yusuke1997/dblp-id-title huggingface dataset:
Leverage Score Sampling for Faster Accelerated Regression and ERM
Privately Answering Classification Queries in the Agnostic PAC Model
Soft-Bayes: Prod for Mixtures of Experts with Log-Loss
Algorithmic Learning Theory, ALT 2019, 22-24 March 2019, Chicago, Illinois, USA
Learning Efficiency of Very Simple Grammars from Positive Data
Dealing with Range Anxiety in Mean Estimation via Statistical Queries
Distances for Markov Chains, and Their Differentiation
RedEx: Beyond Fixed Representation Methods via Convex Optimization
Algorithmic Learning Theory - 25th International Conference, ALT 2014, Bled, Slovenia, October 8-10, 2014. Proceedings
A Map of Update Constraints in Inductive Inference
On Inductive Learning for Three Kinds of Data Structures
Corrupt Bandits for Preserving Local Privacy
Online Self-Concordant and Relatively Smooth Minimization, With Applications to Online Portfolio Selection and Learning Quantum States
Learnability of Probabilistic Automata via Oracles
Can Adversarially Robust Learning LeverageComputational Hardness?
Inductive Inference of Monogenic Pure Context-free Languages
Approximate Inference in Probabilistic Models
Generalized Unification as Background Knowledge in Learning Logic Programs
A Decomposition Based Induction Model for Discovering Concept Clusters from Databases
Derived Sets and Inductive Inference
A Generalized Neyman-Pearson Criterion for Optimal Domain Adaptation
Iterated Vector Fields and Conservatism, with Applications to Federated Learning
An Efficient Algorithm for Learning with Semi-bandit Feedback
Automatic Learning from Positive Data and Negative Counterexamples
{Multi-Player Bandits Revisited}
On the Learnability of Vector Spaces
Probabilistic Generalization of Simple Grammars and Its Application to Reinforcement Learning
On Computable Online Learning
On Paraphrasing-Based Analogical Reasoning
Analogical Reasoning Based on Higher-Order Unification
Bounding, Concentrating, and Truncating: Unifying Privacy Loss Composition for Data Analytics
A Unified Theory of Supervised Online Learnability
Which classes of elementary formal systems are polynomial-time learnable
Refining the Sample Complexity of Comparative Learning
Multi-task and Lifelong Learning of Kernels
Adaptive Sampling for Incremental Optimization Using Stochastic Gradient Descent
Algorithmic Learning Theory, 18th International Conference, ALT 2007, Sendai, Japan, October 1-4, 2007, Proceedings
Explanation-Based Reuse of Prolog Programs
Multi-task {K}ernel {L}earning Based on {P}robabilistic {L}ipschitzness
Algorithmic Learning Theory, 12th International Conference, ALT 2001, Washington, DC, USA, November 25-28, 2001, Proceedings
On Learning Unions of Pattern Languages and Tree Patterns
Algebraic Analysis for Singular Statistical Estimation
Distribution Free Learning with Local Queries
Mutual Information Gaining Algorithm and Its Relation to PAC-Learning Algorithm
A Typed Lambda-Calculus for Proving-by-Example and Bottom-Up Generalization Procedure
Extreme State Aggregation beyond MDPs
Average-Case Active Learning with Costs
Monotonicity versus Efficiency for Learning Languages from Texts
A New Algorithm for Automatic Configuration of Hidden Markov Models
On Learning and Co-learning of Minimal Programs
Simple PAC Learning of Simple Decision Lists
LIME: A System for Learning Relations
Exact Learning of Multivalued Dependencies
Dueling Optimization with a Monotone Adversary
Exact Learning of Finite Unions of Graph Patterns from Queries
Re-adapting the Regularization of Weights for Non-stationary Regression
An efficient query learning algorithm for zero-suppressed binary decision diagrams
PAC Learning Depth-3 $\textrm{AC}^0$ Circuits of Bounded Top Fanin
A Theory of Similarity Functions for Learning and Clustering
Online PCA with Optimal Regrets
Inductive inferability for formal languages from positive data
Learning Recursive Functions Refutably
Teachability in Computational Learning
Toward a Classification of Finite Partial-Monitoring Games
Universally Consistent Online Learning with Arbitrarily Dependent Responses
A Vector-Contraction Inequality for Rademacher Complexities
A Calculus for Logical Clustering
Efficient Pure Exploration for Combinatorial Bandits with Semi-Bandit Feedback
Distributed Online Learning for Joint Regret with Communication Constraints
Discovery of Abstract Concepts by a Robot
A Technical Note on Non-Stationary Parametric Bandits: Existing Mistakes and Preliminary Solutions
Learning Linearly Separable Languages
Cautious Limit Learning
Collaborative Clustering: Sample Complexity and Efficient Algorithms
On the Last Iterate Convergence of Momentum Methods
Attribute-Efficient Learning of Halfspaces with Malicious Noise: Near-Optimal Label Complexity and Noise Tolerance
CRIMED: Lower and Upper Bounds on Regret for Bandits with Unbounded Stochastic Corruption
Identifying Nearly Minimal Gödel Numbers From Additional Information
Algorithmic Learning Theory, 9th International Conference, ALT '98, Otzenhausen, Germany, October 8-10, 1998, Proceedings
Tight Bounds on ℓ 1 Approximation and Learning of Self-Bounding Functions
A Lower Bound for Learning Distributions Generated by Probabilistic Automata
An Algorithm for Iterative Selection of Blocks of Features
Online Learning of Facility Locations
Learnability of Relatively Quantified Generalized Formulas
General parallel optimization a without metric
Towards Realistic Theories of Learning
Dynamic Modeling in Inductive Inference
Identifying Regular Languages over Partially-Commutative Monoids
Application of Classical Nonparametric Predictors to Learning Conditionally I.I.D. Data
Team Learning as a Game
PAC Learning of Some Subclasses of Context-Free Grammars with Basic Distributional Properties from Positive Data
A Class of Prolog Programs Inferable from Positive Data
Online Non-Additive Path Learning under Full and Partial Information
Last-Iterate Convergence Rates for Min-Max Optimization: Convergence of Hamiltonian Gradient Descent and Consensus Optimization
Reconstructing Weighted Graphs with Minimal Query Complexity
General Discounting Versus Average Reward
Generalization Bounds for Some Ordinal Regression Algorithms
Partial Occam's Razor and Its Applications
Protein Secondary Structure Prediction Based on Stochastic-Rule Learning
Structured Neural Networks and Their Flash Learning
Maximum Entropy Principle in Non-ordered Setting
Finding Robust Nash equilibria
Learning Intermediate Concepts
An Efficient PAC Algorithm for Reconstructing a Mixture of Lines
First-Order Bayesian Regret Analysis of Thompson Sampling
Estimation of the Data Region Using Extreme-Value Distributions
A Chain Rule for the Expected Suprema of Gaussian Processes
Bandit Algorithms Based on Thompson Sampling for Bounded Reward Distributions
Monotone Conditional Complexity Bounds on Future Prediction Errors
Pure Exploration in Multi-armed Bandits Problems
PAC Learning Using Nadaraya-Watson Estimator Based on Orthonormal Systems
Differentially Private Assouad, Fano, and Le Cam
Efficient and Optimal Algorithms for Contextual Dueling Bandits under Realizability
Things Bayes Can't Do
Newton Diagram and Stochastic Complexity in Mixture of Binomial Distributions
Learning Orthogonal F -Horn Formulas
Learning Regular Languages Using RFSA
Adversarial Online Collaborative Filtering
Analogical Logic Program Synthesis Algorithm That Can Refute Inappropriate Similarities
Polynomial-time inference of unions of tree pattern languages
On Competitive Recommendations
On the Comparison of Inductive Inference Criteria for Uniform Learning of Finite Classes
Learning Equal Matrix Grammers and Multitape Automata with Structural Information
Bandit Online Optimization over the Permutahedron
Finding a One-Variable Pattern from Incomplete Data
Learning Decision Trees with Stochastic Linear Classifiers
On Exact Learning Monotone DNF from Membership Queries
Kernelization of Matrix Updates, When and How?
Learning by Switching Type of Information
Recent Developments in Pattern Mining
EBG and Term-Rewriting Systems
On the complexity of finding stationary points of smooth functions in one dimension
Improved Bounds about On-line Learning of Smooth Functions of a Single Variable
Online Learning of Combinatorial Objects via Extended Formulation
Limiting Behaviors of Nonconvex-Nonconcave Minimax Optimization via Continuous-Time Systems
A Fast Algorithm for Discovering Optimal String Patterns in Large Text Databases
Efficient Induction of Logic Programs
On the Sample Complexity for Neural Trees
A Regularization Approach to Metrical Task Systems
Multicalibrated Partitions for Importance Weights
Parameter identification in Markov chain choice models
Induction of Logic Programs Based on psi-Terms
Discovery Learning in Intelligent Tutoring Systems
Aggregating Algorithm for a Space of Analytic Functions
Changing the Inference Type - Keeping the Hypothesis Space
Non U-Shaped Vacillatory and Team Learning
Epsilon-Approximations of k -label Spaces
From Specifications to Programs: Induction in the Service of Synthesis (Abstract)
Complexity of Computing Vapnik-Chervonenkis Dimension
Proper Learnability and the Role of Unlabeled Data
Measuring Statistical Dependence with Hilbert-Schmidt Norms
Algorithmic Learning Theory, 20th International Conference, ALT 2009, Porto, Portugal, October 3-5, 2009. Proceedings
Analogy is NP-hard
Learning and Revising Theories in Noisy Domains
New Revision Algorithms
Application of Kolmogorov Complexity to Inductive Inference with Limited Memory
Bayesian Active Learning Using Arbitrary Binary Valued Queries
Regularization Learning of Neural Networks for Generalization
Unexpected Effects of Online no-Substitution k-means Clustering
Parallel Learning of Automatic Classes of Languages
Scale-Free Adversarial Multi Armed Bandits
Differentially Private Non-Convex Optimization under the KL Condition with Optimal Rates
On Approximately Identifying Concept Classes in the Limit
Prediction with Expert Advice by Following the Perturbed Leader for General Weights
Inductive Inference of Languages from Samplings
Universal Knowledge-Seeking Agents for Stochastic Environments
Refutable Language Learning with a Neighbor System
Clustering Algorithms for the Centralized and Local Models
On the Complexity of Proper Distribution-Free Learning of Linear Classifiers
Learning a Random DFA from Uniform Strings and State Information
Large-Margin Thresholded Ensembles for Ordinal Regression: Theory and Practice
Faster Rates of Private Stochastic Convex Optimization
String Pattern Discovery
Unifying Learning Methods by Colored Digraphs
Hierarchical Design of Fast Minimum Disagreement Algorithms
Algorithmic Learning Theory 2022: Preface
RBF Neural Networks and Descartes' Rule of Signs
Metric Entropy Duality and the Sample Complexity of Outcome Indistinguishability
Towards learning by abstraction
Learning with Refutation
On-line Learning with Malicious Noise and the Closure Algorithm
Non-uniform Consistency of Online Learning with Random Sampling
On the Expressive Power of Kernel Methods and the Efficiency of Kernel Learning by Association Schemes
Interactive Clustering of Linear Classes and Cryptographic Lower Bounds
Iterative Weighted Least Squares Algorithms for Neural Networks Classifiers
Grammatical Inference: An Old and New Paradigm
Non-asymptotic Calibration and Resolution
On the Relevance of Time in Neural Computation and Learning
On Learning Correlated Boolean Functions Using Statistical Queries
A Perceptual Criterion for Visually Controlling Learning
Universality of Bayesian mixture predictors
On the Learnability of Erasing Pattern Languages in the Query Model
Algorithmic Learning Theory, 17th International Conference, ALT 2006, Barcelona, Spain, October 7-10, 2006, Proceedings
Case-Based Representation and Learning of Pattern Languages
Efficient Private Algorithms for Learning Large-Margin Halfspaces
ACME: An Associative Classifier Based on Maximum Entropy Principle
Strategyproof Learning with Advice
Generalization and Robustness of Batched Weighted Average Algorithm with V-Geometrically Ergodic Markov Data
Algorithmic Connections between Active Learning and Stochastic Convex Optimization
Robust Estimation of Discrete Distributions under Local Differential Privacy
For Universal Multiclass Online Learning, Bandit Feedback and Full Supervision are Equivalent
Synthesis Algorithm for Recursive Process by µ-calculus (Extended Abstract)
Learning Minimal Covers of Functional Dependencies with Queries
Information Distance and Its Extensions
Learning Ordered Binary Decision Diagrams
Non-Adaptive Randomized Algorithm for Group Testing
On the Sample Complexity of Privately Learning Unbounded High-Dimensional Gaussians
Optimal multiclass overfitting by sequence reconstruction from Hamming queries
Active Learning in the Non-realizable Case
Optimistic PAC Reinforcement Learning: the Instance-Dependent View
Algorithmic Learning Theory, 19th International Conference, ALT 2008, Budapest, Hungary, October 13-16, 2008. Proceedings
On the Help of Bounded Shot Verifiers, Comparators and Standardisers for Learnability in Inductive Inference
Prequential Randomness
Predictor-Rejector Multi-Class Abstention: Theoretical Analysis and Algorithms
Inductive Inference of Unbounded Unions of Pattern Languages from Positive Data
Optimal and learned algorithms for the online list update problem with Zipfian accesses
Algorithmic Learning Theory, 8th International Conference, ALT '97, Sendai, Japan, October 6-8, 1997, Proceedings
The Complexity of Explaining Neural Networks Through (group) Invariants
On the V gamma Dimension for Regression in Reproducing Kernel Hilbert Spaces
Conceptual Classifications Guided by a Concept Hierarchy
Learning and Testing Irreducible Markov Chains via the k-Cover Time
Learning Unions of Tree Patterns Using Queries
Average Case Analysis of Pattern Language Learning Algorithms (Abstract)
Optimally-Smooth Adaptive Boosting and Application to Agnostic Learning
Oracles in Sigma p 2 are Sufficient for Exact Learning
Sample Complexity Bounds for Stochastic Shortest Path with a Generative Model
St. Petersburg Portfolio Games
What relations are reliably embeddable in Euclidean space?
Learning r-of-k Functions by Boosting
Is There an Elegant Universal Theory of Prediction?
Finding Total and Partial Orders from Data for Seriation
Learning Strategies Using Decision Lists
Boosting Based on Divide and Merge
Minimax Optimal Bayes Mixtures for Memoryless Sources over Large Alphabets
International Conference on Algorithmic Learning Theory, ALT 2017, 15-17 October 2017, Kyoto University, Kyoto, Japan
Inferring a System from Examples with Time Passage
Mixing Time Estimation in Ergodic Markov Chains from a Single Trajectory with Contraction Methods
Polynomial Time Inductive Inference of Regular Term Tree Languages from Positive Data
How rotation invariant algorithms are fooled by noise on sparse targets
A Combinatorial Metrical Task System Problem Under the Uniform Metric
Learning Structure from Sequences, with Applications in a Digital Library
Unsupervised Slow Subspace-Learning from Stationary Processes
A Characterization of List Regression
Structured Best Arm Identification with Fixed Confidence
Program Error Detection/Correction: Turning PAC Learning into PERFECT Learning (Abstract)
On Generalization Bounds for Neural Networks with Low Rank Layers
Approximate Reduction from AUC Maximization to 1-Norm Soft Margin Optimization
PAC Learning from Positive Statistical Queries
Learning Tree Languages from Positive Examples and Membership Queries
Predictive Complexity and Generalized Entropy Rate of Stationary Ergodic Processes
Order Compression Schemes
The Power of Random Counterexamples
Indefinitely Oscillating Martingales
Efficient sampling from the Bingham distribution
The Maximum Cosine Framework for Deriving Perceptron Based Linear Classifiers
Erasing Pattern Languages Distinguishable by a Finite Number of Strings
Deviations of Stochastic Bandit Regret
Approximation Algorithms for Tensor Clustering
PAC Learnability of a Concept Class under Non-atomic Measures: A Problem by Vidyasagar
A Uniform Lower Error Bound for Half-Space Learning
Sauer's Bound for a Notion of Teaching Complexity
Learning with Higher Order Additional Information
Efficient Learning of Semi-structured Data from Queries
Generalisation under gradient descent via deterministic PAC-Bayes
Predicting Nearly as well as the best Pruning of a Planar Decision Graph
Learning a mixture of two subspaces over finite fields
Dimension-Adaptive Bounds on Compressive FLD Classification
Sample Compression Scheme Reductions
On the Eigenspectrum of the Gram Matrix and Its Relationship to the Operator Eigenspectrum
Anomalous Learning Helps Succinctness (Extended Abstract)
Corruption-Robust Lipschitz Contextual Search
Convergence of Langevin MCMC in KL-divergence
Complexity versus Agreement for Many Views
On the Data Consumption Benefits of Accepting Increased Uncertainty
ACE: A syntax-directed editor customizable from examples and queries
Finding the Rare Cube
Logical Aspects of Several Bottom-Up Fittings
Non-linear Inequalities between Predictive and Kolmogorov Complexities
A Criterion for the Existence of Predictive Complexity for Binary Games
Adaptive Estimation of the Optimal ROC Curve and a Bipartite Ranking Algorithm
Algorithmic Identification of Probabilities Is Hard
Infinitely Divisible Noise in the Low Privacy Regime
An Upper Bound for Aggregating Algorithm for Regression with Changing Dependencies
Probabilitic Limit Identification up to "Small" Sets
Efficient Algorithms for Stochastic Repeated Second-price Auctions
Identification in the Limit of Substitutable Context-Free Languages
Learning Kernel Perceptrons on Noisy Data Using Random Projections
Notes on the PAC Learning of Geometric Concepts with Additional Information
Multiclass Online Learnability under Bandit Feedback
On PAC Learnability of Functional Dependencies
Dimensionality Reduction and (Bucket) Ranking: a Mass Transportation Approach
On Case-Based Representability and Learnability of Languages
Induction of Probabilistic Rules Based on Rough Set Theory
Ordered Term Tree Languages which Are Polynomial Time Inductively Inferable from Positive Data
SQ Lower Bounds for Random Sparse Planted Vector Problem
Distribution-Dependent PAC-Bayes Priors
Global Riemannian Acceleration in Hyperbolic and Spherical Spaces
Monotone Extensions of Boolean Data Sets
Minimization by Incremental Stochastic Surrogate Optimization for Large Scale Nonconvex Problems
Efficient PAC Learning of Halfspaces with Constant Malicious Noise Rate
Inferability of Recursive Real-Valued Functions
Learning Figures with the Hausdorff Metric by Fractals
Hardness of Improper One-Sided Learning of Conjunctions For All Uniformly Falsifiable CSPs
Completing Networks Using Observed Data
Using Attribute Grammars for Description of Inductive Inference Search Space
Generalization bounds for mixing processes via delayed online-to-PAC conversions
Polynomial-Time Sum-of-Squares Can Robustly Estimate Mean and Covariance of Gaussians Optimally
Reaching Goals is Hard: Settling the Sample Complexity of the Stochastic Shortest Path
A Polynomial Time, Pure Differentially Private Estimator for Binary Product Distributions
Quantile Multi-Armed Bandits with 1-bit Feedback
Iterative Learning from Positive Data and Negative Counterexamples
Flattening and Implication
Learning Relational Patterns
Learning in multi-agent environments
Teaching Learners with Restricted Mind Changes
Criterion of Calibration for Transductive Confidence Machine with Limited Feedback
Learning Hypertrees From Shortest Path Queries
Ising Models with Latent Conditional Gaussian Variables
Incremental Learning of Logic Programs
Two Problems for Sophistication
Positive and Unlabeled Examples Help Learning
Priced Learning
A Model Selection Approach for Corruption Robust Reinforcement Learning
Thue Systems and DNA - A Learning Algorithm for a Subclass
Optimal Language Learning
A Sharp Lower Bound for Agnostic Learning with Sample Compression Schemes
Learning under $p$-Tampering Attacks
Exact Learning of Semilinear Sets
Learning from Examples with Typed Equational Programming
Computationally efficient reductions between some statistical models
Topological Properties of Concept Spaces
Asymptotic Learnability of Reinforcement Problems with Arbitrary Dependence
Old Techniques in Differentially Private Linear Regression
Synthesizing Learners Tolerating Computable Noisy Data
Universal Representation of Permutation-Invariant Functions on Vectors and Tensors
Algorithmic Learning Theory - 26th International Conference, ALT 2015, Banff, AB, Canada, October 4-6, 2015, Proceedings
The Mirror Langevin Algorithm Converges with Vanishing Bias
Sequential Sampling Techniques for Algorithmic Learning Theory
Exact Learning of Linear Combinations of Monotone Terms from Function Value Queries
Learning DFA from Simple Examples
A Survey of Preference-Based Online Learning with Bandit Algorithms
Online Prediction under Submodular Constraints
A Complete Characterization of Learnability for Stochastic Noisy Bandits
Learning a Classifier when the Labeling Is Known
Learning Finite Automata Using Label Queries
Parallelism Increases Iterative Learning Power
Adversarially Robust Learning Could Leverage Computational Hardness
Competitive Portfolio Selection Using Stochastic Predictions
The complexity of non-stationary reinforcement learning
Learning Strongly Deterministic Even Linear Languages from Positive Examples
Algorithmic Learning Theory, 14th International Conference, ALT 2003, Sapporo, Japan, October 17-19, 2003, Proceedings
Feasible Iteration of Feasible Learning Functionals
Learning without Coding
Online Multiple Kernel Learning: Algorithms and Mistake Bounds
On Upper-Confidence Bound Policies for Switching Bandit Problems
The Algorithms of Recognition of the Functional Sites in Genetic Texts
Two-Player Games for Efficient Non-Convex Constrained Optimization
Labeled Compression Schemes for Extremal Classes
Inductive Bias of Gradient Descent for Weight Normalized Smooth Homogeneous Neural Nets
Multiclass Boosting Algorithms for Shrinkage Estimators of Class Probability
Language Learning from Membership Queries and Characteristic Examples
A logical analysis of relevance in analogy
Accelerated Training of Max-Margin Markov Networks with Kernels
PAC Bounds for Discounted MDPs
Pseudometrics for State Aggregation in Average Reward Markov Decision Processes
Proving based on similarity
Learning MSO-definable hypotheses on strings
Nonparametric Independence Tests: Space Partitioning and Kernel Approaches
Online Regret Bounds for Markov Decision Processes with Deterministic Transitions
Scale-Free Algorithms for Online Linear Optimization
Robust Inference for Multiclass Classification
Instrument-Armed Bandits
Fisher information lower bounds for sampling
Learning Causal Structures Based on Markov Equivalence Class
Partial Monitoring with Side Information
A Self-Organizing Network Composed of Symbol Nodes With Location Parameter
Implicit Parameter-free Online Learning with Truncated Linear Models
Near-tight closure b ounds for the Littlestone and threshold dimensions
On the Learnability of Shuffle Ideals
Learning With Growing Quality
On-Line Probability, Complexity and Randomness
Limits of Exact Algorithms For Inference of Minimum Size Finite State Machines
Pure Exploration in Infinitely-Armed Bandit Models with Fixed-Confidence
Signal Extraction and Knowledge Discovery Based on Statistical Modeling
Characterizing Statistical Query Learning: Simplified Notions and Proofs
The Complexity of Learning SUBSEQ ( A )
Generalization Bounds for K-Dimensional Coding Schemes in Hilbert Spaces
Stochastic Nonconvex Optimization with Large Minibatches
Stochastic Complexity for Mixture of Exponential Families in Variational Bayes
Algorithmic Learning Theory (ALT) 2017: Preface
A Better Resource Allocation Algorithm with Semi-Bandit Feedback
Learnability with Restricted Focus of Attention guarantees Noise-Tolerance
Monotonic and Non-Monotonic Inductive Inference
Testing Product Distributions: A Closer Look
On the Existence and Convergence of Computable Universal Priors
Machine Learning in Ecosystem Informatics
Efficient Optimal PAC Learning
On the Sample Complexity of Consistent Learning with One-Sided Error