The accepted papers for COLT 2013 have just been revealed. You can also find the list below with a link to the arxiv version when I could find one (if you want me to add a link just send me an email).
I also take the opportunity of this post to remind my student readers that you can apply for a ‘student travel award’ to come participate in COLT 2013. The instructions can be found here. So far I received very few applications so I highly encourage you to give it a try! You can check out this blog post that gives a few reasons to make the effort to travel and participate in conferences.
Update with a message from Philippe Rigollet: Sebastien Bubeck and I are local organizers of this year’s edition of the Conference On Learning Theory (COLT 2013) on June 12-14.
This means that it will take place at Princeton (in Friend 101), and of course, that it’ll be awesome.
The list of accepted papers is very exciting and has been posted on the official COLT 2013 website:
http://orfe.princeton.edu/conferences/colt2013 (see also below).
Thanks to generous sponsors, we have been able to lower the registration costs to a historic minimum (regular: $300, students: $150).
These costs include:
-a cocktail party followed by a banquet on Thursday night
-coffee breaks (with poster presentations)
-lunches (with poster presentation)
-47 amazing presentations
-3 Keynote lectures by Sanjeev Arora (Princeton), Ralf Herbrich (amazon.com) and Yann LeCun (NYU) respectively
The deadline for early registration is May 13 (even if you’re local, help us support the conference by registering and not just crashing the party).
To register, go to:
Please help us spread the word by forwarding this email to anyone interested in Learning Theory and by using your favorite social media.
COLT 2013 accepted papers
- Ittai Abraham, Omar Alonso, Vasilis Kandylas and Aleksandrs Slivkins. Adaptive Crowdsourcing Algorithms for the Bandit Survey Problem
- Jayadev Acharya, Ashkan Jafarpour, Alon Orlitsky and Ananda Theertha Suresh. Optimal Probability Estimation with Applications to Prediction and Classification
- Shivani Agarwal. Surrogate Regret Bounds for the Area Under the ROC Curve via Strongly Proper Losses
- Animashree Anandkumar, Rong Ge, Daniel Hsu and Sham Kakade. A Tensor Spectral Approach to Learning Mixed Membership Community Models
- Oren Anava, Elad Hazan, Shie Mannor and Ohad Shamir. Online Learning for Time Series Prediction
- Joseph Anderson, Navin Goyal and Luis Rademacher. Efficient Learning of Simplices
- Lachlan Andrew, Siddharth Barman, Katrina Ligett, Minghong Lin, Adam Meyerson, Alan Roytman and Adam Wierman. A Tale of Two Metrics: Simultaneous Bounds on Competitiveness and Regret
- Pranjal Awasthi, Vitaly Feldman and Varun Kanade. Learning Using Local Membership Queries
- Francis Bach. Sharp analysis of low-rank kernel matrix approximations
- Maria-Florina Balcan and Phil Long. Active and passive learning of linear separators under log-concave distributions
- Peter Bartlett, Peter Grunwald, Peter Harremoes, Fares Hedayati and Wojciech Kotlowski. Horizon-Independent Optimal Prediction with Log-Loss in Exponential Families
- Gabor Bartok. A near-optimal algorithm for finite partial-monitoring games against adversarial opponents
- Mikhail Belkin, Luis Rademacher and James Voss. Blind Signal Separation in the Presence of Gaussian Noise
- Andrey Bernstein, Shie Mannor and Nahum Shimkin. Opportunistic Strategies for Generalized No-Regret Problems
- Quentin Berthet and Philippe Rigollet. Complexity Theoretic Lower Bounds for Sparse Principal Component Detection
- Sébastien Bubeck, Vianney Perchet and Philippe Rigollet. Bounded regret in stochastic multi-armed bandits
- Chao-Kai Chiang, Chia-Jung Lee and Chi-Jen Lu. Beating Bandits in Gradually Evolving Worlds
- Sung-Soon Choi. Polynomial Time Optimal Query Algorithms for Finding Graphs with Arbitrary Real Weights
- Amit Daniely and Tom Helbertal. The price of bandit information in multiclass online classification
- Sanjoy Dasgupta and Kaushik Sinha. Randomized partition trees for exact nearest neighbor search
- Luc Devroye, Gábor Lugosi and Gergely Neu. Prediction by random-walk perturbation
- Vitaly Feldman, Pravesh Kothari and Jan Vondrak. Representation, Approximation and Learning of Submodular Functions Using Low-rank Decision Trees
- Claudio Gentile, Mark Herbster and Stephen Pasteris. Online Similarity Prediction of Networked Data from Known and Unknown Graphs
- Eyal Gofer, Nicolò Cesa-Bianchi, Claudio Gentile and Yishay Mansour. Regret Minimization for Branching Experts
- Abhradeep Guha Thakurta and Adam Smith. Differentially Private Feature Selection via Stability Arguments, and the Robustness of the Lasso
- Wei Han, Alexander Rakhlin and Karthik Sridharan. Competing With Strategies
- Moritz Hardt and Ankur Moitra. Algorithms and Hardness for Robust Subspace Recovery
- Marcus Hutter. Sparse Adaptive Dirichlet-Like Process Redundancy Bounds
- Daniel Kane, Adam Klivans and Raghu Meka. Learning Halfspaces Under Log-Concave Densities: Polynomial Approximations and Moment Matching
- Emilie Kaufmann and Shivaram Kalyanakrishnan. Information Complexity in Bandit Subset Selection
- Wouter M. Koolen, Nie Jiazhong and Manfred Warmuth. Learning a set of directions
- Cheng Li, Wenxin Jiang and Martin Tanner. General Oracle Inequalities for Gibbs Posterior with Application to Classification and Ranking
- Roi Livni and Pierre Simon. Honest Compressions and Their Application to Compression Schemes
- Mehrdad Mahdavi and Rong Jin. Passive Learning with Target Risk
- Andreas Maurer and Massimiliano Pontil. Excess risk bounds for multitask learning with trace norm regularization
- Stanislav Minsker. Estimation of Extreme Values and Associated Level Sets of a Regression Function via Selective Sampling
- Vianney Perchet and Shie Mannor. Approachability, fast and slow
- Alexander Rakhlin and Karthik Sridharan. Online Learning with Predictable Sequences
- Clayton Scott, Gilles Blanchard and Gregory Handy. Classification with Asymmetric Label Noise: Consistency and Maximal Denoising
- Ohad Shamir. On the Complexity of Bandit and Derivative-Free Stochastic Convex Optimization
- Matus Telgarsky. Boosting with the Logistic Loss is Consistent
- Ruth Urner, Sharon Wulff and Shai Ben-David. PLAL: Cluster-based active learning
- Robert Vandermeulen and Clayton Scott. Consistency of Robust Kernel Density Estimators
- Yining Wang, Liwei Wang, Yuanzhi Li, Di He and Tie-Yan Liu. A Theoretical Analysis of NDCG Type Ranking Measures
- David Woodruff and Qin Zhang. Subspace Embeddings and $\ell_p$-Regression Using Exponential Random Variables
- Lijun Zhang, Mehrdad Mahdavi, Rong Jin, Tianbao Yang and Shenghuo Zhu. Recovering Optimal Solution by Dual Random Projection
- Yuchen Zhang, John Duchi and Martin Wainwright. Divide and Conquer Kernel Ridge Regression