机器学习理论之狂欢 COLT 2020 The 33rd Annual Conference on Learning Theory (COLT 2020) Country: Austria City: Graz 线上举办 Abstr. due: 31.01.2020 Dates: 09.07.20 — 12.07.20 Area Of Sciences: Pedagogy; Organizing comittee e-mail: TBA here: http://learningtheory.org/colt2020/. Organizers: Association for Computational Learning The 33rd Annual Conference on Learning Theory (COLT 2020) will take place in Graz, Austria online…

Written by


机器学习理论之狂欢 COLT2020

机器学习理论之狂欢 COLT 2020

The 33rd Annual Conference on Learning Theory (COLT 2020)

Country: Austria City: Graz


Abstr. due: 31.01.2020 Dates: 09.07.20 — 12.07.20

Area Of Sciences: Pedagogy;

Organizing comittee e-mail: TBA here: http://learningtheory.org/colt2020/.

Organizers: Association for Computational Learning

The 33rd Annual Conference on Learning Theory (COLT 2020) will take place in Graz, Austria online during July 9-12, 2020. We invite submissions of papers addressing theoretical aspects of machine learning and related topics. We strongly support a broad definition of learning theory, including, but not limited to:

  • Design and analysis of learning algorithms
  • Statistical and computational complexity of learning
  • Optimization methods for learning, and/or online and/or stochastic optimization
  • Supervised learning
  • Unsupervised and semi-supervised learning
  • Active and interactive learning
  • Reinforcement learning
  • Online learning and decision-making
  • Interactions of learning theory with other mathematical fields
  • Theory of artificial neural networks, including (theory of) deep learning
  • High-dimensional and non-parametric statistics
  • Learning with algebraic or combinatorial structure
  • Theoretical analysis of probabilistic graphical models
  • Bayesian methods in learning
  • Game theory and learning
  • Learning with system constraints (e.g., privacy, fairness, memory, communication)
  • Learning from complex data (e.g., networks, time series)
  • Learning in other settings (e.g., computational social science, economics)

今年 COLT 同样采取了虚拟会议的方式进行,这种方式已经成为了 20s 新常态,借用 David Hilber 的名言:我们必须熟悉,我们必将熟悉。

先看看本次会议的 CFP:

  • 学习算法的设计与分析 Design and analysis of learning algorithms
  • 学习的统计和计算复杂性 Statistical and computational complexity of learning
  • 学习的(在线/随机)优化方法 Optimization methods for learning, and/or online and/or stochastic optimization
  • 监督学习 Supervised learning
  • 无监督/半监督学习 Unsupervised and semi-supervised learning
  • 主动学习和交互学习 Active and interactive learning
  • 强化学习 Reinforcement learning
  • 在线学习和决策制定 Online learning and decision-making
  • 学习理论与其他数学领域的交叉 Interactions of learning theory with other mathematical fields
  • 人工神经网络理论,包含深度学习理论 Theory of artificial neural networks, including (theory of) deep learning
  • 高维统计与无参数统计 High-dimensional and non-parametric statistics
  • 有代数或者组合结构的学习 Learning with algebraic or combinatorial structure
  • 概率图模型的理论分析 Theoretical analysis of probabilistic graphical models
  • 学习中的贝叶斯方法 Bayesian methods in learning
  • 博弈论与学习 Game theory and learning
  • 有系统限制(如隐私,公平性,记忆,通讯等方面)的学习 Learning with system constraints (e.g., privacy, fairness, memory, communication)
  • 从复杂数据(如网络,时间序列)中学习 Learning from complex data (e.g., networks, time series)
  • 在其他设定(如计算社会科学、经济学等)下的学习 Learning in other settings (e.g., computational social science, economics)


  • Locally Private Hypothesis Selection
    Sivakanth Gopi, Gautam Kamath, Janardhan D Kulkarni, Aleksandar Nikolov, Steven Wu, Huanyu Zhang
  • Differentially Private Mean Estimation of Heavy-Tailed Distributions
    Gautam Kamath, Vikrant Singhal, Jonathan Ullman
  • An O(m/eps^3.5)-Cost Algorithm for Semidefinite Programs with Diagonal Constraints
    Swati Padmanabhan, Yin Tat Lee
  • Adaptive Submodular Maximization under Stochastic Item Costs
    Srinivasan Parthasarathy
  • Gradient descent algorithms for Bures-Wasserstein barycenters
    Sinho Chewi, Philippe Rigollet, Tyler Maunu, Austin Stromme
  • On the gradient complexity of linear regression
    Elad Hazan, Mark Braverman, Max Simchowitz, Blake E Woodworth
  • Improper Learning for Non-Stochastic Control
    Max Simchowitz, Karan Singh, Elad Hazan
  • Root-n-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank
    Kefan Dong, Jian Peng, Yining Wang, Yuan Zhou
  • No-Regret Prediction in Marginally Stable Systems
    Udaya Ghai, Holden Lee, Karan Singh, Cyril Zhang, Yi Zhang
  • Halpern Iteration for Near-Optimal and Parameter-Free Monotone Inclusion and Strong Solutions to Variational Inequalities
    Jelena Diakonikolas
  • PAC learning with stable and private predictions
    Yuval Dagan, Vitaly Feldman
  • Learning a Single Neuron with Gradient Methods
    Gilad Yehudai, Ohad Shamir
  • Universal Approximation with Deep Narrow Networks
    Patrick Kidger, Terry J Lyons
  • Asymptotic Errors for High-Dimensional Convex Penalized Linear Regression beyond Gaussian Matrices
    Alia Abbara, Florent Krzakala, Cedric Gerbelot
  • On the Convergence of Stochastic Gradient Descent with Low-Rank Projections for Convex Low-Rank Matrix Problems
    Dan Garber
  • From Nesterov’s Estimate Sequence to Riemannian Acceleration
    Kwangjun Ahn, Suvrit Sra
  • Selfish Robustness and Equilibria in Multi-Player Bandits
    Etienne Boursier, Vianney Perchet
  • How Good is SGD with Random Shuffling?
    Itay M Safran, Ohad Shamir
  • Exploration by Optimisation in Partial Monitoring
    Tor Lattimore, Csaba Szepesvari
  • Extending Learnability to Auxiliary-Input Cryptographic Primitives and Meta-PAC Learning
    Mikito Nanashima
  • Noise-tolerant, Reliable Active Classification with Comparison Queries
    Max Hopkins, Shachar Lovett, Daniel Kane, Gaurav Mahajan
  • Sharper Bounds for Uniformly Stable Algorithms
    Olivier Bousquet, Yegor Klochkov, Nikita Zhivotovskiy
  • Optimality and Approximation with Policy Gradient Methods in Markov Decision Processes
    Alekh Agarwal, Sham Kakade, Jason Lee, Gaurav Mahajan
  • Consistent recovery threshold of hidden nearest neighbor graphs
    Jian Ding, Yihong Wu, Jiaming Xu, Dana Yang
  • High probability guarantees for stochastic convex optimization
    Damek Davis, Dmitriy Drusvyatskiy
  • Information Directed Sampling for Linear Partial Monitoring
    Johannes Kirschner, Tor Lattimore, Andreas Krause
  • ID3 Learns Juntas for Smoothed Product Distributions
    Eran Malach, Amit Daniely, Alon Brutzkus
  • Tight Lower Bounds for Combinatorial Multi-Armed Bandits
    Nadav Merlis, Shie Mannor
  • Domain Compression and its Application to Randomness-Optimal Distributed Goodness-of-Fit
    Jayadev Acharya, Clement L Canonne, Yanjun Han, Ziteng Sun, Himanshu Tyagi
  • Reasoning About Generalization via Conditional Mutual Information
    Thomas Steinke, Lydia Zakynthinou
  • A Greedy Anytime Algorithm for Sparse PCA
    Dan Vilenchik, Adam Soffer, Guy Holtzman
  • Logsmooth Gradient Concentration and Tighter Runtimes for Metropolized Hamiltonian Monte Carlo
    Yin Tat Lee, Ruoqi Shen, Kevin Tian
  • Provably Efficient Reinforcement Learning with Linear Function Approximation
    Chi Jin, Zhuoran Yang, Zhaoran Wang, Michael Jordan
  • A Fast Spectral Algorithm for Mean Estimation with Sub-Gaussian Rates
    Zhixian Lei, Kyle Luh, Prayaag Venkat, Fred Zhang
  • How to trap a gradient flow
    Dan Mikulincer, Sebastien Bubeck
  • Near-Optimal Algorithms for Minimax Optimization
    Tianyi Lin, Chi Jin, Michael Jordan
  • Model-Based Reinforcement Learning with a Generative Model is Minimax Optimal
    Alekh Agarwal, Sham Kakade, Lin Yang
  • Finite Time Analysis of Linear Two-timescale Stochastic Approximation with Markovian Noise
    Maksim Kaledin, Eric Moulines, Alexey Naumov, Vladislav Tadic, Hoi-To Wai
  • Fast Rates for Online Prediction with Abstention
    Gergely Neu, Nikita Zhivotovskiy
  • Smooth Contextual Bandits: Bridging the Parametric and Non-differentiable Regret Regimes
    YICHUN HU, Nathan Kallus, Xiaojie Mao
  • Data-driven confidence bands for distributed nonparametric regression
    Valeriy Avanesov
  • Tsallis-INF for Decoupled Exploration and Exploitation in Multi-armed Bandits
    Chloé Rouyer, Yevgeny Seldin
  • Pan-Private Uniformity Testing
    Kareem Amin, Matthew Joseph, Jieming Mao
  • ODE-Inspired Analysis for the Biological Version of Oja’s Rule in Solving Streaming PCA
    Mien Brabeeba Wang, Chi-Ning Chou
  • Complexity Guarantees for Polyak Steps with Momentum
    Mathieu Barre, Adrien B Taylor, Alexandre d’Aspremont
  • Calibrated Surrogate Losses for Adversarially Robust Classification
    Han Bao, Clayton Scott, Masashi Sugiyama
  • Near-Optimal Methods for Minimizing Star-Convex Functions and Beyond
    Oliver Hinder, Aaron Sidford, Nimit S Sohoni
  • Faster Projection-free Online Learning
    Edgar Minasyan, Elad Hazan
  • Non-Stochastic Multi-Player Multi-Armed Bandits: Optimal Rate With Collision Information, Sublinear Without
    Sebastien Bubeck, Yuanzhi Li, Yuval Peres, Mark Sellke
  • Coordination without communication: optimal regret in two players multi-armed bandits
    Sebastien Bubeck, Thomas Budzinski
  • EM Algorithm is Sample-Optimal for Learning Mixtures of Well-Separated Gaussians
    Jeongyeol Kwon, Constantine Caramanis
  • Online Learning with Vector Costs and Bandits with Knapsacks
    Thomas Kesselheim, Sahil Singla
  • Better Algorithms for Estimating Non-Parametric Models in Crowd-Sourcing and Rank Aggregation
    Allen X Liu, Ankur Moitra
  • Nearly Non-Expansive Bounds for Mahalanobis Hard Thresholding
    Xiaotong Yuan, Ping Li
  • Learning Halfspaces with Massart Noise Under Structured Distributions
    Ilias Diakonikolas, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis
  • Rigorous Guarantees for Tyler’s M-Estimator via Quantum Expansion
    William C Franks, Ankur Moitra
  • Active Learning for Identification of Linear Dynamical Systems
    Andrew J Wagenmaker, Kevin Jamieson
  • Bounds in query learning
    Hunter S Chase, James Freitag
  • Active Local Learning
    Arturs Backurs, Avrim Blum, Neha Gupta
  • Kernel and Rich Regimes in Overparametrized Models
    Blake E Woodworth, Suriya Gunasekar, Jason Lee, Edward Moroshko, Pedro Henrique Pamplona Savarese, Itay Golan, Daniel Soudry, Nathan Srebro
  • Learning Zero-Sum Simultaneous-Move Markov Games Using Function Approximation and Correlated Equilibrium
    Qiaomin Xie, Yudong Chen, Zhaoran Wang, Zhuoran Yang
  • Finite-Time Analysis of Asynchronous Stochastic Approximation and $Q$-Learning
    Guannan Qu, Adam Wierman
  • Parallels Between Phase Transitions and Circuit Complexity?
    Colin P Sandon, Ankur Moitra, Elchanan Mossel
  • Hierarchical Clustering: A 0.585 Revenue Approximation
    Noga Alon, Yossi Azar, Danny Vainstein
  • Gradient descent follows the regularization path for general losses
    Ziwei Ji, Miroslav Dudik, Robert Schapire, Matus Telgarsky
  • Bessel Smoothing and Multi-Distribution Property Estimation
    Yi Hao, Ping Li
  • Privately Learning Thresholds: Closing the Exponential Gap
    Uri Stemmer, Moni Naor, Haim Kaplan, Yishay Mansour, Katrina Ligett
  • Pessimism About Unknown Unknowns Inspires Conservatism
    Michael K Cohen, Marcus Hutter
  • The Influence of Shape Constraints on the Thresholding Bandit Problem
    James Cheshire, Pierre Menard, Alexandra Carpentier
  • Finite Regret and Cycles with Fixed Step-Size via Alternating Gradient Descent-Ascent
    James P Bailey, Gauthier Gidel, Georgios Piliouras
  • Efficient and robust algorithms for adversarial linear contextual bandits
    Gergely Neu, Julia Olkhovskaya
  • Non-asymptotic Analysis for Nonparametric Testing
    Yun Yang, Zuofeng Shang, Guang Cheng
  • Tree-projected gradient descent for estimating gradient-sparse parameters on graphs
    Sheng Xu, Zhou Fan, Sahand Negahban
  • Covariance-adapting algorithm for semi-bandits with application to sparse rewards
    Pierre Perrault, Vianney Perchet, Michal Valko
  • Last Iterate is Slower than Averaged Iterate in Smooth Convex-Concave Saddle Point Problems
    Noah Golowich, Sarath Pattathil, Constantinos Daskalakis, Asuman Ozdaglar
  • A Corrective View of Neural Networks: Representation, Memorization and Learning
    Dheeraj M Nagaraj, Guy Bresler
  • Learning Entangled Single-Sample Gaussians in the Subset-of-Signals Model
    Yingyu Liang, Hui Yuan
  • Implicit Bias of Gradient Descent for Wide Two-layer Neural Networks Trained with the Logistic Loss
    Lénaïc Chizat, Francis Bach
  • Dimension-Free Bounds for Chasing Convex Functions
    Guru Guruganesh, Anupam Gupta, Charles Argue
  • Optimal group testing
    Oliver Gebhard, Philipp Loick, Maximilian Hahn-Klimroth, Amin Coja-Oghlan
  • On Suboptimality of Least Squares with Application to Estimation of Convex Bodies
    Gil Kur, Alexander Rakhlin, Adityanand Guntuboyina
  • A Nearly Optimal Variant of the Perceptron Algorithm for the Uniform Distribution on the Unit Sphere
    Marco Schmalhofer
  • A Closer Look at Small-loss Bounds for Bandits with Graph Feedback
    Chung-Wei Lee, Haipeng Luo, Mengxiao Zhang
  • Extrapolating the profile of a finite population
    Yihong Wu, Yury Polyanskiy, Soham Jana
  • Balancing Gaussian vectors in high dimension
    Paxton M Turner, Raghu Meka, Philippe Rigollet
  • On the Multiple Descent of Minimum-Norm Interpolants and Restricted Lower Isometry of Kernels
    Tengyuan Liang, Alexander Rakhlin, Xiyu Zhai
  • From tree matching to sparse graph alignment
    Luca Ganassali, Laurent Massoulie
  • Estimating Principal Components under Adversarial Perturbations
    Pranjal Awasthi, Xue Chen, Aravindan Vijayaraghavan
  • Logistic Regression Regret: What’s the Catch?
    Gil I Shamir
  • Efficient, Noise-Tolerant, and Private Learning via Boosting
    Mark Bun, Marco L Carmosino, Jessica Sorrell
  • Highly smooth minimization of non-smooth problems
    Brian Bullins
  • Proper Learning, Helly Number, and an Optimal SVM Bound
    Olivier Bousquet, Steve Hanneke, Shay Moran, Nikita Zhivotovskiy
  • Efficient Parameter Estimation of Truncated Boolean Product Distributions
    Dimitris Fotakis, Alkis Kalavasis, Christos Tzamos
  • Estimation and Inference with Trees and Forests in High Dimensions
    Vasilis Syrgkanis, Emmanouil Zampetakis
  • Closure Properties for Private Classification and Online Prediction
    Noga Alon, Amos Beimel, Shay Moran, Uri Stemmer
  • New Potential-Based Bounds for Prediction with Expert Advice
    Vladimir A Kobzar, Robert Kohn, Zhilei Wang
  • Distributed Signal Detection under Communication Constraints
    Jayadev Acharya, Clement L Canonne, Himanshu Tyagi
  • Hardness of Identity Testing for Restricted Boltzmann Machines and Potts models
    Antonio Blanca, Zongchen Chen, Eric Vigoda, Daniel Stefankovic
  • Algorithms and SQ Lower Bounds for PAC Learning One-Hidden-Layer ReLU Networks
    Ilias Diakonikolas, Daniel M Kane, Vasilis Kontonis, Nikos Zarifis
  • Costly Zero Order Oracles
    Renato Paes Leme, Jon Schneider
  • Precise Tradeoffs in Adversarial Training for Linear Regression
    Adel Javanmard, Mahdi Soltanolkotabi, Hamed Hassani
  • Approximate is Good Enough: Probabilistic Variants of Dimensional and Margin Complexity
    Pritish Kamath, Omar Montasser, Nathan Srebro
  • Wasserstein Control of Mirror Langevin Monte Carlo
    Kelvin Shuangjian Zhang, Gabriel Peyré, Jalal Fadili, Marcelo Pereyra
  • The estimation error of general first order methods
    Michael V Celentano, Andrea Montanari, Yuchen Wu
  • Second-Order Information in Non-Convex Stochastic Optimization: Power and Limitations
    Yossi Arjevani, Yair Carmon, John Duchi, Dylan Foster, Ayush Sekhari, Karthik Sridharan
  • Implicit regularization for deep neural networks driven by an Ornstein-Uhlenbeck like process
    Guy Blanc, Neha Gupta, Gregory Valiant, Paul Valiant
  • Free Energy Wells and Overlap Gap Property in Sparse PCA
    Ilias Zadik, Alexander S. Wein, Gerard Ben Arous
  • Robust causal inference under covariate shift via worst-case subpopulation treatment effects
    Sookyo Jeong, Hongseok Namkoong
  • Winnowing with Gradient Descent
    Ehsan Amid, Manfred K. Warmuth
  • Embedding Dimension of Polyhedral Losses
    Jessica J Finocchiaro, Rafael Frongillo, Bo Waggoner
  • List Decodable Subspace Recovery
    Morris Yau, Prasad Raghavendra
  • Approximation Schemes for ReLU Regression
    Ilias Diakonikolas, Surbhi Goel, Sushrut Karmalkar, Adam Klivans, Mahdi Soltanolkotabi
  • Learning Over-parametrized Two-layer ReLU Neural Networks beyond NTK
    Yuanzhi Li, Tengyu Ma, Hongyang R Zhang
  • Fine-grained Analysis for Linear Stochastic Approximation with Averaging: Polyak-Ruppert, Non-asymptotic Concentration and Beyond
    Wenlong Mou, Chris Junchi Li, Martin Wainwright, Peter Bartlett, Michael Jordan
  • Learning Polynomials in Few Relevant Dimensions
    Sitan Chen, Raghu Meka
  • Efficient improper learning for online logistic regression
    Pierre Gaillard, Rémi Jézéquel, Alessandro Rudi
  • Lipschitz and Comparator-Norm Adaptivity in Online Learning
    Zakaria Mhammedi, Wouter M Koolen
  • Information Theoretic Optimal Learning of Gaussian Graphical Models
    Sidhant Misra, Marc D Vuffray, Andrey Lokhov
  • Reducibility and Statistical-Computational Gaps from Secret Leakage
    Matthew S Brennan, Guy Bresler
  • Taking a hint: How to leverage loss predictors in contextual bandits?
    Chen-Yu Wei, Haipeng Luo, Alekh Agarwal


Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s

Create a website or blog at WordPress.com

%d bloggers like this: