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ORCID25. COLT 2012: Edinburgh, Scotland, UK
- Shie Mannor, Nathan Srebro, Robert C. Williamson:
COLT 2012 - The 25th Annual Conference on Learning Theory, June 25-27, 2012, Edinburgh, Scotland. JMLR Proceedings 23, JMLR.org 2012 - Shie Mannor, Nathan Srebro:
Preface. 1.1-1.2 - Zohar Shay Karnin, Edo Liberty, Shachar Lovett, Roy Schwartz, Omri Weinstein:
Unsupervised SVMs: On the Complexity of the Furthest Hyperplane Problem. 2.1-2.17 - Elad Hazan, Sham M. Kakade:
(weak) Calibration is Computationally Hard. 3.1-3.10 - Maria-Florina Balcan, Florin Constantin, Satoru Iwata, Lei Wang:
Learning Valuation Functions. 4.1-4.24 - Niv Buchbinder, Shahar Chen, Joseph Naor, Ohad Shamir:
Unified Algorithms for Online Learning and Competitive Analysis. 5.1-5.18 - Chao-Kai Chiang, Tianbao Yang, Chia-Jung Lee, Mehrdad Mahdavi, Chi-Jen Lu, Rong Jin, Shenghuo Zhu:
Online Optimization with Gradual Variations. 6.1-6.20 - Satyen Kale:
Commentary on "Online Optimization with Gradual Variations". 6.21-6.24 - Fares Hedayati, Peter L. Bartlett:
The Optimality of Jeffreys Prior for Online Density Estimation and the Asymptotic Normality of Maximum Likelihood Estimators. 7.1-7.13 - Peter Grünwald:
Commentary on "The Optimality of Jeffreys Prior for Online Density Estimation and the Asymptotic Normality of Maximum Likelihood Estimators". 7.14-7.17 - Taiji Suzuki:
PAC-Bayesian Bound for Gaussian Process Regression and Multiple Kernel Additive Model. 8.1-8.20 - Daniel J. Hsu, Sham M. Kakade, Tong Zhang:
Random Design Analysis of Ridge Regression. 9.1-9.24 - Mark Rudelson, Shuheng Zhou:
Reconstruction from Anisotropic Random Measurements. 10.1-10.24 - Benjamin Recht, Christopher Ré:
Toward a Noncommutative Arithmetic-geometric Mean Inequality: Conjectures, Case-studies, and Consequences. 11.1-11.24 - John C. Duchi:
Commentary on "Toward a Noncommutative Arithmetic-geometric Mean Inequality: Conjectures, Case-studies, and Consequences". 11.25-11.27 - Adityanand Guntuboyina, Bodhisattva Sen:
L1 Covering Numbers for Uniformly Bounded Convex Functions. 12.1-12.13 - Yuyang Wang, Roni Khardon, Dmitry Pechyony, Rosie Jones:
Generalization Bounds for Online Learning Algorithms with Pairwise Loss Functions. 13.1-13.22 - Rocco A. Servedio, Li-Yang Tan, Justin Thaler:
Attribute-Efficient Learning and Weight-Degree Tradeoffs for Polynomial Threshold Functions. 14.1-14.19 - Parikshit Gopalan, Adam R. Klivans, Raghu Meka:
Learning Functions of Halfspaces using Prefix Covers. 15.1-15.10 - Vitaly Feldman, Varun Kanade:
Computational Bounds on Statistical Query Learning. 16.1-16.22 - Vitaly Feldman:
Learning DNF Expressions from Fourier Spectrum. 17.1-17.19 - Sanjoy Dasgupta:
Consistency of Nearest Neighbor Classification under Selective Sampling. 18.1-18.15 - Nir Ailon, Ron Begleiter, Esther Ezra:
Active Learning Using Smooth Relative Regret Approximations with Applications. 19.1-19.20 - Maria-Florina Balcan, Steve Hanneke:
Robust Interactive Learning. 20.1-20.34 - Mesrob I. Ohannessian, Munther A. Dahleh:
Rare Probability Estimation under Regularly Varying Heavy Tails. 21.1-21.24 - Jayadev Acharya, Hirakendu Das, Ashkan Jafarpour, Alon Orlitsky, Shengjun Pan, Ananda Theertha Suresh:
Competitive Classification and Closeness Testing. 22.1-22.18 - Magalie Fromont, Béatrice Laurent, Matthieu Lerasle, Patricia Reynaud-Bouret:
Kernels Based Tests with Non-asymptotic Bootstrap Approaches for Two-sample Problems. 23.1-23.23 - Prateek Jain, Pravesh Kothari, Abhradeep Thakurta:
Differentially Private Online Learning. 24.1-24.34 - Daniel Kifer, Adam D. Smith, Abhradeep Thakurta:
Private Convex Optimization for Empirical Risk Minimization with Applications to High-dimensional Regression. 25.1-25.40 - Maria-Florina Balcan, Avrim Blum, Shai Fine, Yishay Mansour:
Distributed Learning, Communication Complexity and Privacy. 26.1-26.22 - Jacob D. Abernethy, Rafael M. Frongillo:
A Characterization of Scoring Rules for Linear Properties. 27.1-27.13 - Dario García-García, Robert C. Williamson:
Divergences and Risks for Multiclass Experiments. 28.1-28.20 - Takafumi Kanamori, Akiko Takeda, Taiji Suzuki:
A Conjugate Property between Loss Functions and Uncertainty Sets in Classification Problems. 29.1-29.23 - David P. Helmbold, Philip M. Long:
New Bounds for Learning Intervals with Implications for Semi-Supervised Learning. 30.1-30.15 - Lisa Hellerstein, Devorah Kletenik, Linda Sellie, Rocco A. Servedio:
Tight Bounds on Proper Equivalence Query Learning of DNF. 31.1-31.18 - Nakul Verma:
Distance Preserving Embeddings for General n-Dimensional Manifolds. 32.1-32.28 - Animashree Anandkumar, Daniel J. Hsu, Sham M. Kakade:
A Method of Moments for Mixture Models and Hidden Markov Models. 33.1-33.34 - Nicolò Cesa-Bianchi, Claudio Gentile, Fabio Vitale, Giovanni Zappella:
A Correlation Clustering Approach to Link Classification in Signed Networks. 34.1-34.20 - Kamalika Chaudhuri, Fan Chung Graham, Alexander Tsiatas:
Spectral Clustering of Graphs with General Degrees in the Extended Planted Partition Model. 35.1-35.23 - Mikhail Belkin, Qichao Que, Yusu Wang, Xueyuan Zhou:
Toward Understanding Complex Spaces: Graph Laplacians on Manifolds with Singularities and Boundaries. 36.1-36.26 - Daniel A. Spielman, Huan Wang, John Wright:
Exact Recovery of Sparsely-Used Dictionaries. 37.1-37.18 - Elad Hazan, Satyen Kale, Shai Shalev-Shwartz:
Near-Optimal Algorithms for Online Matrix Prediction. 38.1-38.13 - Rina Foygel:
Commentary on "Near-Optimal Algorithms for Online Matrix Prediction". 38.14-38.17 - Shipra Agrawal, Navin Goyal:
Analysis of Thompson Sampling for the Multi-armed Bandit Problem. 39.1-39.26 - Shiau Hong Lim, Peter Auer:
Autonomous Exploration For Navigating In MDPs. 40.1-40.24 - Sébastien Bubeck, Nicolò Cesa-Bianchi, Sham M. Kakade:
Towards Minimax Policies for Online Linear Optimization with Bandit Feedback. 41.1-41.14 - Sébastien Bubeck, Aleksandrs Slivkins:
The Best of Both Worlds: Stochastic and Adversarial Bandits. 42.1-42.23
Open problems
- Lihong Li, Olivier Chapelle:
Open Problem: Regret Bounds for Thompson Sampling. 43.1-43.3 - H. Brendan McMahan, Matthew J. Streeter:
Open Problem: Better Bounds for Online Logistic Regression. 44.1-44.3 - Jan Ramon, Constantin Comendant:
Open Problem: Learning Dynamic Network Models from a Static Snapshot. 45.1-45.3 - Cynthia Rudin, Robert E. Schapire, Ingrid Daubechies:
Open Problem: Does AdaBoost Always Cycle? 46.1-46.4 - Ohad Shamir:
Open Problem: Is Averaging Needed for Strongly Convex Stochastic Gradient Descent? 47.1-47.3
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