yusuke1997/dblp-id-title huggingface dataset:
Computationally and Statistically Efficient Truncated Regression
Impossibility of Partial Recovery in the Graph Alignment Problem
Lazy OCO: Online Convex Optimization on a Switching Budget
Complexity Guarantees for Polyak Steps with Momentum
A Generalized Representer Theorem
Teaching Dimensions based on Cooperative Learning
Tight Bounds on the Hardness of Learning Simple Nonparametric Mixtures
Efficient Agnostic PAC-Learning with Simple Hypothesis
Gaussian Process Optimization with Adaptive Sketching: Scalable and No Regret
Convex Games in Banach Spaces
PAC learning with stable and private predictions
The title of the paper
Estimation of smooth densities in Wasserstein distance
A Second-Order Method for Stochastic Bandit Convex Optimisation
Truthful Linear Regression
Averaging Stochastic Gradient Descent on Riemannian Manifolds
On Learning vs. Refutation
Fast Rates for Structured Prediction
Intrinsic dimensionality and generalization properties of the R-norm inductive bias
Towards a Theoretical Foundation for Laplacian-Based Manifold Methods
Accelerated SGD for Non-Strongly-Convex Least Squares
Approximate Guarantees for Dictionary Learning
A Computational Model of Teaching
Aggregation of supports along the Lasso path
Learning general Gaussian mixtures with efficient score matching
Robust Regression by Boosting the Median
Nonparametric Bandits with Covariates
The Importance of Convexity in Learning with Squared Loss
Regret to the Best vs. Regret to the Average
Learning with Queries but Incomplete Information (Extended Abstract)
Inductive Principles of the Search for Empirical Dependences (Methods Based on Weak Convergence of Probability Measures)
On Exact Specification by Examples
Lattice-Based Methods Surpass Sum-of-Squares in Clustering
On the Learnability and Design of Output Codes for Multiclass Problems
Linear Bandits on Ellipsoids: Minimax Optimal Algorithms
Agnostic Learning of Geometric Patterns (Extended Abstract)
Lower Bounds for Higher-Order Convex Optimization
Breaking the Lower Bound with (Little) Structure: Acceleration in Non-Convex Stochastic Optimization with Heavy-Tailed Noise
Reducibility and Computational Lower Bounds for Problems with Planted Sparse Structure
The Externalities of Exploration and How Data Diversity Helps Exploitation
Generalization for Adaptively-chosen Estimators via Stable Median
Learning Read-Once Formulas Using Membership Queries
Machine Unlearning via Algorithmic Stability
Criteria for Specifying Machine Complexity in Learning
Optimal Probability Estimation with Applications to Prediction and Classification
Ranking and Scoring Using Empirical Risk Minimization
Polynomial Learnability of Linear Threshold Approximations
Fast, blind, and accurate: Tuning-free sparse regression with global linear convergence
Kernel Thinning
Acceleration of Learning in Binary Choice Problems
The More, the Merrier: the Blessing of Dimensionality for Learning Large Gaussian Mixtures
On the sample complexity of parameter estimation in logistic regression with normal design
Exploring Facets of Language Generation in the Limit
Learning Patterns for Detection with Multiscale Scan Statistics
Bandits with Movement Costs and Adaptive Pricing
Nonparametric Regression with Shallow Overparameterized Neural Networks Trained by GD with Early Stopping
Cautiously Optimistic Policy Optimization and Exploration with Linear Function Approximation
The Robustness of the p -Norm Algorithms
Outlier-Robust Learning of Ising Models Under Dobrushin's Condition
Provable Benefits of Representational Transfer in Reinforcement Learning
Low-rank fine-tuning lies between lazy training and feature learning
A Parametrization Scheme for Classifying Models of Learnability
Optimal Rates for Regularized Least Squares Regression
The Expressive Power of Tuning Only the Normalization Layers
Sparse Regression Learning by Aggregation and Langevin Monte-Carlo
Proceedings of The 28th Conference on Learning Theory, COLT 2015, Paris, France, July 3-6, 2015
Algorithms for Lipschitz Learning on Graphs
On Query-efficient Planning in MDPs under Linear Realizability of the Optimal State-value Function
Inferring Graphs from Walks
Learning the dependence structure of rare events: a non-asymptotic study
An information-theoretic lower bound in time-uniform estimation
Online Learning Without Prior Information
Separating PAC and Mistake-Bound Learning Models Over the Boolean Domain (Abstract)
Density-preserving quantization with application to graph downsampling
Generalization Guarantees via Algorithm-dependent Rademacher Complexity
On the Performance of Empirical Risk Minimization with Smoothed Data
On the Computational Power of Neural Nets
On the Computability of Robust PAC Learning
Optimal Robust Estimation under Local and Global Corruptions: Stronger Adversary and Smaller Error
Generalised Entropy and Asymptotic Complexities of Languages
New Projection-free Algorithms for Online Convex Optimization with Adaptive Regret Guarantees
Pushing the Efficiency-Regret Pareto Frontier for Online Learning of Portfolios and Quantum States
Strong Gaussian Approximation for the Sum of Random Vectors
Community Recovery in the Degree-Heterogeneous Stochastic Block Model
Towards a Complete Analysis of Langevin Monte Carlo: Beyond Poincaré Inequality
Label optimal regret bounds for online local learning
Resource Bounded Next Value and Explanatory Identification: Learning Automata, Patterns and Polynomials On-Line
Competing with the Empirical Risk Minimizer in a Single Pass
U-Shaped, Iterative, and Iterative-with-Counter Learning
Regularized Linear Regression: A Precise Analysis of the Estimation Error
Data Selection for ERMs
Nearly Optimal Sampling Algorithms for Combinatorial Pure Exploration
Open Problem: The Statistical Query Complexity of Learning Sparse Halfspaces
Computational Learning Theory and Kernel Machines, 16th Annual Conference on Computational Learning Theory and 7th Kernel Workshop, COLT/Kernel 2003, Washington, DC, USA, August 24-27, 2003, Proceedings
Subset Ranking Using Regression
On Learning Bounded-Width Branching Programs
Fast determinantal point processes via distortion-free intermediate sampling
Learning by Distances
How catastrophic can catastrophic forgetting be in linear regression?
When Oracles Do Not Help
Internal Regret in On-Line Portfolio Selection
Relating Clustering Stability to Properties of Cluster Boundaries
Heavy-tailed Estimation is Easier than Adversarial Contamination
Equivalence Queries and Approximate Fingerprints
Robustness and Generalization
Agnostic Boosting
On the Approximability of Sparse PCA
Online Learning Meets Optimization in the Dual
Coordination without communication: optimal regret in two players multi-armed bandits
Combining Expert Advice Efficiently
Regret Analysis of the Finite-Horizon Gittins Index Strategy for Multi-Armed Bandits
Blind Signal Separation in the Presence of Gaussian Noise
Local Complexities for Empirical Risk Minimization
Estimation and Approximation Bounds for Gradient-Based Reinforcement Learning
Types of Monotonic Language Learning and Their Characterization
Greed Is Good: Near-Optimal Submodular Maximization via Greedy Optimization
Modeling from Features: a Mean-field Framework for Over-parameterized Deep Neural Networks
Learning Integer Lattices
Preference-based Teaching
The Cross Validation Problem
Sparse Recovery in Convex Hulls of Infinite Dictionaries
lil' UCB : An Optimal Exploration Algorithm for Multi-Armed Bandits
Making SGD Parameter-Free
Strong Entropy Concentration, Game Theory, and Algorithmic Randomness
Statistical-Computational Tradeoffs in Mixed Sparse Linear Regression
Information-Theoretic Thresholds for the Alignments of Partially Correlated Graphs
Learning Additive Models Online with Fast Evaluating Kernels
On the Role of Search for Learning
Escaping the Local Minima via Simulated Annealing: Optimization of Approximately Convex Functions
Open Problem: Optimal Instance-Dependent Sample Complexity for finding Nash Equilibrium in Two Player Zero-Sum Matrix games
Learning and testing junta distributions with sub cube conditioning
Learning Bounds for Support Vector Machines with Learned Kernels
Improving Algorithms for Boosting
Online learning in repeated auctions
SGD in the Large: Average-case Analysis, Asymptotics, and Stepsize Criticality
Recovering Labels from Crowdsourced Data: an Optimal and Polynomial-Time Method
Learning Regular Sets with an Incomplete Membership Oracle
Learning to Stop with Surprisingly Few Samples
Refined Sample Complexity for Markov Games with Independent Linear Function Approximation (Extended Abstract)
A Model of Sequence Extrapolation
Sample complexity of population recovery
Learning Communities in the Presence of Errors
Regret Minimization With Concept Drift
Learning Automata from Ordered Examples
Combinatorial Prediction Markets
Open Problem: Is Margin Sufficient for Non-Interactive Private Distributed Learning?
Finding a most biased coin with fewest flips
Efficient Online Linear Control with Stochastic Convex Costs and Unknown Dynamics
Open Problem: Fixed-Parameter Tractability of Zonotope Problems
Gaussian Cooling and Dikin Walks: The Interior-Point Method for Logconcave Sampling
Compressing to VC Dimension Many Points
Efficient Contextual Bandits in Non-stationary Worlds
Active Local Learning
Policy Optimization for Stochastic Shortest Path
A Statistical Mechanics Analysis of Gram Matrix Eigenvalue Spectra
The entropic barrier: a simple and optimal universal self-concordant barrier
Contextual Bandits with Packing and Covering Constraints: A Modular Lagrangian Approach via Regression
The estimation error of general first order methods
Learning Halfspaces with Massart Noise Under Structured Distributions
Estimation and Inference with Trees and Forests in High Dimensions
Surrogate Regret Bounds for the Area Under the ROC Curve via Strongly Proper Losses
Polylogarithmic-Overhead Piecemeal Graph Exploration
Information Directed Sampling for Linear Partial Monitoring
On the Distance from Calibration in Sequential Prediction
A Convex Formulation for Mixed Regression with Two Components: Minimax Optimal Rates
Self-Consistency of the Fokker Planck Equation
Accelerated and Sparse Algorithms for Approximate Personalized PageRank and Beyond
Optimal Average-Case Reductions to Sparse PCA: From Weak Assumptions to Strong Hardness
Testing of Index-Invariant Properties in the Huge Object Model
Sequence Prediction Based on Monotone Complexity
Computing High-dimensional Confidence Sets for Arbitrary Distributions
Fast parallel sampling under isoperimetry
Breaking the Curse of Multiagency: Provably Efficient Decentralized Multi-Agent RL with Function Approximation
A Framework for Statistical Clustering with a Constant Time Approximation Algorithms for K-Median Clustering
A Nearly Tight Bound for Fitting an Ellipsoid to Gaussian Random Points
Open Problem: Is There an Online Learning Algorithm That Learns Whenever Online Learning Is Possible?
Learning Polynomials in Few Relevant Dimensions
Regularized Dikin Walks for Sampling Truncated Logconcave Measures, Mixed Isoperimetry and Beyond Worst-Case Analysis
Geometric Barriers for Stable and Online Algorithms for Discrepancy Minimization
Majorizing Measures, Sequential Complexities, and Online Learning
A Note on VC-Dimension and Measures of Sets of Reals
CLASSIC Learning
Conservativeness and Monotonicity for Learning Algorithms
Private Sequential Learning
Counting Stars is Constant-Degree Optimal For Detecting Any Planted Subgraph: Extended Abstract
The Implicit Bias of Benign Overfitting
On-Line Estimation with the Multivariate Gaussian Distribution
Open Problem: First-Order Regret Bounds for Contextual Bandits
On Learning Noisy Threshold Functions with Finite Precision Weights
Learnability by Fixed Distributions
Reconstructing Trees from Traces
Variations on U-Shaped Learning
Breaking the $1/\sqrtn$ Barrier: Faster Rates for Permutation-based Models in Polynomial Time
From Graphs to Manifolds - Weak and Strong Pointwise Consistency of Graph Laplacians
Hardness of Learning Noisy Halfspaces using Polynomial Thresholds
List Online Classification
Dimension-free Structured Covariance Estimation
Sharper Bounds for Chebyshev Moment Matching, with Applications
On the Convergence of MDL Density Estimation
Learning Boolean Read-Once Formulas with Arbitrary Symmetric and Constant Fan-in Gates
Adaptive Hausdorff Estimation of Density Level Sets
Finding Low Error Clusterings
Online Similarity Prediction of Networked Data from Known and Unknown Graphs
Degrees of Inferability
Universal Well-Calibrated Algorithm for On-Line Classification
Efficient decentralized multi-agent learning in asymmetric queuing systems
Fast Mean Estimation with Sub-Gaussian Rates
ODE-Inspired Analysis for the Biological Version of Oja's Rule in Solving Streaming PCA
Making the Last Iterate of SGD Information Theoretically Optimal
Mansour's Conjecture is True for Random DNF Formulas
Online Stackelberg Optimization via Nonlinear Control
Active Regression via Linear-Sample Sparsification
Extracting Certainty from Uncertainty: Regret Bounded by Variation in Costs
On Approximate Truth
Active Coverage for PAC Reinforcement Learning
The Space Complexity of Learning-Unlearning Algorithms (extended abstract)
Combining Online Learning Guarantees
Maximum Entropy Distributions: Bit Complexity and Stability
Improved Rates for the Stochastic Continuum-Armed Bandit Problem
Learning and inference in the presence of corrupted inputs
DNF - If You Can't Learn'em, Teach'em: An Interactive Model of Teaching
Polynomial low degree hardness for Broadcasting on Trees (Extended Abstract)
Fast matrix completion without the condition number
Polynomial Time Inference of a Subclass of Context-Free Transformations
Contextual bandits with continuous actions: Smoothing, zooming, and adapting
Learning Disjunctions of Predicates
The Perceptron Algorithm vs. Winnow: Linear vs. Logarithmic Mistake Bounds when few Input Variables are Relevant
Offline Reinforcement Learning with Realizability and Single-policy Concentrability
Teaching Dimension and the Complexity of Active Learning
On the Limitations of Embedding Methods
Learning Markov Chains with Variable Memory Length from Noisy Output
The Bethe and Sinkhorn Permanents of Low Rank Matrices and Implications for Profile Maximum Likelihood
Exact Learning Composed Classes with a Small Number of Mistakes
Achieving All with No Parameters: AdaNormalHedge
Reasoning About Generalization via Conditional Mutual Information
Smoothed Online Learning is as Easy as Statistical Learning
Mixing Time of the Proximal Sampler in Relative Fisher Information via Strong Data Processing Inequality (Extended Abstract)
Iterate Averaging as Regularization for Stochastic Gradient Descent
The Role of Critical Sets in Vapnik-Chervonenkis Theory
Open Problem: Tensor Decompositions: Algorithms up to the Uniqueness Threshold?
Learning without concentration
Instance-Optimality in Interactive Decision Making: Toward a Non-Asymptotic Theory
Learning DNF over the Uniform Distribution using a Quantum Example Oracle
Optimal Learning via the Fourier Transform for Sums of Independent Integer Random Variables
PAB-Decisions for Boolean and Real-Valued Features
Quantum State and Unitary Learning Implies Circuit Lower Bounds
Superconstant Inapproximability of Decision Tree Learning
Sample Complexity Bounds on Differentially Private Learning via Communication Complexity
Unleashing Linear Optimizers for Group-Fair Learning and Optimization
Two-Sided Weak Submodularity for Matroid Constrained Optimization and Regression
Pan-Private Uniformity Testing
Benign Overfitting without Linearity: Neural Network Classifiers Trained by Gradient Descent for Noisy Linear Data
Softmax Policy Gradient Methods Can Take Exponential Time to Converge
On Learning Binary Weights for Majority Functions
Proceedings of the Third Annual Workshop on Computational Learning Theory, COLT 1990, University of Rochester, Rochester, NY, USA, August 6-8, 1990
Structure-agnostic Optimality of Doubly Robust Learning for Treatment Effect Estimation (Extended Abstract)
Corrigendum for "Learnability of Description Logics"
A Leave-One-out Cross Validation Bound for Kernel Methods with Applications in Learning
When can unlabeled data improve the learning rate?
Comparing Clusterings by the Variation of Information
On sampling diluted Spin-Glasses using Glauber Dynamics
Model-Based Reinforcement Learning with a Generative Model is Minimax Optimal
Convergence rates and approximation results for SGD and its continuous-time counterpart
Learning from a Consistently Ignorant Teacher
Solving SDPs for synchronization and MaxCut problems via the Grothendieck inequality
On the Stability of Random Matrix Product with Markovian Noise: Application to Linear Stochastic Approximation and TD Learning
Efficient Learning of Continuous Neural Networks
Low coordinate degree algorithms II: Categorical signals and generalized stochastic block models
Reaching Kesten-Stigum Threshold in the Stochastic Block Model under Node Corruptions
Non-asymptotic Analysis for Nonparametric Testing
Learning Context-Free Grammars from Structural Data in Polynomial Time
Learning Monotone Log-Term DNF Formulas
Efficient active learning of sparse halfspaces
Reducing Kernel Matrix Diagonal Dominance Using Semi-definite Programming
Batch-Size Independent Regret Bounds for the Combinatorial Multi-Armed Bandit Problem
Learning a set of directions
Generalization versus Classification
Efficient Projection-Free Online Convex Optimization with Membership Oracle
On the Role of Procrastination for Machine Learning
Online Learning Guided Curvature Approximation: A Quasi-Newton Method with Global Non-Asymptotic Superlinear Convergence
Fundamental Limits of Non-Linear Low-Rank Matrix Estimation
Adaptive Bandit Convex Optimization with Heterogeneous Curvature
Minimax Fixed-Design Linear Regression
Gradient Descent for One-Hidden-Layer Neural Networks: Polynomial Convergence and SQ Lower Bounds
Algorithmic Aspects of the Log-Laplace Transform and a Non-Euclidean Proximal Sampler
Logarithmic Width Suffices for Robust Memorization
Unconstrained Online Linear Learning in Hilbert Spaces: Minimax Algorithms and Normal Approximations
Correlation Clustering with Noisy Partial Information
Fine-Grained Gap-Dependent Bounds for Tabular MDPs via Adaptive Multi-Step Bootstrap
Algorithmic Chaining and the Role of Partial Feedback in Online Nonparametric Learning
Bandit Principal Component Analysis
Precise Tradeoffs in Adversarial Training for Linear Regression
Testable Learning of General Halfspaces with Adversarial Label Noise
Depth Separation for Neural Networks
Decision Making in Changing Environments: Robustness, Query-Based Learning, and Differential Privacy
General Polynomial Time Decomposition Algorithms
Optimization, Isoperimetric Inequalities, and Sampling via Lyapunov Potentials
Towards a Query-Optimal and Time-Efficient Algorithm for Clustering with a Faulty Oracle
Polynomial Time Prediction Strategy with Almost Optimal Mistake Probability
Self-Optimizing and Pareto-Optimal Policies in General Environments Based on Bayes-Mixtures
A Gap Between the Gaussian RKHS and Neural Networks: An Infinite-Center Asymptotic Analysis
Eigenspace Restructuring: A Principle of Space and Frequency in Neural Networks
Learning Stochastic Functions by Smooth Simultaneous Estimation
Robust Reductions from Ranking to Classification
Ultraconservative Online Algorithms for Multiclass Problems
Elementary Formal Systems, Intrinsic Complexity, and Procrastination
More Efficient PAC-Learning of DNF with Membership Queries Under the Uniform Distribution
Optimally Combining Classifiers Using Unlabeled Data
Polynomial Learnability of Semilinear Sets
Fundamental Limits of Matrix Sensing: Exact Asymptotics, Universality, and Applications
Self Bounding Learning Algorithms
Some easy optimization problems have the overlap-gap property
Approximate Equivalence of Markov Decision Processes
COLT 2012 - The 25th Annual Conference on Learning Theory, June 25-27, 2012, Edinburgh, Scotland
Language Learning from Texts: Mind Changes, Limited Memory and Monotonicity (Extended Abstract)
Spectral Estimators for Multi-Index Models: Precise Asymptotics and Optimal Weak Recovery
Learning Permutations with Exponential Weights
Model Selection by Bootstrap Penalization for Classification
Optimality and Approximation with Policy Gradient Methods in Markov Decision Processes
Open Problem: Restricted Eigenvalue Condition for Heavy Tailed Designs
A Direct Sum Result for the Information Complexity of Learning
Proceedings of the Fourth Annual Workshop on Computational Learning Theory, COLT 1991, Santa Cruz, California, USA, August 5-7, 1991
Optimal Rates of Aggregation
Tutorial: Learning Topics in Game-Theoretic Decision Making
On a Generalized Notion of Mistake Bounds
Multi-Observation Elicitation
Double Explore-then-Commit: Asymptotic Optimality and Beyond
On the Complexity of Bandit and Derivative-Free Stochastic Convex Optimization
Efficient Algorithms for Sparse Moment Problems without Separation
Truthfulness of Decision-Theoretic Calibration Measures
A Proof of The Changepoint Detection Threshold Conjecture in Preferential Attachment Models
Ranking with Kernels in Fourier space
Are There Local Maxima in the Infinite-Sample Likelihood of Gaussian Mixture Estimation?
Minimax Policies for Adversarial and Stochastic Bandits
Learning Large-Alphabet and Analog Circuits with Value Injection Queries
Open Problem: Can Single-Shuffle SGD be Better than Reshuffling SGD and GD?
Stability vs Implicit Bias of Gradient Methods on Separable Data and Beyond
The Power of Depth for Feedforward Neural Networks
Asymptotically Optimal Generalization Error Bounds for Noisy, Iterative Algorithms
Existence of Adversarial Examples for Random Convolutional Networks via Isoperimetric Inequalities on $\mathbb{SO}(d)$
Robust Sparse Mean Estimation via Sum of Squares
Mind Change Optimal Learning of Bayes Net Structure
Aspects of Complexity of Conservative Probabilistic Learning
Artificial Constraints and Hints for Unbounded Online Learning
Counting Motifs with Graph Sampling
Faster Projection-free Online Learning
Learnability of Solutions to Conjunctive Queries: The Full Dichotomy
Square Hellinger Subadditivity for Bayesian Networks and its Applications to Identity Testing
Asymptotic behavior of \(\ell_p\)-based Laplacian regularization in semi-supervised learning
Realizable Learning is All You Need
Nearly Optimal Regret for Decentralized Online Convex Optimization
Distribution-Dependent Analysis of Gibbs-ERM Principle
Estimating Principal Components under Adversarial Perturbations
Learning Hidden Markov Models Using Conditional Samples
Fast, Sample-Efficient, Affine-Invariant Private Mean and Covariance Estimation for Subgaussian Distributions
Ticketed Learning-Unlearning Schemes
On the Sample Complexity of PAC-Learning Using Random and Chosen Examples
Mirror Descent Algorithms with Nearly Dimension-Independent Rates for Differentially-Private Stochastic Saddle-Point Problems extended abstract
On the Performance of Thompson Sampling on Logistic Bandits
Benign Overfitting of Constant-Stepsize SGD for Linear Regression
Open Problem: Property Elicitation and Elicitation Complexity
Useful Total Quality Management Critical Success Fundamentals in Higher Education Institution
Enhancing User Experience with Embodied Cognition
Developing Safety Competence Process for Vocational Students
Customer eXperience: A Bridge Between Service Science and Human-Computer Interaction
Product Function Analysis: Reducing Cost of Production Service Line with Work Teams
Alternative Ensemble Classifier Based on Penalty Strategy for Improving Prediction Accuracy
ERGO@OFFICE: A Participatory Ergonomics Approach for Strategic Interventions and Prevention of Musculoskeletal Disorders in SMEs
Analysis on IoT Activities in Japanese Companies: Toward Innovation or Cost Reduction
The Impact of Ergonomic Design on Smart Garments
The Human-Tech Matrix: A Socio-Technical Approach to Evaluation of Automated Transport Systems
Design for Seniors: A Case Study Based on Human Centric Lighting
Human Machine Interface Issues for Drone Fleet Management
Modularity in Seaport Management: Identification of the Research Problem