yusuke1997/dblp-id-title huggingface dataset:
Learning in Threshold Networks
A Data-Dependent Skeleton Estimate for Learning
Mirror Descent Strikes Again: Optimal Stochastic Convex Optimization under Infinite Noise Variance
Learning Mixtures of Product Distributions Using Correlations and Independence
The Strength of Noninclusions for Teams of Finite Learners (Extended Abstract)
Minimax Regret Optimization for Robust Machine Learning under Distribution Shift
Tight Lower Bounds for Combinatorial Multi-Armed Bandits
Teaching Classes with High Teaching Dimension Using Few Examples
Learning µ-branching Programs with Queries
Mind Change Efficient Learning
Agnostic Proper Learning of Halfspaces under Gaussian Marginals
Agnostic Online Learning
How Many Strings Are Easy to Predict?
Robust Selective Sampling from Single and Multiple Teachers
Challenges in Machine Learning for Text Classification
Exact and Robust Conformal Inference Methods for Predictive Machine Learning with Dependent Data
Taking a Big Step: Large Learning Rates in Denoising Score Matching Prevent Memorization
From Averaging to Acceleration, There is Only a Step-size
Rigorous Learning Curve Bounds from Statistical Mechanics
Large Margin Classification Using the Perceptron Algorithm
The SMART approach to instance-optimal online learning
Gradient descent follows the regularization path for general losses
Polynomial Time and Sample Complexity for Non-Gaussian Component Analysis: Spectral Methods
Learning to Communicate via Unknown Channel (Abstract)
Learning Sparsely Used Overcomplete Dictionaries
Optimal Mean Estimation without a Variance
An Efficient Re-scaled Perceptron Algorithm for Conic Systems
Fast and robust tensor decomposition with applications to dictionary learning
Differentially Private Synthetic Graphs Preserving Triangle-Motif Cuts
Last Iterate is Slower than Averaged Iterate in Smooth Convex-Concave Saddle Point Problems
Impossible Tuning Made Possible: A New Expert Algorithm and Its Applications
On Agnostic Learning with {0, *, 1}-Valued and Real-Valued Hypotheses
Estimation of Extreme Values and Associated Level Sets of a Regression Function via Selective Sampling
A Dimension-free Computational Upper-bound for Smooth Optimal Transport Estimation
Simplified PAC-Bayesian Margin Bounds
Online Learning in Dynamically Changing Environments
Uniqueness of Tensor Decompositions with Applications to Polynomial Identifiability
Submodular Optimization under Noise
Memoryless Sequences for Differentiable Losses
Sparsity vs. Large Margins for Linear Classifiers
Computational-Statistical Gaps in Gaussian Single-Index Models (Extended Abstract)
Open Problem: Tight Online Confidence Intervals for RKHS Elements
On the Hardness of Bandit Learning
Tree-projected gradient descent for estimating gradient-sparse parameters on graphs
COLT 2009 - The 22nd Conference on Learning Theory, Montreal, Quebec, Canada, June 18-21, 2009
Optimal SQ Lower Bounds for Learning Halfspaces with Massart Noise
Time series prediction and online learning
l 1 Regularization in Infinite Dimensional Feature Spaces
Thresholding Based Outlier Robust PCA
Universal Lower Bounds and Optimal Rates: Achieving Minimax Clustering Error in Sub-Exponential Mixture Models
Online Tracking of Linear Subspaces
Minimizing Regret with Label Efficient Prediction
The Optimality of Jeffreys Prior for Online Density Estimation and the Asymptotic Normality of Maximum Likelihood Estimators
Robust Estimation for Random Graphs
Open Problem: Polynomial linearly-convergent method for g-convex optimization?
Lower Bounds on the Sample Complexity of Exploration in the Multi-armed Bandit Problem
Nearly Non-Expansive Bounds for Mahalanobis Hard Thresholding
Consistent recovery threshold of hidden nearest neighbor graphs
Asking Questions to Minimize Errors
Statistical Query Algorithms and Low Degree Tests Are Almost Equivalent
On Prediction of Individual Sequences Relative to a Set of Experts in the Presence of Noise
Lower Bounds for Private Estimation of Gaussian Covariance Matrices under All Reasonable Parameter Regimes
Global Convergence of the EM Algorithm for Mixtures of Two Component Linear Regression
Faster Sampling without Isoperimetry via Diffusion-based Monte Carlo
Open Problem: Online Local Learning
PExact = Exact Learning
How to Use Expert Advice in the Case when Actual Values of Estimated Events Remain Unknown
Better Algorithms for Stochastic Bandits with Adversarial Corruptions
Non-asymptotic convergence bounds for Sinkhorn iterates and their gradients: a coupling approach
On the Power of Learning Robustly
Improved Second-Order Bounds for Prediction with Expert Advice
Markov Decision Processes in Large State Spaces
Open Problem: Can Local Regularization Learn All Multiclass Problems?
Learning Halfspaces Under Log-Concave Densities: Polynomial Approximations and Moment Matching
Towards Fundamental Limits for Active Multi-distribution Learning
Learning Algorithms in the Limit
Estimating a Mixture of Two Product Distributions
Open Problem: Recursive Teaching Dimension Versus VC Dimension
The Pareto Frontier of Instance-Dependent Guarantees in Multi-Player Multi-Armed Bandits with no Communication
Exact Recovery of Clusters in Finite Metric Spaces Using Oracle Queries
Almost Tight Upper Bound for Finding Fourier Coefficients of Bounded Pseudo- Boolean Functions
The Learnability of Formal Concepts
Open Problem: Are all VC-classes CPAC learnable?
Adaptivity to Smoothness in X-armed bandits
Improved Offline Contextual Bandits with Second-Order Bounds: Betting and Freezing
Private Online Prediction from Experts: Separations and Faster Rates
The Fundamental Limits of Recovering Planted Subgraphs (extended abstract)
Winnowing with Gradient Descent
Lower bounds for testing graphical models: colorings and antiferromagnetic Ising models
A Faster Approximation Algorithm for the Gibbs Partition Function
Breaking The Dimension Dependence in Sparse Distribution Estimation under Communication Constraints
Simple Bayesian Algorithms for Best Arm Identification
Exact Identification of Circuits Using Fixed Points of Amplification Functions (Abstract)
An Optimal Parallel Algorithm for Learning DFA
Non-Stochastic Multi-Player Multi-Armed Bandits: Optimal Rate With Collision Information, Sublinear Without
The Sample Complexity of Optimizing a Convex Function
Online Sparse Linear Regression
Stochastic Convex Optimization
The Complexity of Learning Halfspaces using Generalized Linear Methods
A High-dimensional Convergence Theorem for U-statistics with Applications to Kernel-based Testing
Towards Optimal Algorithms for Multi-Player Bandits without Collision Sensing Information
Strong Minimax Lower Bounds for Learning
Regret Minimization for Branching Experts
Action-Constrained Markov Decision Processes With Kullback-Leibler Cost
Ellipsoid Approximation Using Random Vectors
Efficient Near-Optimal Algorithm for Online Shortest Paths in Directed Acyclic Graphs with Bandit Feedback Against Adaptive Adversaries
Aggregation and Sparsity Via l 1 Penalized Least Squares
An Efficient Minimax Optimal Estimator For Multivariate Convex Regression
Complexity of Teaching by a Restricted Number of Examples
Optimal Scoring Rules for Multi-dimensional Effort
Open Problem: Order Optimal Regret Bounds for Kernel-Based Reinforcement Learning
Orthogonal Causal Calibration (Extended Abstract)
Information Complexity in Bandit Subset Selection
Learning to Prune: Speeding up Repeated Computations
An uncertainty principle for Linear Recurrent Neural Networks
Functions with average smoothness: structure, algorithms, and learning
Learning with Square Loss: Localization through Offset Rademacher Complexity
Efficient Classification for Metric Data
Analysis of a Simple Learning Algorithm: Learning Foraging Thresholds for Lizards
Analysis of Two Gradient-based Algorithms for On-line Regression
On the Complexity of Learning from Drifting Distributions
Improved Path-length Regret Bounds for Bandits
Suboptimal Behavior of Bayes and MDL in Classification Under Misspecification
Random DFA's Can Be Approximately Learned from Sparse Uniform Examples
Worst-Case Quadratic Loss Bounds for a Generalization of the Widrow-Hoff Rule
Learning Via Queries With Teams and Anomilies
Sum-of-squares meets square loss: Fast rates for agnostic tensor completion
Optimal approximation of continuous functions by very deep ReLU networks
An Information Theoretic Tradeoff between Complexity and Accuracy
Tracking the Best of Many Experts
Corruption-robust exploration in episodic reinforcement learning
Adaptive Recovery of Signals by Convex Optimization
Open Problem: Black-Box Reductions and Adaptive Gradient Methods for Nonconvex Optimization
Information-Theoretic Generalization Bounds for Stochastic Gradient Descent
General Oracle Inequalities for Gibbs Posterior with Application to Ranking
Computable learning of natural hypothesis classes
Selfish Robustness and Equilibria in Multi-Player Bandits
Generalization Bounds for Data-Driven Numerical Linear Algebra
On Using Extended Statistical Queries to Avoid Membership Queries
Learning Theory, 19th Annual Conference on Learning Theory, COLT 2006, Pittsburgh, PA, USA, June 22-25, 2006, Proceedings
Polynomial-Time Learning of Very Simple Grammars from Positive Data
Improved Boosting Algorithms using Confidence-Rated Predictions
Margin-Based Ranking Meets Boosting in the Middle
Robust Learning - Rich and Poor
Stochastic linear optimization never overfits with quadratically-bounded losses on general data
Learning Curves in Large Neural Networks
Types of Noise in Data for Concept Learning
Bayes correlated equilibria, no-regret dynamics in Bayesian games, and the price of anarchy
Hierarchical Clustering in Graph Streams: Single-Pass Algorithms and Space Lower Bounds
Towards a Characterization of Polynomial Preference Elicitation with Value Queries in Combinatorial Auctions (Extended Abstract)
Big-Step-Little-Step: Efficient Gradient Methods for Objectives with Multiple Scales
On the Role of Channel Capacity in Learning Gaussian Mixture Models
Stochastic Constrained Contextual Bandits via Lyapunov Optimization Based Estimation to Decision Framework
Generalization in Partially Connected Layered Neural Networks
Using a Linear Fit to Determine Monotonicity Directions
Exact Mean Square Linear Stability Analysis for SGD
Learning Non-Discriminatory Predictors
Simple Learning Algorithms Using Divide and Conquer
Bounds in query learning
Learning Conjunctions of Two Unate DNF Formulas (Extended Abstract): Computational and Informational Results
PLAL: Cluster-based active learning
Generating all Maximal Independent Sets of Bounded-Degree Hypergraphs
Efficient Convex Optimization with Membership Oracles
A Guide to Learning Arithmetic Circuits
Toward Instance-Optimal State Certification With Incoherent Measurements
On The Memory Complexity of Uniformity Testing
The Last-Iterate Convergence Rate of Optimistic Mirror Descent in Stochastic Variational Inequalities
Learning One-Dimensional Geometric Patterns Under One-Sided Random Misclassification Noise
Faster Algorithms for Testing under Conditional Sampling
Minimax Instrumental Variable Regression and L 2 Convergence Guarantees without Identification or Closedness
Convergence of Gradient Descent with Small Initialization for Unregularized Matrix Completion
Testing (Conditional) Mutual Information - Extended Abstract
Online Policy Optimization in Unknown Nonlinear Systems
InfoNCE Loss Provably Learns Cluster-Preserving Representations
Agnostic Active Learning of Single Index Models with Linear Sample Complexity
Exploring Local Norms in Exp-concave Statistical Learning
Orthogonal Statistical Learning with Self-Concordant Loss
Depth Separations in Neural Networks: What is Actually Being Separated?
Boosting with the Logistic Loss is Consistent
Optimal Differentially Private Sampling of Unbounded Gaussians
Algorithms for Sparse LPN and LSPN Against Low-noise (extended abstract)
Fast Rates for Online Prediction with Abstention
Non-convex matrix sensing: Breaking the quadratic rank barrier in the sample complexity Extended Abstract
On Computationally Efficient Multi-Class Calibration
Bayesian Networks and Inner Product Spaces
On the Query Complexity of Learning
Non-Euclidean Differentially Private Stochastic Convex Optimization
Computational Intractability of Strategizing against Online Learners
Robust Separations in Inductive Inference (Abstract)
A Spectral Algorithm for Learning Hidden Markov Models
STay-ON-the-Ridge: Guaranteed Convergence to Local Minimax Equilibrium in Nonconvex-Nonconcave Games
Hardness of Identity Testing for Restricted Boltzmann Machines and Potts models
Data Dependent Risk Bounds for Hierarchical Mixture of Experts Classifiers
Learning First Order Universal Horn Expressions
Non-Convex Matrix Completion Against a Semi-Random Adversary
Strategizing against Learners in Bayesian Games
Learning and Robust Learning of Product Distributions
Fit Like You Sample: Sample-Efficient Generalized Score Matching from Fast Mixing Diffusions
Depth and Feature Learning are Provably Beneficial for Neural Network Discriminators
Tackling Combinatorial Distribution Shift: A Matrix Completion Perspective
Beyond Worst-Case Online Classification: VC-Based Regret Bounds for Relaxed Benchmarks
Frequencies vs Biases: Machine Learning Problems in Natural Language Processing (Extended Abstract)
Frank-Wolfe with a Nearest Extreme Point Oracle
21st Annual Conference on Learning Theory - COLT 2008, Helsinki, Finland, July 9-12, 2008
Density Evolution in the Degree-correlated Stochastic Block Model
Learning Using Local Membership Queries
Polynomial Regression under Arbitrary Product Distributions
On the Inductive Inference of Real Valued Functions
Projective DNF Formulae and Their Revision
Fast two-time-scale stochastic gradient method with applications in reinforcement learning
Optimal Graph Reconstruction by Counting Connected Components in Induced Subgraphs
Efficient Parameter Estimation of Truncated Boolean Product Distributions
How Fast Is k -Means?
Empirical bounds for functions with weak interactions
Fast and Multiphase Rates for Nearest Neighbor Classifiers
Proceedings of the Tenth Annual Conference on Computational Learning Theory, COLT 1997, Nashville, Tennessee, USA, July 6-9, 1997
Learning with metric losses
Achieving Optimal Dynamic Regret for Non-stationary Bandits without Prior Information
On Linear Stochastic Approximation: Fine-grained Polyak-Ruppert and Non-Asymptotic Concentration
Distribution-independent Reliable Learning
Private and polynomial time algorithms for learning Gaussians and beyond
Lower Bounds on Regret for Noisy Gaussian Process Bandit Optimization
On the Sample Complexity of Weak Learning
Online Learning with Set-valued Feedback
Neural Networks can Learn Representations with Gradient Descent
Bellman Strikes Again! The Growth Rate of Sample Complexity with Dimension for the Nearest Neighbor Classifier
On the Convergence of Min-Max Langevin Dynamics and Algorithm
Further Explanation of the Effectiveness of Voting Methods: The Game between Margins and Weights
Smaller Confidence Intervals From IPW Estimators via Data-Dependent Coarsening (Extended Abstract)
On Genetic Algorithms
Strategies for Prediction Under Imperfect Monitoring
The Structured Abstain Problem and the Lovász Hinge
Improved Algorithms for Effective Resistance Computation on Graphs
A Theory of Interpretable Approximations
The Shortest Path Problem Under Partial Monitoring
Hidden Integrality of SDP Relaxations for Sub-Gaussian Mixture Models
Learning a Single Neuron with Adversarial Label Noise via Gradient Descent
Learning DNF Formulae Under Classes of Probability Distributions
A Priori Generalization Analysis of the Deep Ritz Method for Solving High Dimensional Elliptic Partial Differential Equations
On the Power of Sigmoid Neural Networks
Private Center Points and Learning of Halfspaces
On Optimal Learning Algorithms for Multiplicity Automata
The merged-staircase property: a necessary and nearly sufficient condition for SGD learning of sparse functions on two-layer neural networks
Beyond Parallel Pancakes: Quasi-Polynomial Time Guarantees for Non-Spherical Gaussian Mixtures
From External to Internal Regret
Damped Online Newton Step for Portfolio Selection
$\ell_1$ Regression using Lewis Weights Preconditioning and Stochastic Gradient Descent
Learning Intersections of Two Margin Halfspaces under Factorizable Distributions
Optimum Follow the Leader Algorithm
Proceedings of the 30th Conference on Learning Theory, COLT 2017, Amsterdam, The Netherlands, 7-10 July 2017
Investigating the Distribution Assumptions in the Pac Learning Model
The Role of Learning in Autonomous Robots
Streaming PCA: Matching Matrix Bernstein and Near-Optimal Finite Sample Guarantees for Oja's Algorithm
Adaptive Discretization for Adversarial Lipschitz Bandits
Conference on Learning Theory 2016: Preface
Improper Learning for Non-Stochastic Control
Detecting Correlations with Little Memory and Communication
The Rademacher Complexity of Linear Transformation Classes
Linear Bellman Completeness Suffices for Efficient Online Reinforcement Learning with Few Actions
On the Sample Complexity of Learning Functions with Bounded Variation
(ε, u)-Adaptive Regret Minimization in Heavy-Tailed Bandits
An algorithm with nearly optimal pseudo-regret for both stochastic and adversarial bandits
First-order regret bounds for combinatorial semi-bandits
Online Structured Prediction with Fenchel-Young Losses and Improved Surrogate Regret for Online Multiclass Classification with Logistic Loss
Distributed Cooperative Bayesian Learning Strategies
Online Learning and Blackwell Approachability in Quitting Games
The Sparse Vector Technique, Revisited
Learning a Function of r Relevant Variables
Convex Optimization with Unbounded Nonconvex Oracles using Simulated Annealing
Online Learning: Sufficient Statistics and the Burkholder Method
Learning k -Term DNF Formulas with an Incomplete Membership Oracle
Optimal Group Testing
The Precision of Query Points as a Resource for Learning Convex Polytopes with Membership Queries
Optimal and instance-dependent guarantees for Markovian linear stochastic approximation
On Bayes Methods for On-Line Boolean Prediction
Towards Testing Monotonicity of Distributions Over General Posets
Function Classes That Approximate the Bayes Risk
Stochastic Variance Reduction for Variational Inequality Methods
Multi-view Regression Via Canonical Correlation Analysis
On Learning in the Limit and Non-Uniform (epsilon, delta)-Learning
On-Line Learning with an Oblivious Environment and the Power of Randomization
Mean-field theory of two-layers neural networks: dimension-free bounds and kernel limit
Learning Sparse Multivariate Polynomials over a Field with Queries and Counterexamples
A Stochastic View of Optimal Regret through Minimax Duality
Knowledge-Based Nonlinear Kernel Classifiers
Online Learning for Time Series Prediction
Non-Euclidean High-Order Smooth Convex Optimization Extended Abstract
Reliable Agnostic Learning
Finite Regret and Cycles with Fixed Step-Size via Alternating Gradient Descent-Ascent
Surprising properties of dropout in deep networks
On the Complexity of Function Learning
Approximate Methods for Sequential Decision Making Using Expert Advice
Playing the Matching-Shoulders Lob-Pass Game with Logarithmic Regret
On Learning from Noisy and Incomplete Examples
Estimation of Time-Varying Parameters in Statistical Models: An Optimization Approach
Learnability and Stability in the General Learning Setting
Improved Hardness Results for Learning Intersections of Halfspaces
Bounds on the Sample Complexity of Bayesian Learning Using Information Theory and the VC Dimension
Survival of the strictest: Stable and unstable equilibria under regularized learning with partial information
Learning a Single Neuron with Gradient Methods
Online Linear Optimization via Smoothing
On a Syntactic Characterization of Classification with a Mind Change Bound
Near-optimal method for highly smooth convex optimization
Data Dependent Concentration Bounds for Sequential Prediction Algorithms
On the Complexity of Learning Strings and Sequences
Optimization-Based Separations for Neural Networks
Fast and Furious Symmetric Learning in Zero-Sum Games: Gradient Descent as Fictitious Play
Efficient Learning Algorithms Yield Circuit Lower Bounds
Open Problem: Anytime Convergence Rate of Gradient Descent
Asymptotic Errors for High-Dimensional Convex Penalized Linear Regression beyond Gaussian Matrices
Adapting to a Changing Environment: the Brownian Restless Bandits
Streaming k-PCA: Efficient guarantees for Oja's algorithm, beyond rank-one updates
Optimal learners for multiclass problems
Hypothesis testing with low-degree polynomials in the Morris class of exponential families
Fast algorithm for overcomplete order-3 tensor decomposition
Statistical Learning with a Nuisance Component
Model predictive control is almost optimal for restless bandits
Noisy Group Testing in the Linear Regime: Exact Thresholds and Efficient
Pure entropic regularization for metrical task systems
Generalization Bounds for Voting Classifiers Based on Sparsity and Clustering
Sampling from a log-concave distribution with compact support with proximal Langevin Monte Carlo
Toward Efficient Agnostic Learning
On the Minimax Regret of Sequential Probability Assignment via Square-Root Entropy
Stable Transductive Learning
Choosing a Reliable Hypothesis
Reconstructing weighted voting schemes from partial information about their power indices
Minimax-optimal reward-agnostic exploration in reinforcement learning
Learning Relatively Small Classes
Information-theoretic thresholds for community detection in sparse networks
Learning Neural Networks with Sparse Activations
Active Sampling for Multiple Output Identification
Boosting Based on a Smooth Margin
Is your function low dimensional?
Algorithmic Stability and Sanity-Check Bounds for Leave-one-Out Cross-Validation
Bias-Variance Error Bounds for Temporal Difference Updates
Horizon-Free Reinforcement Learning in Polynomial Time: the Power of Stationary Policies
Predicting Nearly as Well as the Best Pruning of a Decision Tree
Proceedings of the First Annual Workshop on Computational Learning Theory, COLT '88, Cambridge, MA, USA, August 3-5, 1988
On Finding Large Conjunctive Clusters
Langevin Monte Carlo and JKO splitting
Deterministic Finite-Memory Bias Estimation
Robust causal inference under covariate shift via worst-case subpopulation treatment effects
Regret Lower Bound and Optimal Algorithm in Dueling Bandit Problem
How Many Queries Are Needed to Learn One Bit of Information?
Bayes and Tukey Meet at the Center Point
An Improved Gap-Dependency Analysis of the Noisy Power Method
Learning Two Layer Rectified Neural Networks in Polynomial Time
Exponential Convergence Rates in Classification
Universality of empirical risk minimization
How Local Should a Learning Method Be?
Nonlinear Estimators and Tail Bounds for Dimension Reduction in l 1 Using Cauchy Random Projections
Corrupted Learning Dynamics in Games
Optimal Algorithms for Online Convex Optimization with Multi-Point Bandit Feedback
Model Selection and Stability in k-means Clustering
Prediction by random-walk perturbation
Near-optimal fitting of ellipsoids to random points
A Chaining Algorithm for Online Nonparametric Regression
Sign rank versus VC dimension
Negative curvature obstructs acceleration for strongly geodesically convex optimization, even with exact first-order oracles
Multiclass Learning, Boosting, and Error-Correcting Codes
Approximate Cluster Recovery from Noisy Labels
Oracle Bounds and Exact Algorithm for Dyadic Classification Trees
Efficient approaches for escaping higher order saddle points in non-convex optimization
Open Loop Optimistic Planning
Model Selection and Error Estimation
Free Energy Wells and Overlap Gap Property in Sparse PCA
ID3 Learns Juntas for Smoothed Product Distributions
The All-or-Nothing Phenomenon in Sparse Linear Regression
Private Realizable-to-Agnostic Transformation with Near-Optimal Sample Complexity
Open Problem: Fast Stochastic Exp-Concave Optimization
Conference on Learning Theory 2025: Preface
Curvature and complexity: Better lower bounds for geodesically convex optimization
Stability of k -Means Clustering
Open Problem: Tightness of maximum likelihood semidefinite relaxations