted papers are the following.
  1. Phil Long. New bounds on the price of bandit feedback for mistake-bounded online multiclass learning.
  2. Henry Reeve and Gavin Brown. Minimax rates on manifolds with approximate nearest neighbours.
  3. Daniil Ryabko. Universality of Bayesian mixture predictors
  4. Fahimeh Bayeh, Ziyuan Gao and Sandra Zilles. Erasing Pattern Languages Distinguishable by a Finite Number of Strings.
  5. Nader Bshouty, Nuha Diab, Shada R. Kawar and Robert J. Shahla. Non-Adaptive Randomized Algorithm for Group Testing.
  6. Rupert Hölzl, Sanjay Jain, Philipp Schlicht, Karen Seidel and Frank Stephan. Automatic Learning from Repetitive Texts.
  7. Odalric-Ambrym Maillard. Boundary Crossing Probabilities for General Exponential Families.
  8. Ziyuan Gao, David Kirkpatrick, Christoph Ries, Hans Simon and Sandra Zilles. Preference-based Teaching of Unions of Geometric Objects.
  9. Vadim Lozin, Igor Razgon, Victor Zamaraev, Elena Zamaraeva and Nikolai Zolotykh. Specifying a positive threshold function via extremal points.
  10. Pierre Menard and Aurélien Garivier. A minimax and asymptotically optimal algorithm for stochastic bandits.
  11. Mano Vikash Janardhanan. Graph Verification with a Betweenness Oracle.
  12. Maria-Florina Balcan, Avrim Blum and Vaishnavh Nagarajan. Lifelong Learning in Costly Feature Spaces.
  13. Jungseul Ok, Se-Young Yun, Alexandre Proutiere and Rami Mochaourab. Collaborative Clustering: Sample Complexity and Efficient Algorithms.
  14. Arushi Gupta and Daniel Hsu. Parameter identification in Markov chain choice models.
  15. Danielle Ensign, Scott Neville, Arnab Paul and Suresh Venkatasubramanian. The Complexity of Explaining Neural Networks Through (group) Invariants.
  16. Hayato Mizumoto, Shota Todoroki, Diptarama, Ryo Yoshinaka and Ayumi Shinohara. An efficient query learning algorithm for zero-suppressed binary decision diagrams.
  17. Laurent Orseau, Tor Lattimore and Shane Legg. Soft-Bayes: Prod for Mixtures of Experts with Log-Loss.
  18. Daniil Ryabko. Hypothesis testing on infinite random graphs.
  19. Wojciech Kotlowski. Scale-Invariant Unconstrained Online Learning.
  20. Martin Grohe, Christof Löding and Martin Ritzert. Learning MSO-definable hypotheses on strings.
  21. Dana Angluin and Tyler Dohrn. The Power of Random Counterexamples.
  22. Niklas Thiemann, Christian Igel, Olivier Wintenberger and Yevgeny Seldin. A Strongly Quasiconvex PAC-Bayesian Bound.
  23. Timo Kötzing, Martin Schirneck and Karen Seidel. Normal Forms in Semantic Language Identification.
  24. Jaouad Mourtada and Odalric-Ambrym Maillard. Efficient tracking of a growing number of experts.
  25. Vitaly Feldman, Pravesh K Kothari and Jan Vondrak. Tight Bounds on ℓ1 Approximation and Learning of Self-Bounding Functions.
  26. Di Chen and Jeff Phillips. Relative Error Embeddings of the Gaussian Kernel Distance.
  27. Alan Fern, Robby Goetschalckx, Mandana Hamidi-Haines and Prasad Tadepalli. Adaptive Submodularity with Varying Query Sets: An Application to Active Multi-label Learning.
  28. Mohammad Mahdi Ajallooeian, Ruitong Huang, Csaba Szepesvári and Martin Müller. Structured Best Arm Identification with Fixed Confidence.
  29. Ata Kaban. On Compressive Ensemble Induced Regularisation: How Close is the Finite Ensemble Precision Matrix from the Infinite Ensemble?
  30. Vitaly Feldman. Dealing with Range Anxiety in Mean Estimation via Statistical Queries.
  31. Yuyi Wang, Zheng-Chu Guo and Jan Ramon. Learning from networked examples.
  32. Ning Ding, Yanli Ren and Dawu Gu. PAC Learning Depth-3 AC0 Circuits of Bounded Top Fanin.
  33. Pooria Joulani, Andras Gyorgy and Csaba Szepesvári. A Modular Analysis of Adaptive (Non-)Convex Optimization: Optimism, Composite Objectives, and Variational Bounds.


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