yusuke1997/dblp-id-title huggingface dataset:
A new ranking scheme for modern data and its application to two-sample hypothesis testing
Generalizing Complex Hypotheses on Product Distributions: Auctions, Prophet Inequalities, and Pandora's Problem
Positive Definite Rational Kernels
Adversarial Online Learning with Temporal Feedback Graphs
Online Learning with Composite Loss Functions
Convergence of Discrete MDL for Sequential Prediction
Some Remarks About Space-Complexity of Learning, and Circuit Complexity of Recognizing
A General Lower Bound on the Number of Examples Needed for Learning
Non-Learnable Classes of Boolean Formulae That Are Closer Under Variable Permutation
Hardness Results for General Two-Layer Neural Networks
Reinforcement Learning of POMDPs using Spectral Methods
Exact Community Recovery in Correlated Stochastic Block Models
A Polynomial-Time Algorithm for Learning k- Variable Pattern Languages from Examples
Online Learning from Optimal Actions
On the Complexity of Bandit Linear Optimization
A Hitting Time Analysis of Stochastic Gradient Langevin Dynamics
Lipschitz Bandits: Regret Lower Bound and Optimal Algorithms
Approximation Schemes for ReLU Regression
Lipschitz and Comparator-Norm Adaptivity in Online Learning
Interactive Algorithms: from Pool to Stream
Principal Component Analysis and Higher Correlations for Distributed Data
Multiplicative Updates for Large Margin Classifiers
Spectral thresholds in the bipartite stochastic block model
Combining Labeled and Unlabeled Data with Co-Training
Online Learning with Prior Knowledge
Low-Degree Multicalibration
Further and stronger analogy between sampling and optimization: Langevin Monte Carlo and gradient descent
On-Line Learning of Functions of Bounded Variation under Various Sampling Schemes
Proceedings of the Fifth Annual ACM Conference on Computational Learning Theory, COLT 1992, Pittsburgh, PA, USA, July 27-29, 1992
Private Mean Estimation of Heavy-Tailed Distributions
It was "all" for "nothing": sharp phase transitions for noiseless discrete channels
Thinking Inside the Ball: Near-Optimal Minimization of the Maximal Loss
On a Class of Gibbs Sampling over Networks
EM's Convergence in Gaussian Latent Tree Models
Is Planted Coloring Easier than Planted Clique?
Differential privacy and robust statistics in high dimensions
S2: An Efficient Graph Based Active Learning Algorithm with Application to Nonparametric Classification
Follow the Leader with Dropout Perturbations
Optimal Scheduling of Dynamic Transport
Fundamental Limits of Weak Recovery with Applications to Phase Retrieval
On Learning Languages from Positive Data and a Limited Number of Short Counterexamples
Finding Super-spreaders in Network Cascades
Open Problem: The Dependence of Sample Complexity Lower Bounds on Planning Horizon
A near-optimal algorithm for approximating the John Ellipsoid
Tighter PAC-Bayes Bounds Through Coin-Betting
Online Non-Parametric Regression
Strong Memory Lower Bounds for Learning Natural Models
Stochastic Gradient Descent Learns State Equations with Nonlinear Activations
The Min-Max Complexity of Distributed Stochastic Convex Optimization with Intermittent Communication
Mean-field nonparametric estimation of interacting particle systems
Two-Sample Tests for Large Random Graphs Using Network Statistics
On a Learnability Question Associated to Neural Networks with Continuous Activations (Extended Abstract)
On Learning Embedded Symmetric Concepts
Inductive Inference: An Abstract Approach
The Sample Complexity of Approximate Rejection Sampling With Applications to Smoothed Online Learning
The Power of Adaptivity in SGD: Self-Tuning Step Sizes with Unbounded Gradients and Affine Variance
Rank-one matrix estimation: analytic time evolution of gradient descent dynamics
Blackwell's Approachability with Approximation Algorithms
Condition-number-independent Convergence Rate of Riemannian Hamiltonian Monte Carlo with Numerical Integrators
On the Complexity of Multi-Agent Decision Making: From Learning in Games to Partial Monitoring
Universally Instance-Optimal Mechanisms for Private Statistical Estimation
Corralling a Band of Bandit Algorithms
Time-Space Tradeoffs for Learning Finite Functions from Random Evaluations, with Applications to Polynomials
Computing Optimal Regularizers for Online Linear Optimization
Learning Switching Concepts
On the Sample Complexity of Finding Good Search Strategies
Hardness of Agnostically Learning Halfspaces from Worst-Case Lattice Problems
Thompson Sampling for Learning Parameterized Markov Decision Processes
On-Line Learning with Imperfect Monitoring
On the Optimal Capacity of Binary Neural Networks: Rigorous Combinatorial Approaches
The Optimal Approximation Factor in Density Estimation
Smooth e-Intensive Regression by Loss Symmetrization
Teachers, Learners and Black Boxes
Sparse PCA Beyond Covariance Thresholding
Achieving the Bayes Error Rate in Stochastic Block Model by SDP, Robustly
An Information-Theoretic Approach to Minimax Regret in Partial Monitoring
Learning in Neural Networks
Open Problem: Second order regret bounds based on scaling time
Anytime Acceleration of Gradient Descent
Estimating a Boolean Perceptron from Its Average Satisfying Assignment: A Bound on the Precision Required
On the Power of Membership Queries in Agnostic Learning
A Local Convergence Theory for Mildly Over-Parameterized Two-Layer Neural Network
Black-Box Reductions for Decentralized Online Convex Optimization in Changing Environments
Adaptive Crowdsourcing Algorithms for the Bandit Survey Problem
Horizon-Independent Optimal Prediction with Log-Loss in Exponential Families
Exact Learning of Read- k Disjoint DNF and Not-So-Disjoint DNF
Online Learning for Global Cost Functions
An Open Problem Regarding the Convergence of Universal A Priori Probability
Subspace Detection: A Robust Statistics Formulation
Cross-Validation for Binary Classification by Real-Valued Functions: Theoretical Analysis
Robust Trainability of Single Neurons
Large Stepsize Gradient Descent for Logistic Loss: Non-Monotonicity of the Loss Improves Optimization Efficiency
Relations Between Probabilistic and Team One-Shot Learners (Extended Abstract)
Intrinsic Complexity of Learning Geometrical Concepts from Positive Data
Efficient improper learning for online logistic regression
Logistic Regression Regret: What's the Catch?
Fast Exact Matrix Completion with Finite Samples
Low-degree phase transitions for detecting a planted clique in sublinear time
On the Benefits of Large Learning Rates for Kernel Methods
Linear Algorithms for Online Multitask Classification
Efficient PAC Learning from the Crowd
Further Results on the Margin Distribution
Mixability and the Existence of Weak Complexities
When Can Two Unsupervised Learners Achieve PAC Separation?
Online Markov Decision Processes with Aggregate Bandit Feedback
Spherical Dimension
Entropy Bounds for Restricted Convex Hulls
The Sample Complexity of Simple Binary Hypothesis Testing
Faster Rates for Convex-Concave Games
Permutation Tests for Classification
Oracles and Queries that are Sufficient for Exact Learning (Extended Abstract)
Game Theory, On-Line Prediction and Boosting
Marginal Singularity, and the Benefits of Labels in Covariate-Shift
General Convergence Results for Linear Discriminant Updates
Discrepancy, Coresets, and Sketches in Machine Learning
Community Detection in the Hypergraph SBM: Optimal Recovery Given the Similarity Matrix
On the Structure of Degrees of Inferability
On the Intrinsic Complexity of Learning Recursive Functions
Open Problem: Is There a First-Order Method that Only Converges to Local Minimax Optima?
Finite Sample Analysis of Two-Timescale Stochastic Approximation with Applications to Reinforcement Learning
Better Private Distribution Testing by Leveraging Unverified Auxiliary Data
On the well-spread property and its relation to linear regression
Competing in the Dark: An Efficient Algorithm for Bandit Linear Optimization
Improving Random Projections Using Marginal Information
Learning Switch Configurations
(Nearly) Optimal Private Linear Regression for Sub-Gaussian Data via Adaptive Clipping
Abstract Combinatorial Characterizations of Exact Learning via Queries
The query complexity of sampling from strongly log-concave distributions in one dimension
An Efficient Bandit Algorithm for sqrt(T) Regret in Online Multiclass Prediction?
Minimax Relative Loss Analysis for Sequential Prediction Algorithms Using Parametric Hypotheses
Path Kernels and Multiplicative Updates
Pure Exploration of Multi-armed Bandit Under Matroid Constraints
Learning Logic Programs by Using the Product Homomorphism Method
Unifying Divergence Minimization and Statistical Inference Via Convex Duality
Learning Ising Models with Independent Failures
Geometric Lower Bounds for Distributed Parameter Estimation under Communication Constraints
Data-dependent Bounds with T-Optimal Best-of-Both-Worlds Guarantees in Multi-Armed Bandits using Stability-Penalty Matching
Geodesically convex M-estimation in metric spaces
The Geometry of Losses
Sharp analysis of EM for learning mixtures of pairwise differences
Fast Mixing for Discrete Point Processes
Tracking a Small Set of Experts by Mixing Past Posteriors
Learning Arithmetic Circuits via Partial Derivatives
Localized Upper and Lower Bounds for Some Estimation Problems
Beyond Hartigan Consistency: Merge Distortion Metric for Hierarchical Clustering
Occam's Razor for Functions
Closure Properties for Private Classification and Online Prediction
Minimax Bounds for Active Learning
Learning Distributions from Random Walks
Inferring Descriptive Generalisations of Formal Languages
Faster Low-Rank Approximation and Kernel Ridge Regression via the Block-Nyström Method
Kernels and Regularization on Graphs
Optimal learning via local entropies and sample compression
Convergence to Nearly Minimal Size Grammars by Vacillating Learning Machines
Maximum Entropy Distribution Estimation with Generalized Regularization
On Communication Complexity of Classification Problems
Empirical Bayes via ERM and Rademacher complexities: the Poisson model
Certified Computation from Unreliable Datasets
Planting trees in graphs, and finding them back
Local Glivenko-Cantelli
Spike-and-Slab Posterior Sampling in High Dimensions
Learning with Equivalence Constraints and the Relation to Multiclass Learning
Probability is More Powerful Than Team for Language Identification from Positive Data
Top-K ranking with a monotone adversary
Adaptively Tracking the Best Bandit Arm with an Unknown Number of Distribution Changes
Some Constructions of Private, Efficient, and Optimal K-Norm and Elliptic Gaussian Noise
The Vertex Sample Complexity of Free Energy is Polynomial
Online Learning to Transport via the Minimal Selection Principle
Bridging the Gap: Rademacher Complexity in Robust and Standard Generalization
Logistic Regression, AdaBoost and Bregman Distances
Open Problem: Average-Case Hardness of Hypergraphic Planted Clique Detection
Efficient Bandit Convex Optimization: Beyond Linear Losses
Online Learning with Vector Costs and Bandits with Knapsacks
Learning and 1-bit Compressed Sensing under Asymmetric Noise
Predicting the Labelling of a Graph via Minimum $p$-Seminorm Interpolation
Last-iterate Convergence of Decentralized Optimistic Gradient Descent/Ascent in Infinite-horizon Competitive Markov Games
Improved Parallel Algorithm for Minimum Cost Submodular Cover Problem
The Minimax Strategy for Gaussian Density Estimation. pp
A Theoretical Analysis of Query Selection for Collaborative Filtering
Following the Flattened Leader
Gap-Free Clustering: Sensitivity and Robustness of SDP
Learning Structurally Reversible Context-Free Grammars from Queries and Counterexamples in Polynomial Time
List Decodable Subspace Recovery
On-line Variance Minimization in O(n2) per Trial?
Strongly Non-U-Shaped Learning Results by General Techniques
Transformation of Probabilistic Learning Strategies into Deterministic Learning Strategies
Interactive Fingerprinting Codes and the Hardness of Preventing False Discovery
(Nearly) Dimension Independent Private ERM with AdaGrad Ratesvia Publicly Estimated Subspaces
Open Problem: Risk of Ruin in Multiarmed Bandits
Gradient descent algorithms for Bures-Wasserstein barycenters
Open problem: Convergence of single-timescale mean-field Langevin descent-ascent for two-player zero-sum games
Mode Estimation with Partial Feedback
Learning Linear Threshold Functions in the Presence of Classification Noise
Reducing the Number of Queries in Self-Directed Learning
Community detection in the hypergraph stochastic block model and reconstruction on hypertrees
Competing With Strategies
Bandit Convex Optimization: \(\sqrt{T}\) Regret in One Dimension
Bounded Parameter Markov Decision Processes with Average Reward Criterion
Embedding Dimension of Polyhedral Losses
On-Line Learning Algorithms for Path Experts with Non-Additive Losses
Beating the Adaptive Bandit with High Probability
Ranking with a P-Norm Push
Multitask Learning with Expert Advice
Insufficient Statistics Perturbation: Stable Estimators for Private Least Squares Extended Abstract
Learning Near-Optimal Policies with Bellman-Residual Minimization Based Fitted Policy Iteration and a Single Sample Path
Exponential savings in agnostic active learning through abstention
Efficient median of means estimator
Asymptotic Log-Loss of Prequential Maximum Likelihood Codes
Open Problem: Lower bounds for Boosting with Hadamard Matrices
The Role of Interactivity in Structured Estimation
Extending Learnability to Auxiliary-Input Cryptographic Primitives and Meta-PAC Learning
Boosting with Diverse Base Classifiers
Efficiency of conformalized ridge regression
An Almost Optimal PAC Algorithm
On Almost Sure Convergence Rates of Stochastic Gradient Methods
On Spectral Learning of Mixtures of Distributions
The Sample Complexity of Robust Covariance Testing
An Improved On-line Algorithm for Learning Linear Evaluation Functions
Generalized SMO-Style Decomposition Algorithms
A Closer Look at Small-loss Bounds for Bandits with Graph Feedback
On Testing and Learning Quantum Junta Channels
The EM Algorithm gives Sample-Optimality for Learning Mixtures of Well-Separated Gaussians
Finite Time Analysis of Linear Two-timescale Stochastic Approximation with Markovian Noise
Stochastic Approximation of Smooth and Strongly Convex Functions: Beyond the $O(1/T)$ Convergence Rate
Learning Branches and Learning to Win Closed Games
The Real Price of Bandit Information in Multiclass Classification
A Rank-1 Sketch for Matrix Multiplicative Weights
Fast Algorithms for a New Relaxation of Optimal Transport
Learning in Parallel
Active Learning on Trees and Graphs
Continuous Drifting Games
Characterizing Dependence of Samples along the Langevin Dynamics and Algorithms via Contraction of Φ-Mutual Information (Extended Abstract)
The Optimality of Polynomial Regression for Agnostic Learning under Gaussian Marginals in the SQ Model
The One-Inclusion Graph Algorithm is not Always Optimal
A Unified Analysis of Nonstochastic Delayed Feedback for Combinatorial Semi-Bandits, Linear Bandits, and MDPs
The Convergence Rate of AdaBoost
Online Isotonic Regression
A Data Prism: Semi-verified learning in the small-alpha regime
Partitioning Well-Clustered Graphs: Spectral Clustering Works!
Dropping Convexity for Faster Semi-definite Optimization
Uniqueness of Ordinal Embedding
Adaptive Aggregation for Reinforcement Learning with Efficient Exploration: Deterministic Domains
From Pseudorandomness to Multi-Group Fairness and Back
Asymptotically Optimal Information-Directed Sampling
A Second-Order Perceptron Algorithm
Relative Expected Instantaneous Loss Bounds
Universal Online Learning with Bounded Loss: Reduction to Binary Classification
On-Line Portfolio Selection
PAC Bounds for Multi-armed Bandit and Markov Decision Processes
The Query Complexity of Finding Local Minima in the Lattice
Near-Optimal Methods for Minimizing Star-Convex Functions and Beyond
Algorithms and Hardness for Robust Subspace Recovery
Memory-Limited U-Shaped Learning
Approximately Stationary Bandits with Knapsacks
Sampling from the Mean-Field Stationary Distribution
Accuracy-Memory Tradeoffs and Phase Transitions in Belief Propagation
Calculation of the Learning Curve of Bayes Optimal Classification Algorithm for Learning a Perceptron With Noise
Fast and Optimal Prediction on a Labeled Tree
Spectral Estimators for Structured Generalized Linear Models via Approximate Message Passing (Extended Abstract)
New Lower Bounds for Testing Monotonicity and Log Concavity of Distributions
A Randomized Approximation of the MDL for Stochastic Models with Hidden Variables
Towards Instance Optimal Bounds for Best Arm Identification
Parallels Between Phase Transitions and Circuit Complexity?
On Mean Estimation for General Norms with Statistical Queries
Scale-free Unconstrained Online Learning for Curved Losses
Kernel and Rich Regimes in Overparametrized Models
Principal Component Analysis with Contaminated Data: The High Dimensional Case
The Gradient Complexity of Linear Regression
Uniform Stability for First-Order Empirical Risk Minimization
Adaptive Submodularity: A New Approach to Active Learning and Stochastic Optimization
Improved Minimax Bounds on the Test and Training Distortion of Empirically Designed Vector Quantizers
Learning Neural Networks with Two Nonlinear Layers in Polynomial Time
A Theory for Memory-Based Learning
PAC Analogues of Perceptron and Winnow via Boosting the Margin
More Efficient Internal-Regret-Minimizing Algorithms
Instance-Dependent Regret Bounds for Learning Two-Player Zero-Sum Games with Bandit Feedback
Multitask Learning via Shared Features: Algorithms and Hardness
Conference on Learning Theory, COLT 2020, 9-12 July 2020, Virtual Event [Graz, Austria]
Open Problem: Parameter-Free and Scale-Free Online Algorithms
Decision Making in Hybrid Environments: A Model Aggregation Approach
Near Optimal Heteroscedastic Regression with Symbiotic Learning
Uniform concentration and symmetrization for weak interactions
Simultaneous Learning of Concepts and Simultaneous Estimation of Probabilities
Multiplayer Bandit Learning, from Competition to Cooperation
Graph Learning with a Nearest Neighbor Approach
Learning With Hints
On Equivalence of Martingale Tail Bounds and Deterministic Regret Inequalities
Norm-Based Capacity Control in Neural Networks
Learning Rotations
Adaptive and Self-Confident On-Line Learning Algorithms
The Budgeted Multi-armed Bandit Problem
Keeping the Neural Networks Simple by Minimizing the Description Length of the Weights
On Learning Monotone DNF under Product Distributions
On the Learnability of Finite Automata
Detection-Recovery Gap for Planted Dense Cycles
Learning Probabilistic Read-Once Formulas on Product Distributions
Span-Agnostic Optimal Sample Complexity and Oracle Inequalities for Average-Reward RL
Active Learning for Smooth Problems
Learning 2µ DNF Formulas and kµ Decision Trees
Proceedings of the 29th Conference on Learning Theory, COLT 2016, New York, USA, June 23-26, 2016
PAC Learning Axis-Aligned Rectangles with Respect to Product Distributions from Multiple-Instance Examples
Learning Classes of Probabilistic Automata
A Statistical Taylor Theorem and Extrapolation of Truncated Densities
Analysis of Greedy Expert Hiring and an Application to Memory-Based Learning (Extended Abstract)
U-Calibration: Forecasting for an Unknown Agent
Vox Populi: Collecting High-Quality Labels from a Crowd
Exponentially Improved Dimensionality Reduction for l1: Subspace Embeddings and Independence Testing
Differentially Private Algorithms for the Stochastic Saddle Point Problem with Optimal Rates for the Strong Gap
On Self-Directed Learning
A Second-order Look at Stability and Generalization
Logarithmic Regret Algorithms for Online Convex Optimization
Computation-information gap in high-dimensional clustering
Bad Universal Priors and Notions of Optimality
Computational Lower Bounds for Community Detection on Random Graphs
Graphical Economics
Efficient Algorithms for Online Decision Problems
Learning Random Monotone DNF Under the Uniform Distribution
Optimal Algorithms for the Coin Weighing Problem with a Spring Scale
On the Complexity of Learning with Kernels
Open Problem: Approximate Planning of POMDPs in the class of Memoryless Policies
Learning Internal Representations
High probability generalization bounds for uniformly stable algorithms with nearly optimal rate
Information Theoretic Optimal Learning of Gaussian Graphical Models
More Theorems about Scale-sensitive Dimensions and Learning
Dominating Distributions and Learnability
Learning Overcomplete Latent Variable Models through Tensor Methods
Corruption-Robust Contextual Search through Density Updates
Learning from Censored and Dependent Data: The case of Linear Dynamics
Toward L_∞Recovery of Nonlinear Functions: A Polynomial Sample Complexity Bound for Gaussian Random Fields
The Power of Vacillation
Bounds on Sample Size for Policy Evaluation in Markov Environments
Precise Asymptotic Analysis of Deep Random Feature Models
Random Graph Matching with Improved Noise Robustness
Deterministic Sparse Fourier Approximation via Fooling Arithmetic Progressions
Topological Expressivity of ReLU Neural Networks
A Sequential Approximation Bound for Some Sample-Dependent Convex Optimization Problems with Applications in Learning
Geometric Bounds for Generalization in Boosting
Learnability of Bipartite Ranking Functions
Open problem: Improper learning of mixtures of Gaussians
Structural Results about Exact Learning with Unspecified Attribute Values
Lasso with Latents: Efficient Estimation, Covariate Rescaling, and Computational-Statistical Gaps
Genetic Algorithms and Machine Learning
Online Variance Reduction for Stochastic Optimization
Inference on Strongly Identified Functionals of Weakly Identified Functions
On the Complexity of Teaching
Conference On Learning Theory, COLT 2018, Stockholm, Sweden, 6-9 July 2018
Streaming Algorithms for Ellipsoidal Approximation of Convex Polytopes
Non-asymptotic Analysis of Biased Stochastic Approximation Scheme
Faster Algorithms for High-Dimensional Robust Covariance Estimation
Geometric Parameters of Kernel Machines
Extension of the PAC Framework to Finite and Countable Markov Chains
Computational Limitations in Robust Classification and Win-Win Results
Transductive Rademacher Complexity and Its Applications
Tutorial: Machine Learning Methods in Natural Language Processing
Some Label Efficient Learning Results
Quadratic Upper Bound for Recursive Teaching Dimension of Finite VC Classes
Lipschitz Adaptivity with Multiple Learning Rates in Online Learning
Online with Spectral Bounds
Belief propagation, robust reconstruction and optimal recovery of block models
Tighter Bounds for Multi-Armed Bandits with Expert Advice
Multilevel Optimization for Inverse Problems
A Loss Bound Model for On-Line Stochastic Prediction Strategies
Piecemeal Graph Exploration by a Mobile Robot (Extended Abstract)
Properly Learning Poisson Binomial Distributions in Almost Polynomial Time
Language Learning in Dependence on the Space of Hypotheses
Attribute-Efficient Learning in Query and Mistake-Bound Models
When does gradient descent with logistic loss interpolate using deep networks with smoothed ReLU activations?
Tight (Lower) Bounds for the Fixed Budget Best Arm Identification Bandit Problem
Active Tolerant Testing
Improved Discretization Analysis for Underdamped Langevin Monte Carlo
Exponential Convergence Time of Gradient Descent for One-Dimensional Deep Linear Neural Networks
Data-Dependent Margin-Based Generalization Bounds for Classification
Maximal Margin Classification for Metric Spaces
Predictive Hebbian Learning
Learning Multivariate Log-concave Distributions
Privately Learning High-Dimensional Distributions
Conference on Learning Theory 2015: Preface
Exploiting Random Walks for Learning
Learning Simple Deterministic Languages
Finite-Time Analysis of Asynchronous Stochastic Approximation and $Q$-Learning
Domain Compression and its Application to Randomness-Optimal Distributed Goodness-of-Fit
Predicting a Binary Sequence Almost As Well As the Optimal Biased Coin
Learning Probabilistic Prediction Functions
Optimal Stragies and Minimax Lower Bounds for Online Convex Games
Approximate Testing and Learnability
An Inequality for Nearly Log-Concave Distributions with Applications to Learning