COLT 2017 accepted papers

Posted on June 3, 2017 by Sebastien Bubeck
The list of accepted papers at COLT 2017 has been published and it looks particularly good (see below with links to arxiv version)! The growth trend of previous years continues with 228 submissions (14% increase from 2016) and 73 accepted papers. Note also that this year’s edition will be in Amsterdam beginning of July which should be fun. The deadline for early registration is in two days, so hurry up!

COLT 2017 accepted papers
– Constantinos Daskalakis, Manolis Zampetakis and Christos Tzamos. Ten Steps of EM Suffice for Mixtures of Two Gaussians
– Shachar Lovett and Jiapeng Zhang. Noisy Population Recovery from Unknown Noise
– Lijun Zhang, Tianbao Yang and Rong Jin. Empirical Risk Minimization for Stochastic Convex Optimization: $O(1/n)$- and $O(1/n^2)$-type of Risk Bounds
– Jonathan Scarlett, Ilija Bogunovic and Volkan Cevher. Lower Bounds on Regret for Noisy Gaussian Process Bandit Optimization
– Avrim Blum and Yishay Mansour. Efficient Co-Training of Linear Separators under Weak Dependence
– Michal Moshkovitz and Dana Moshkovitz. Mixing Implies Lower Bounds for Space Bounded Learning
– Mitali Bafna and Jonathan Ullman. The Price of Selection in Differential Privacy
– Nader Bshouty, Dana Drachsler Cohen, Martin Vechev and Eran Yahav. Learning Disjunctions of Predicates
– Avinatan Hassidim and Yaron Singer. Submodular Optimization under Noise
– Debarghya Ghoshdastidar, Ulrike von Luxburg, Maurilio Gutzeit and Alexandra Carpentier. Two-Sample Tests for Large Random Graphs using Network Statistics
– Andreas Maurer. A second-order look at stability and generalization
– Eric Balkanski and Yaron Singer. The Sample Complexity of Optimizing a Convex Function
– Yevgeny Seldin and Gabor Lugosi. An Improved Parametrization and Analysis of the EXP3++ Algorithm for Stochastic and Adversarial Bandits
– Daniel Vainsencher, Shie Mannor and Huan Xu. Ignoring Is a Bliss: Learning with Large Noise Through Reweighting-Minimization
– Alekh Agarwal, Haipeng Luo, Behnam Neyshabur and Robert Schapire. Corralling a Band of Bandit Algorithms
– David Helmbold and Phil Long. Surprising properties of dropout in deep networks
– Nikita Zhivotovskiy. Optimal learning via local entropies and sample compression
– Ilias Diakonikolas, Daniel Kane and Alistair Stewart. Learning Multivariate Log-concave Distributions
– Max Simchowitz, Kevin Jamieson and Benjamin Recht. The Simulator: Understanding Adaptive Sampling in the Moderate-Confidence Regime
– Lunjia Hu, Ruihan Wu, Tianhong Li and Liwei Wang. Quadratic Upper Bound for Recursive Teaching Dimension of Finite VC Classes
– Alon Gonen and Shai Shalev-Shwartz. Fast Rates for Empirical Risk Minimization of Strict Saddle Problems
– Nicolas Brosse, Alain Durmus, Eric Moulines and Marcelo Pereyra. Sampling from a log-concave distribution with compact support with proximal Langevin Monte Carlo
– Marc Lelarge and Léo Miolane. Fundamental limits of symmetric low-rank matrix estimation
– Anima Anandkumar, Yuan Deng, Rong Ge and Hossein Mobahi. Homotopy Analysis for Tensor PCA
– Ravi Kannan and Santosh Vempala. The Hidden Hubs Problem
– Amit Daniely. Depth Separation for Neural Networks
– Tomer Koren, Roi Livni and Yishay Mansour. Bandits with Movement Costs and Adaptive Pricing
– Nicholas Harvey, Christopher Liaw and Abbas Mehrabian. Nearly-tight VC-dimension bounds for neural networks
– Clément Canonne, Ilias Diakonikolas, Daniel Kane and Alistair Stewart. Testing Bayesian Networks
– Arnak Dalalyan. Further and stronger analogy between sampling and optimization: Langevin Monte Carlo and gradient descent
– Maxim Raginsky, Alexander Rakhlin and Matus Telgarsky. Non-Convex Learning via Stochastic Gradient Langevin Dynamics: A Nonasymptotic Analysis
– Alon Cohen, Tamir Hazan and Tomer Koren. Tight Bounds for Bandit Combinatorial Optimization
– Moran Feldman, Christopher Harshaw and Amin Karbasi. Greed Is Good: Near-Optimal Submodular Maximization via Greedy Optimization
– Surbhi Goel, Varun Kanade, Adam Klivans and Justin Thaler. Reliably Learning the ReLU in Polynomial Time
– Bin Hu, Peter Seiler and Anders Rantzer. A Unified Analysis of Stochastic Optimization Methods Using Jump System Theory and Quadratic Constraints
– Michael Kearns and Zhiwei Steven Wu. Predicting with Distributions
– Nicolò Cesa-Bianchi, Pierre Gaillard, Claudio Gentile and Sébastien Gerchinovitz. Online Nonparametric Learning, Chaining, and the Role of Partial Feedback
– Lijie Chen, Jian Li and Mingda Qiao. Towards Instance Optimal Bounds for Best Arm Identification
– Vitaly Feldman. A General Characterization of the Statistical Query Complexity
– Jerry Li. Robust Sparse Estimation Tasks in High Dimensions (*to be merge)
– Nicolas Flammarion and Francis Bach. Stochastic Composite Least-Squares Regression with convergence rate O(1/n)
– Yeshwanth Cherapanamjeri, Prateek Jain and Praneeth Netrapalli. Thresholding based Efficient Outlier Robust PCA
– Amir Globerson, Roi Livni and Shai Shalev-Shwartz. Effective Semisupervised Learning on Manifolds
– Victor-Emmanuel Brunel, Ankur Moitra, Philippe Rigollet and John Urschel. Rates of estimation for determinantal point processes
– Yuchen Zhang, Percy Liang and Moses Charikar. A Hitting Time Analysis of Stochastic Gradient Langevin Dynamics
– Ilias Zadik and David Gamarnik. High-Dimensional Regression with Binary Coefficients. Estimating Squared Error and a Phase Transition
– Ashok Cutkosky and Kwabena Boahen. Online Learning Without Prior Information
– Pasin Manurangsi and Aviad Rubinstein. Inapproximability of VC Dimension and Littlestone’s Dimension
– Joon Kwon, Vianney Perchet and Claire Vernade. Sparse Stochastic Bandits
– Blake Woodworth, Suriya Gunasekar, Mesrob I. Ohannessian and Nathan Srebro. Learning Non-Discriminatory Predictors
– Alexander Rakhlin and Karthik Sridharan. On Equivalence of Martingale Tail Bounds and Deterministic Regret Inequalities
– Maria-Florina Balcan, Vaishnavh Nagarajan, Ellen Vitercik and Colin White. Learning-Theoretic Foundations of Algorithm Configuration for Combinatorial Partitioning Problems
– Arpit Agarwal, Shivani Agarwal, Sepehr Assadi and Sanjeev Khanna. Learning with Limited Rounds of Adaptivity: Coin Tossing, Multi-Armed Bandits, and Ranking from Pairwise Comparisons
– Jerry Li and Ludwig Schmidt. Robust Proper Learning for Mixtures of Gaussians via Systems of Polynomial Inequalities
– Jialei Wang, Weiran Wang and Nathan Srebro. Memory and Communication Efficient Distributed Stochastic Optimization with Minibatch Prox
– Alexandr Andoni, Daniel Hsu, Kevin Shi and Xiaorui Sun. Correspondence retrieval
– Andrea Locatelli, Alexandra Carpentier and Samory Kpotufe. Adaptivity to Noise Parameters in Nonparametric Active Learning
– David Gamarnik, Quan Li and Hongyi Zhang. Matrix Completion from O(n) Samples in Linear Time
– Salil Vadhan. On Learning versus Refutation
– Simon Du, Sivaraman Balakrishnan and Aarti Singh. Computationally Efficient Robust Estimation of Sparse Functionals (*to be merged)
– Sebastian Casalaina-Martin, Rafael Frongillo, Tom Morgan and Bo Waggoner. Multi-Observation Elicitation
– Constantinos Daskalakis and Qinxuan Pan. Square Hellinger Subadditivity for Bayesian Networks and its Applications to Identity Testing
– Vitaly Feldman and Thomas Steinke. Generalization for Adaptively-chosen Estimators via Stable Median
– Shipra Agrawal, Vashist Avadhanula, Vineet Goyal and Assaf Zeevi. Thompson Sampling for the MNL-Bandit
– Rafael Frongillo and Andrew Nobel. Memoryless Sequences for Differentiable Losses
– Gergely Neu and Vicenç Gómez. Fast rates for online learning in Linearly Solvable Markov Decision Processes
– Pranjal Awasthi, Avrim Blum, Nika Haghtalab and Yishay Mansour. Efficient PAC Learning from the Crowd
– Tselil Schramm and David Steurer. Fast and robust tensor decomposition with applications to dictionary learning
– Song Mei, Theodor Misiakiewicz, Andrea Montanari and Roberto Oliveira. Solving SDPs for synchronization and MaxCut problems via the Grothendieck inequality
– Yury Polyanskiy, Ananda Theertha Suresh and Yihong Wu. Sample complexity of population recovery
– Holden Lee, Rong Ge, Tengyu Ma, Andrej Risteski and Sanjeev Arora. On the Ability of Neural Nets to Express Distributions
– Aaron Potechin and David Steurer. Exact tensor completion with sum-of-squares
– Lijie Chen, Anupam Gupta, Jian Li, Mingda Qiao and Ruosong Wang. Nearly Optimal Sampling Algorithms for Combinatorial Pure Exploration
– Dylan Foster, Alexander Rakhlin and Karthik Sridharan. ZIGZAG: A new approach to adaptive online learning
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4 Responses to "COLT 2017 accepted papers"

  • By Zirkonyum December 5, 2020 - 5:42 am

    Thank You for sharing, this is a good source!

    Reply
  • By MICHAEL FROM NORTHWESTERN UNI. November 24, 2020 - 3:36 am

    No way!!

    Reply
  • By Suzi Anrew November 24, 2020 - 3:34 am

    Thank you for sharing

    Reply
  • By Kamalika June 3, 2017 - 4:26 pm

    Also for those authors who haven’t submitted the final versions yet — the final version deadline is tomorrow (Jun 4), so hurry up 🙂

    Reply

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