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ORCID36th COLT 2024: Edmonton, Canada
- Shipra Agrawal, Aaron Roth:
The Thirty Seventh Annual Conference on Learning Theory, June 30 - July 3, 2023, Edmonton, Canada. Proceedings of Machine Learning Research 247, PMLR 2024 - Preface. i
- Raghavendra Addanki, Siddharth Bhandari:
Limits of Approximating the Median Treatment Effect. 1-21 - Ishaq Aden-Ali, Mikael Møller Høandgsgaard, Kasper Green Larsen, Nikita Zhivotovskiy:
Majority-of-Three: The Simplest Optimal Learner? 22-45 - Maryam Aliakbarpour, Konstantina Bairaktari, Gavin Brown, Adam Smith, Nathan Srebro, Jonathan R. Ullman:
Metalearning with Very Few Samples Per Task. 46-93 - Noga Alon, Shay Moran, Hilla Schefler, Amir Yehudayoff:
A Unified Characterization of Private Learnability via Graph Theory. 94-129 - Philip Amortila, Tongyi Cao, Akshay Krishnamurthy:
Mitigating Covariate Shift in Misspecified Regression with Applications to Reinforcement Learning. 130-160 - Nima Anari, Sinho Chewi, Thuy-Duong Vuong:
Fast parallel sampling under isoperimetry. 161-185 - Felipe Areces, Chen Cheng, John C. Duchi, Kuditipudi Rohith:
Two fundamental limits for uncertainty quantification in predictive inference. 186-218 - Charles Arnal, Vivien Cabannes, Vianney Perchet:
Mode Estimation with Partial Feedback. 219-220 - Hilal Asi, John C. Duchi, Saminul Haque, Zewei Li, Feng Ruan:
Universally Instance-Optimal Mechanisms for Private Statistical Estimation. 221-259 - Julian Asilis, Siddartha Devic, Shaddin Dughmi, Vatsal Sharan, Shang-Hua Teng:
Regularization and Optimal Multiclass Learning. 260-310 - Sepehr Assadi, Chen Wang:
The Best Arm Evades: Near-optimal Multi-pass Streaming Lower Bounds for Pure Exploration in Multi-armed Bandits. 311-358 - Idan Attias, Steve Hanneke, Alkis Kalavasis, Amin Karbasi, Grigoris Velegkas:
Universal Rates for Regression: Separations between Cut-Off and Absolute Loss. 359-405 - Pranjal Awasthi, Nishanth Dikkala, Pritish Kamath, Raghu Meka:
Learning Neural Networks with Sparse Activations. 406-425 - Siddhartha Banerjee, Alankrita Bhatt, Christina Lee Yu:
The SMART approach to instance-optimal online learning. 426 - Kiril Bangachev, Guy Bresler:
Detection of L∞ Geometry in Random Geometric Graphs: Suboptimality of Triangles and Cluster Expansion. 427-497 - MohammadHossein Bateni, Prathamesh Dharangutte, Rajesh Jayaram, Chen Wang:
Metric Clustering and MST with Strong and Weak Distance Oracles. 498-550 - Moïse Blanchard, Doron Cohen, Aryeh Kontorovich:
Correlated Binomial Process. 551-595 - Adam Block, Alexander Rakhlin, Abhishek Shetty:
On the Performance of Empirical Risk Minimization with Smoothed Data. 596-629 - Anna M. Brandenberger, Cassandra Marcussen, Elchanan Mossel, Madhu Sudan:
Errors are Robustly Tamed in Cumulative Knowledge Processes. 630-631 - Guy Bresler, Chenghao Guo, Yury Polyanskiy:
Thresholds for Reconstruction of Random Hypergraphs From Graph Projections. 632-647 - Marco Bressan, Nicolò Cesa-Bianchi, Emmanuel Esposito, Yishay Mansour, Shay Moran, Maximilian Thiessen:
A Theory of Interpretable Approximations. 648-668 - Marco Bressan, Emmanuel Esposito, Maximilian Thiessen:
Efficient Algorithms for Learning Monophonic Halfspaces in Graphs. 669-696 - William Brown, Christos H. Papadimitriou, Tim Roughgarden:
Online Stackelberg Optimization via Nonlinear Control. 697-749 - Gavin Brown, Jonathan Hayase, Samuel B. Hopkins, Weihao Kong, Xiyang Liu, Sewoong Oh, Juan C. Perdomo, Adam Smith:
Insufficient Statistics Perturbation: Stable Estimators for Private Least Squares Extended Abstract. 750-751 - Rares-Darius Buhai, Jingqiu Ding, Stefan Tiegel:
Computational-Statistical Gaps for Improper Learning in Sparse Linear Regression. 752-771 - Yair Carmon, Oliver Hinder:
The Price of Adaptivity in Stochastic Convex Optimization. 772-774 - Matthias C. Caro, Tom Gur, Cambyse Rouzé, Daniel Stilck França, Sathyawageeswar Subramanian:
Information-theoretic generalization bounds for learning from quantum data. 775-839 - Jérémie Chalopin, Victor Chepoi, Fionn Mc Inerney, Sébastien Ratel:
Non-Clashing Teaching Maps for Balls in Graphs. 840-875 - Gautam Chandrasekaran, Adam T. Klivans, Vasilis Kontonis, Raghu Meka, Konstantinos Stavropoulos:
Smoothed Analysis for Learning Concepts with Low Intrinsic Dimension. 876-922 - Zachary Chase, Bogdan Chornomaz, Steve Hanneke, Shay Moran, Amir Yehudayoff:
Dual VC Dimension Obstructs Sample Compression by Embeddings. 923-946 - Lesi Chen, Jing Xu, Jingzhao Zhang:
On Finding Small Hyper-Gradients in Bilevel Optimization: Hardness Results and Improved Analysis. 947-980 - Sitan Chen, Shyam Narayanan:
A faster and simpler algorithm for learning shallow networks. 981-994 - Fan Chen, Constantinos Daskalakis, Noah Golowich, Alexander Rakhlin:
Near-Optimal Learning and Planning in Separated Latent MDPs. 995-1067 - Mingyu Chen, Xuezhou Zhang:
Scale-free Adversarial Reinforcement Learning. 1068-1101 - Byron Chin, Ankur Moitra, Elchanan Mossel, Colin Sandon:
The power of an adversary in Glauber dynamics. 1102-1124 - Miranda Christ, Sam Gunn, Or Zamir:
Undetectable Watermarks for Language Models. 1125-1139 - Nicolas Christianson, Bo Sun, Steven H. Low, Adam Wierman:
Risk-Sensitive Online Algorithms (Extended Abstract). 1140-1141 - Omer Cohen, Ron Meir, Nir Weinberger:
Statistical curriculum learning: An elimination algorithm achieving an oracle risk. 1142-1199 - Edith Cohen, Xin Lyu, Jelani Nelson, Tamás Sarlós, Uri Stemmer:
Lower Bounds for Differential Privacy Under Continual Observation and Online Threshold Queries. 1200-1222 - Lee Cohen, Yishay Mansour, Shay Moran, Han Shao:
Learnability Gaps of Strategic Classification. 1223-1259 - Yan Dai, Qiwen Cui, Simon S. Du:
Refined Sample Complexity for Markov Games with Independent Linear Function Approximation (Extended Abstract). 1260-1261 - Alex Damian, Loucas Pillaud-Vivien, Jason D. Lee, Joan Bruna:
Computational-Statistical Gaps in Gaussian Single-Index Models (Extended Abstract). 1262 - Constantinos Daskalakis, Noah Golowich:
Is Efficient PAC Learning Possible with an Oracle That Responds "Yes" or "No"? 1263-1307 - Ilias Diakonikolas, Daniel M. Kane, Sihan Liu, Nikos Zarifis:
Testable Learning of General Halfspaces with Adversarial Label Noise. 1308-1335 - Ilias Diakonikolas, Daniel M. Kane, Thanasis Pittas, Nikos Zarifis:
Statistical Query Lower Bounds for Learning Truncated Gaussians. 1336-1363 - Ilias Diakonikolas, Daniel M. Kane:
Efficiently Learning One-Hidden-Layer ReLU Networks via SchurPolynomials. 1364-1378 - Daniil Dmitriev, Kristóf Szabó, Amartya Sanyal:
On the Growth of Mistakes in Differentially Private Online Learning: A Lower Bound Perspective. 1379-1398 - Nathan Doumèche, Francis Bach, Gérard Biau, Claire Boyer:
Physics-informed machine learning as a kernel method. 1399-1450 - Maximilien Dreveton, Alperen Gözeten, Matthias Grossglauser, Patrick Thiran:
Universal Lower Bounds and Optimal Rates: Achieving Minimax Clustering Error in Sub-Exponential Mixture Models. 1451-1485 - John C. Duchi, Saminul Haque:
An information-theoretic lower bound in time-uniform estimation. 1486-1500 - Charilaos Efthymiou, Kostas Zampetakis:
On sampling diluted Spin-Glasses using Glauber Dynamics. 1501-1515 - Ayoub El Hanchi, Chris J. Maddison, Murat A. Erdogdu:
Minimax Linear Regression under the Quantile Risk. 1516-1572 - Liad Erez, Alon Cohen, Tomer Koren, Yishay Mansour, Shay Moran:
The Real Price of Bandit Information in Multiclass Classification. 1573-1598 - Ekin Ergen, Moritz Grillo:
Topological Expressivity of ReLU Neural Networks. 1599-1642 - Amedeo Roberto Esposito, Marco Mondelli:
Contraction of Markovian Operators in Orlicz Spaces and Error Bounds for Markov Chain Monte Carlo (Extended Abstract). 1643-1645 - Bertrand Even, Christophe Giraud, Nicolas Verzelen:
Computation-information gap in high-dimensional clustering. 1646-1712 - Hidde Fokkema, Dirk van der Hoeven, Tor Lattimore, Jack J. Mayo:
Online Newton Method for Bandit Convex Optimisation Extended Abstract. 1713-1714 - Aarshvi Gajjar, Wai Ming Tai, Xingyu Xu, Chinmay Hegde, Christopher Musco, Yi Li:
Agnostic Active Learning of Single Index Models with Linear Sample Complexity. 1715-1754 - Aditya Gangrade, Tianrui Chen, Venkatesh Saligrama:
Safe Linear Bandits over Unknown Polytopes. 1755-1795 - Khashayar Gatmiry, Jonathan A. Kelner, Santosh S. Vempala:
Sampling Polytopes with Riemannian HMC: Faster Mixing via the Lewis Weights Barrier. 1796-1881 - Gianmarco Genalti, Lupo Marsigli, Nicola Gatti, Alberto Maria Metelli:
(ε, u)-Adaptive Regret Minimization in Heavy-Tailed Bandits. 1882-1915 - Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Raghu Meka, Chiyuan Zhang:
On Convex Optimization with Semi-Sensitive Features. 1916-1938 - Noah Golowich, Ankur Moitra:
Linear Bellman Completeness Suffices for Efficient Online Reinforcement Learning with Few Actions. 1939-1981 - Tomás González, Cristóbal Guzmán, Courtney Paquette:
Mirror Descent Algorithms with Nearly Dimension-Independent Rates for Differentially-Private Stochastic Saddle-Point Problems extended abstract. 1982 - Parikshit Gopalan, Lunjia Hu, Guy N. Rothblum:
On Computationally Efficient Multi-Class Calibration. 1983-2026 - Parikshit Gopalan, Princewill Okoroafor, Prasad Raghavendra, Abhishek Sherry, Mihir Singhal:
Omnipredictors for regression and the approximate rank of convex functions. 2027-2070 - Spencer L. Gordon, Erik Jahn, Bijan Mazaheri, Yuval Rabani, Leonard J. Schulman:
Identification of mixtures of discrete product distributions in near-optimal sample and time complexity. 2071-2091 - Pascale Gourdeau, Tosca Lechner, Ruth Urner:
On the Computability of Robust PAC Learning. 2092-2121 - Daniel Grier, Hakop Pashayan, Luke Schaeffer:
Principal eigenstate classical shadows. 2122-2165 - Yuzhou Gu, Aaradhya Pandey:
Community detection in the hypergraph stochastic block model and reconstruction on hypertrees. 2166-2203 - Hengquan Guo, Xin Liu:
Stochastic Constrained Contextual Bandits via Lyapunov Optimization Based Estimation to Decision Framework. 2204-2231 - Shivam Gupta, Samuel Hopkins, Eric C. Price:
Beyond Catoni: Sharper Rates for Heavy-Tailed and Robust Mean Estimation. 2232-2269 - Yanjun Han, Tianze Jiang, Yihong Wu:
Prediction from compression for models with infinite memory, with applications to hidden Markov and renewal processes. 2270-2307 - Steve Hanneke:
The Star Number and Eluder Dimension: Elementary Observations About the Dimensions of Disagreement. 2308-2359 - Steve Hanneke, Shay Moran, Tom Waknine:
List Sample Compression and Uniform Convergence. 2360-2388 - Samuel B. Hopkins, Anqi Li:
Adversarially-Robust Inference on Trees via Belief Propagation. 2389-2417 - Daniel Hsu, Arya Mazumdar:
On the sample complexity of parameter estimation in logistic regression with normal design. 2418-2437 - Xunpeng Huang, Difan Zou, Hanze Dong, Yi-An Ma, Tong Zhang:
Faster Sampling without Isoperimetry via Diffusion-based Monte Carlo. 2438-2493 - Dong Huang, Xianwen Song, Pengkun Yang:
Information-Theoretic Thresholds for the Alignments of Partially Correlated Graphs. 2494-2518 - Han Huang, Pakawut Jiradilok, Elchanan Mossel:
Reconstructing the Geometry of Random Geometric Graphs (Extended Abstract). 2519-2521 - Shinji Ito, Taira Tsuchiya, Junya Honda:
Adaptive Learning Rate for Follow-the-Regularized-Leader: Competitive Analysis and Best-of-Both-Worlds. 2522-2563 - Arun Jambulapati, Syamantak Kumar, Jerry Li, Shourya Pandey, Ankit Pensia, Kevin Tian:
Black-Box k-to-1-PCA Reductions: Theory and Applications. 2564-2607 - Arun Jambulapati, Aaron Sidford, Kevin Tian:
Closing the Computational-Query Depth Gap in Parallel Stochastic Convex Optimization. 2608-2643 - Zeyu Jia, Alexander Rakhlin, Ayush Sekhari, Chen-Yu Wei:
Offline Reinforcement Learning: Role of State Aggregation and Trajectory Data. 2644-2719 - Yiheng Jiang, Sinho Chewi, Aram-Alexandre Pooladian:
Algorithms for mean-field variational inference via polyhedral optimization in the Wasserstein space. 2720-2721 - Yujia Jin, Ishani Karmarkar, Christopher Musco, Aaron Sidford, Apoorv Vikram Singh:
Faster Spectral Density Estimation and Sparsification in the Nuclear Norm (Extended Abstract). 2722 - Matthew Joseph, Alexander Yu:
Some Constructions of Private, Efficient, and Optimal K-Norm and Elliptic Gaussian Noise. 2723-2766 - Alkis Kalavasis, Anay Mehrotra, Manolis Zampetakis:
Smaller Confidence Intervals From IPW Estimators via Data-Dependent Coarsening (Extended Abstract). 2767 - Yuqian Cheng, Daniel M. Kane, Zhicheng Zheng:
New Lower Bounds for Testing Monotonicity and Log Concavity of Distributions. 2768-2794 - Yu-Chun Kao, Min Xu, Cun-Hui Zhang:
Choosing the p in Lp Loss: Adaptive Rates for Symmetric Mean Estimation. 2795-2839 - Jonathan A. Kelner, Frederic Koehler, Raghu Meka, Dhruv Rohatgi:
Lasso with Latents: Efficient Estimation, Covariate Rescaling, and Computational-Statistical Gaps. 2840-2886 - Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan:
Testable Learning with Distribution Shift. 2887-2943 - Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan:
Learning Intersections of Halfspaces with Distribution Shift: Improved Algorithms and SQ Lower Bounds. 2944-2978 - Caleb Koch, Carmen Strassle, Li-Yang Tan:
The title of the paper. 2979-3010 - Lingkai Kong, Molei Tao:
Convergence of Kinetic Langevin Monte Carlo on Lie groups. 3011-3063 - Kontonis Vasilis, Mingchen Ma, Tzamos Christos:
Active Learning with Simple Questions. 3064-3098 - Yunbum Kook, Matthew Shunshi Zhang, Sinho Chewi, Murat A. Erdogdu, Mufan (Bill) Li:
Sampling from the Mean-Field Stationary Distribution. 3099-3136 - Yunbum Kook, Santosh S. Vempala:
Gaussian Cooling and Dikin Walks: The Interior-Point Method for Logconcave Sampling. 3137-3240 - Alexander Kozachinskiy, Tomasz Steifer:
Simple online learning with consistent oracle. 3241-3256 - Itai Kreisler, Maor Ivgi, Oliver Hinder, Yair Carmon:
Accelerated Parameter-Free Stochastic Optimization. 3257-3324 - Ilja Kuzborskij, Kwang-Sung Jun, Yulian Wu, Kyoungseok Jang, Francesco Orabona:
Better-than-KL PAC-Bayes Bounds. 3325-3352 - Tosca Lechner, Shai Ben-David:
Inherent limitations of dimensions for characterizing learnability of distribution classes. 3353-3374 - Jongyeong Lee, Junya Honda, Shinji Ito, Min-hwan Oh:
Follow-the-Perturbed-Leader with Fréchet-type Tail Distributions: Optimality in Adversarial Bandits and Best-of-Both-Worlds. 3375-3430 - Gen Li, Yuling Yan, Yuxin Chen, Jianqing Fan:
Minimax-optimal reward-agnostic exploration in reinforcement learning. 3431-3436 - Gene Li, Lin Chen, Adel Javanmard, Vahab Mirrokni:
Optimistic Rates for Learning from Label Proportions. 3437-3474 - Yiheng Lin, James A. Preiss, Fengze Xie, Emile Anand, Soon-Jo Chung, Yisong Yue, Adam Wierman:
Online Policy Optimization in Unknown Nonlinear Systems. 3475-3522 - Yuhan Liu, Jayadev Acharya:
The role of randomness in quantum state certification with unentangled measurements. 3523-3555 - Linxi Liu, Li Ma:
Spatial properties of Bayesian unsupervised trees. 3556-3581 - Quanquan C. Liu, Vaidehi Srinivas:
The Predicted-Updates Dynamic Model: Offline, Incremental, and Decremental to Fully Dynamic Transformations. 3582-3641 - Brendan Lucier, Sarath Pattathil, Aleksandrs Slivkins, Mengxiao Zhang:
Autobidders with Budget and ROI Constraints: Efficiency, Regret, and Pacing Dynamics. 3642-3643 - Josep Lumbreras, Marco Tomamichel:
Linear bandits with polylogarithmic minimax regret. 3644-3682 - Jianhao Ma, Salar Fattahi:
Convergence of Gradient Descent with Small Initialization for Unregularized Matrix Completion. 3683-3742 - Davide Maran, Alberto Maria Metelli, Matteo Papini, Marcello Restelli:
Projection by Convolution: Optimal Sample Complexity for Reinforcement Learning in Continuous-Space MDPs. 3743-3774 - Giovanni Luca Marchetti, Christopher J. Hillar, Danica Kragic, Sophia Sanborn:
Harmonics of Learning: Universal Fourier Features Emerge in Invariant Networks. 3775-3797 - Jay Mardia, Kabir Aladin Verchand, Alexander S. Wein:
Low-degree phase transitions for detecting a planted clique in sublinear time. 3798-3822 - Claudio Mayrink Verdun, Oleh Melnyk, Felix Krahmer, Peter Jung:
Fast, blind, and accurate: Tuning-free sparse regression with global linear convergence. 3823-3872 - Pierre Mergny, Justin Ko, Florent Krzakala, Lenka Zdeborová:
Fundamental Limits of Non-Linear Low-Rank Matrix Estimation. 3873 - Elchanan Mossel, Anirudh Sridhar:
Finding Super-spreaders in Network Cascades. 3874-3914 - Rotem Mulayoff, Tomer Michaeli:
Exact Mean Square Linear Stability Analysis for SGD. 3915-3969 - Gergely Neu, Matteo Papini, Ludovic Schwartz:
Optimistic Information Directed Sampling. 3970-4006 - Sloan Nietert, Ziv Goldfeld, Soroosh Shafiee:
Robust Distribution Learning with Local and Global Adversarial Corruptions (extended abstract). 4007-4008 - Kazusato Oko, Yujin Song, Taiji Suzuki, Denny Wu:
Learning sum of diverse features: computational hardness and efficient gradient-based training for ridge combinations. 4009-4081 - Suzanna Parkinson, Greg Ongie, Rebecca Willett, Ohad Shamir, Nathan Srebro:
Depth Separation in Norm-Bounded Infinite-Width Neural Networks. 4082-4114 - Kumar Kshitij Patel, Margalit Glasgow, Ali Zindari, Lingxiao Wang, Sebastian U. Stich, Ziheng Cheng, Nirmit Joshi, Nathan Srebro:
The Limits and Potentials of Local SGD for Distributed Heterogeneous Learning with Intermittent Communication. 4115-4157 - Binghui Peng, Aviad Rubinstein:
The complexity of approximate (coarse) correlated equilibrium for incomplete information games. 4158-4184 - Binghui Peng:
The sample complexity of multi-distribution learning. 4185-4204 - Ankit Pensia, Varun S. Jog, Po-Ling Loh:
The Sample Complexity of Simple Binary Hypothesis Testing. 4205-4206 - Naty Peter, Eliad Tsfadia, Jonathan R. Ullman:
Smooth Lower Bounds for Differentially Private Algorithms via Padding-and-Permuting Fingerprinting Codes. 4207-4239 - Alireza Fathollah Pour, Hassan Ashtiani, Shahab Asoodeh:
Sample-Optimal Locally Private Hypothesis Selection and the Provable Benefits of Interactivity. 4240-4275 - Nikita Puchkin, Maxim V. Rakhuba:
Dimension-free Structured Covariance Estimation. 4276-4306 - Mingda Qiao, Letian Zheng:
On the Distance from Calibration in Sequential Prediction. 4307-4357 - Vinod Raman, Unique Subedi, Ananth Raman, Ambuj Tewari:
Apple Tasting: Combinatorial Dimensions and Minimax Rates. 4358-4380 - Vinod Raman, Unique Subedi, Ambuj Tewari:
Online Learning with Set-valued Feedback. 4381-4412 - Yilong Qin, Andrej Risteski:
Fit Like You Sample: Sample-Efficient Generalized Score Matching from Fast Mixing Diffusions. 4413-4457 - Shinsaku Sakaue, Han Bao, Taira Tsuchiya, Taihei Oki:
Online Structured Prediction with Fenchel-Young Losses and Improved Surrogate Regret for Online Multiclass Classification with Logistic Loss. 4458-4486 - Wilfred Salmon, Sergii Strelchuk, Tom Gur:
Provable Advantage in Quantum PAC Learning. 4487-4510 - Sergey Samsonov, Daniil Tiapkin, Alexey Naumov, Eric Moulines:
Improved High-Probability Bounds for the Temporal Difference Learning Algorithm via Exponential Stability. 4511-4547 - Khashayar Gatmiry, Jon Schneider:
Adversarial Online Learning with Temporal Feedback Graphs. 4548-4572 - Siyu Chen, Heejune Sheen, Tianhao Wang, Zhuoran Yang:
Training Dynamics of Multi-Head Softmax Attention for In-Context Learning: Emergence, Convergence, and Optimality (extended abstract). 4573 - Mahdi Soleymani, Tara Javidi:
A Non-Adaptive Algorithm for the Quantitative Group Testing Problem. 4574-4592 - Vishwak Srinivasan, Andre Wibisono, Ashia C. Wilson:
Fast sampling from constrained spaces using the Metropolis-adjusted Mirror Langevin algorithm. 4593-4635 - Ludovic Stephan, Yizhe Zhu:
A non-backtracking method for long matrix and tensor completion. 4636-4690 - Arun Sai Suggala, Y. Jennifer Sun, Praneeth Netrapalli, Elad Hazan:
Second Order Methods for Bandit Optimization and Control. 4691-4763 - Stefan Tiegel:
Improved Hardness Results for Learning Intersections of Halfspaces. 4764-4786 - Nuri Mert Vural, Murat A. Erdogdu:
Pruning is Optimal for Learning Sparse Features in High-Dimensions. 4787-4861 - Yuanyu Wan, Tong Wei, Mingli Song, Lijun Zhang:
Nearly Optimal Regret for Decentralized Online Convex Optimization. 4862-4888 - Ziao Wang, Weina Wang, Lele Wang:
Efficient Algorithms for Attributed Graph Alignment with Vanishing Edge Correlation Extended Abstract. 4889-4890 - Zhichao Wang, Denny Wu, Zhou Fan:
Nonlinear spiked covariance matrices and signal propagation in deep neural networks. 4891-4957 - Andre Wibisono, Yihong Wu, Kaylee Yingxi Yang:
Optimal score estimation via empirical Bayes smoothing. 4958-4991 - Changlong Wu, Jin Sima, Wojciech Szpankowski:
Oracle-Efficient Hybrid Online Learning with Unknown Distribution. 4992-5018 - Jingfeng Wu, Peter L. Bartlett, Matus Telgarsky, Bin Yu:
Large Stepsize Gradient Descent for Logistic Loss: Non-Monotonicity of the Loss Improves Optimization Efficiency. 5019-5073 - Jiancong Xiao, Ruoyu Sun, Qi Long, Weijie Su:
Bridging the Gap: Rademacher Complexity in Robust and Standard Generalization. 5074-5075 - Xuzhi Yang, Tengyao Wang:
Multiple-output composite quantile regression through an optimal transport lens. 5076-5122 - Yuepeng Yang, Antares Chen, Lorenzo Orecchia, Cong Ma:
Top-K ranking with a monotone adversary. 5123-5162 - Xifan Yu, Ilias Zadik, Peiyuan Zhang:
Counting Stars is Constant-Degree Optimal For Detecting Any Planted Subgraph: Extended Abstract. 5163-5165 - Sihan Zeng, Thinh T. Doan:
Fast two-time-scale stochastic gradient method with applications in reinforcement learning. 5166-5212 - Zihan Zhang, Yuxin Chen, Jason D. Lee, Simon S. Du:
Settling the sample complexity of online reinforcement learning. 5213-5219 - Zihan Zhang, Wenhao Zhan, Yuxin Chen, Simon S. Du, Jason D. Lee:
Optimal Multi-Distribution Learning. 5220-5223 - Yihan Zhang, Hong Chang Ji, Ramji Venkataramanan, Marco Mondelli:
Spectral Estimators for Structured Generalized Linear Models via Approximate Message Passing (Extended Abstract). 5224-5230 - Matthew Zurek, Yudong Chen:
Gap-Free Clustering: Sensitivity and Robustness of SDP. 5231-5300 - Julian Asilis, Siddartha Devic, Shaddin Dughmi, Vatsal Sharan, Shang-Hua Teng:
Open Problem: Can Local Regularization Learn All Multiclass Problems? 5301-5305 - Achraf Azize, Debabrota Basu:
Open Problem: What is the Complexity of Joint Differential Privacy in Linear Contextual Bandits? 5306-5311 - Clément L. Canonne:
Open Problem: Tight Characterization of Instance-Optimal Identity Testing. 5312-5316 - Xinyi Chen, Elad Hazan:
Open Problem: Black-Box Reductions and Adaptive Gradient Methods for Nonconvex Optimization. 5317-5324 - Steve Hanneke, Shay Moran, Tom Waknine:
Open problem: Direct Sums in Learning Theory. 5325-5329 - Bingshan Hu, Nishant A. Mehta:
Open Problem: Optimal Rates for Stochastic Decision-Theoretic Online Learning Under Differentially Privacy. 5330-5334 - Guy Kornowski, Ohad Shamir:
Open Problem: Anytime Convergence Rate of Gradient Descent. 5335-5339 - Sattar Vakili:
Open Problem: Order Optimal Regret Bounds for Kernel-Based Reinforcement Learning. 5340-5344 - Guillaume Wang, Lénaïc Chizat:
Open problem: Convergence of single-timescale mean-field Langevin descent-ascent for two-player zero-sum games. 5345-5350
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