yusuke1997/dblp-id-title huggingface dataset:
A knowledge transfer model: From zero-knowledge to full-knowledge
Agnostic Membership Query Learning with Nontrivial Savings: New Results and Techniques
Learning from Networked Examples
Program Synthesis in the Presence of Infinite Number of Inaccuracies
On Version Space Compression
A Comparison of Identification Criteria for Inductive Inference of Recursive Real-Valued Functions
Solving Semi-infinite Linear Programs Using Boosting-Like Methods
Learning a Representation for Optimizable Formulas
Learning Attribute-Efficiently with Corrupt Oracles
Loss Functions, Complexities, and the Legendre Transformation
On Learning Monotone Boolean Functions under the Uniform Distribution
Reflective Inductive Inference of Recursive Functions
The Follow Perturbed Leader Algorithm Protected from Unbounded One-Step Losses
Following the Perturbed Leader to Gamble at Multi-armed Bandits
Co-learnability and FIN-identifiability of Enumerable Classes of Total Recursive Functions
Deciding What to Learn in Explanation-Based Macro-Rule Learning
A Probabilistic Identification Result
Non-stochastic Bandits With Evolving Observations
Use of Reduction Arguments in Determining Popperian FIN-Type Learning Capabilities
Combinatorial Online Prediction via Metarounding
Adversarial Contextual Bandits Go Kernelized
Minimax Rates and Efficient Algorithms for Noisy Sorting
A Stochastic Approach to Genetic Information Processing
Adaptive Group Testing Algorithms to Estimate the Number of Defectives
Sampling Without Compromising Accuracy in Adaptive Data Analysis
International Conference on Algorithmic Learning Theory, 24-27 February 2025, Politecnico di Milano, Milan, Italy
Constant regret for sequence prediction with limited advice
Improved High-Probability Regret for Adversarial Bandits with Time-Varying Feedback Graphs
Reformulation of Explanation by Linear Logic: Toward Logic for Explanation
A Method of Similarity-Driven Knowledge Revision for Type Specializations
Computational Models of Neural Representations in the Human Brain
Efficient Agnostic Learning with Average Smoothness
Structured Weight-Based Prediction Algorithms
Limit Learning Equivalence Structures
Efficiently Learning the Metric with Side-Information
Algorithmic Learning Theory 2019: Preface
Linear Reinforcement Learning with Ball Structure Action Space
Iterative Learning from Texts and Counterexamples Using Additional Information
Algorithmic Learning Theory, 13th International Conference, ALT 2002, Lübeck, Germany, November 24-26, 2002, Proceedings
Ranking Median Regression: Learning to Order through Local Consensus
Defensive Forecasting for Linear Protocols
Learning Languages by Collecting Cases and Tuning Parameters
Mixture of Vector Experts
Projection-free Adaptive Regret with Membership Oracles
Coordinate Descent Faceoff: Primal or Dual?
Algorithmic Learning Theory, 7th International Workshop, ALT '96, Sydney, Australia, October 23-25, 1996, Proceedings
Attribute-efficient learning of monomials over highly-correlated variables
Algorithmic Learning Theory 2020: Preface
Optimal Online Prediction in Adversarial Environments
Thompson Sampling for Adversarial Bit Prediction
Learnability beyond Uniform Convergence
Learning Concatenations of Locally Testable Languages from Positive Data
Continuity of Performance Metrics for Thin Feature Maps
Inductive Inference of an Approximate Concept from Positive Data
PeerReview4All: Fair and Accurate Reviewer Assignment in Peer Review
Time Consistent Discounting
Reflecting and Self-Confident Inductive Inference Machines
Clustering, Hamming Embedding, Generalized LSH and the Max Norm
Learning from Entailment of Logic Programs with Local Variables
Learning k -Term Monotone Boolean Formulae
Recovery from multiple faults in relational theory
Finding Relevant Variables in PAC Model with Membership Queries
Maximizing Agreements and CoAgnostic Learning
Learning Taxonomic Relation by Case-Based Reasoning
Efficient Algorithm for Learning Simple Regular Expressions from Noisy Examples
PageRank Optimization in Polynomial Time by Stochastic Shortest Path Reformulation
Tailoring Representations to Different Requirements
The Arrowsmith Project: 2005 Status Report
International Conference on Algorithmic Learning Theory, February 20-23, 2023, Singapore
Decision Trees: More Theoretical Justification for Practical Algorithms
Efficient and Optimal Fixed-Time Regret with Two Experts
An adaptive stochastic optimization algorithm for resource allocation
Learnability of Exclusive-Or Expansion Based on Monotone DNF Formulas
On the Strength of Incremental Learning
Constructing Multiclass Learners from Binary Learners: A Simple Black-Box Analysis of the Generalization Errors
Algorithms and Software for Collaborative Discovery from Autonomous, Semantically Heterogeneous, Distributed Information Sources
Learning and Verifying Graphs Using Queries with a Focus on Edge Counting
Uniform regret bounds over R d for the sequential linear regression problem with the square loss
An Application Of Bernstein Polynomials in PAC Model
Regular Inference as Vertex Coloring
Variance-Reduced Conservative Policy Iteration
Vapnik-Chervonenkis Dimension of Parallel Arithmetic Computations
A Spectral Algorithm with Additive Clustering for the Recovery of Overlapping Communities in Networks
From Inductive Inference to Algorithmic Learning Theory
Asymptotically Optimal Agents
A Note on Support Vector Machine Degeneracy
Challenge for Info-plosion
Learning Continuous Latent Variable Models with Bregman Divergences
Closedness Properties in EX-Identification of Recursive Functions
Classes with Easily Learnable Subclasses
Classification with Intersecting Rules
A Unified Approach to Inductive Logic and Case-Based Reasoning (Extended Abstract)
Adversarial Online Learning with Changing Action Sets: Efficient Algorithms with Approximate Regret Bounds
Constructive Learning of Translations Based on Dictionaries
Online Influence Maximization with Local Observations
Learning and Optimizing with Preferences
Incremental Learning with Ordinal Bounded Example Memory
TensorPlan and the Few Actions Lower Bound for Planning in MDPs under Linear Realizability of Optimal Value Functions
Stochastic Dueling Bandits with Adversarial Corruption
Real-Valued Multiple-Instance Learning with Queries
Dictionary Learning for the Almost-Linear Sparsity Regime
On Learning Systolic Languages
Improved rates for prediction and identification of partially observed linear dynamical systems
On Noise-Tolerant Learning of Sparse Parities and Related Problems
Numberings Optimal for Learning
Learning by Erasing
Algorithmic Learning Theory, 11th International Conference, ALT 2000, Sydney, Australia, December 11-13, 2000, Proceedings
The Kindest Cut: Minimum Message Length Segmentation
A Closer Look at Adaptive Regret
Learning from Random Text
Faster Noisy Power Method
Algorithmic Learning Theory, 4th International Workshop, ALT '93, Tokyo, Japan, November 8-10, 1993, Proceedings
Generalization Bounds for Time Series Prediction with Non-stationary Processes
Inferring Unions of the Pattern Languages by the Most Fitting Covers
Algorithmic Learning Theory, 10th International Conference, ALT '99, Tokyo, Japan, December 6-8, 1999, Proceedings
Error dynamics of mini-batch gradient descent with random reshuffling for least squares regression
Learning Nested Differences in the Presence of Malicious Noise
Learning from Label Preferences
An Efficient Algorithm for Cooperative Semi-Bandits
High-accuracy sampling from constrained spaces with the Metropolis-adjusted Preconditioned Langevin Algorithm
Analogical reasoning using elementary formal systems with mismatch
Thompson Sampling: An Asymptotically Optimal Finite-Time Analysis
Monotonic Language Learning
Leveraging Initial Hints for Free in Stochastic Linear Bandits
On the Role of Update Constraints and Text-Types in Iterative Learning
Intrinsic Complexity of Uniform Learning
A PAC-Bayes Bound for Tailored Density Estimation
A Model for Combinatorial Dictionary Learning and Inference
The Discovery Science Project in Japan
Cellular Tree Classifiers
Algebraic and Analytic Approaches for Parameter Learning in Mixture Models
Sharp bounds on aggregate expert error
On the Convergence Speed of MDL Predictions for Bernoulli Sequences
A Note on the Query Complexity of Learning DFA (Extended Abstract)
On the Teaching Complexity of Linear Sets
Learning One-Variable Pattern Languages Very Efficiently on Average, in Parallel, and by Asking Queries
Hannan Consistency in On-Line Learning in Case of Unbounded Losses Under Partial Monitoring
Learning with Temporary Memory
On Approximate Learning by Multi-layered Feedforward Circuits
On Best-Arm Identification with a Fixed Budget in Non-Parametric Multi-Armed Bandits
Efficient Learning of Regular Expressions from Good Examples
Learning Acyclic First-Order Horn Sentences from Entailment
Boosting, Voting Classifiers and Randomized Sample Compression Schemes
Sample Compression for Real-Valued Learners
Tournaments, Johnson Graphs and NC-Teaching
Calibration and Internal No-Regret with Random Signals
Algorithmic Learning Theory, Third Workshop, ALT '92, Tokyo, Japan, October 20-22, 1992, Proceedings
Learning Coherent Concepts
On the Smallest Possible Dimension and the Largest Possible Margin of Linear Arrangements Representing Given Concept Classes Uniform Distribution
Learning with Deep Cascades
Tight Bounds for Local Glivenko-Cantelli
Learning and Extending Sublanguages
The Plug-in Approach for Average-Reward and Discounted MDPs: Optimal Sample Complexity Analysis
Difficulties in Forcing Fairness of Polynomial Time Inductive Inference
Do PAC-Learners Learn the Marginal Distribution?
Can Learning in the Limit Be Done Efficiently?
Nonparametric Multiple Change Point Estimation in Highly Dependent Time Series
Buy Low, Sell High
Model Inference of Constrained Recursive Figures
Robust Learning of Automatic Classes of Languages
Some Rates of Convergence for the Selected Lasso Estimator
Full Swap Regret and Discretized Calibration
Erratum: Constructing Multiclass Learners from Binary Learners: A Simple Black-Box Analysis of the Generalization Errors
Convergence of score-based generative modeling for general data distributions
A minimax near-optimal algorithm for adaptive rejection sampling
The Divide-and-Conquer Manifesto
Learning Mildly Context-Sensitive Languages with Multidimensional Substitutability from Positive Data
A Class of Prolog Programs with Non-linear Outputs Inferable from Positive Data
Noisy Computing of the Threshold Function
Inventing Discovery Tools: Combining Information Visualization with Data Mining
Cleaning up the neighborhood: A full classification for adversarial partial monitoring
Tuning Bandit Algorithms in Stochastic Environments
Dynamically Delayed Postdictive Completeness and Consistency in Learning
Pseudonorm Approachability and Applications to Regret Minimization
Efficient coordinate-wise leading eigenvector computation
Smooth Boosting Using an Information-Based Criterion
Online Learning in Markov Decision Processes with Continuous Actions
Mathematical Theory of Neural Learning
A Strongly Quasiconvex PAC-Bayesian Bound
The Consistency Dimension and Distribution-Dependent Learning from Queries (Extended Abstract)
Exact Learning of Juntas from Membership Queries
On the Absence of Predictive Complexity for Some Games
The Safe Bayesian - Learning the Learning Rate via the Mixability Gap
Online Learning with Off-Policy Feedback
Learning what to remember
Learning of Boolean Functions Using Support Vector Machines
Sample Selection Bias Correction Theory
Defensive Universal Learning with Experts
Inductive inference of formal languages from positive data enumerated primitive-recursively
Algorithmic Stability of Heavy-Tailed Stochastic Gradient Descent on Least Squares
Efficient Learning of Real Time One-Counter Automata
Refined Lower Bounds for Nearest Neighbor Condensation
Combining Models of Approximation with Partial Learning
Inductive Inference of Term Rewriting Systems Realizing Algebras
On Learning the Optimal Waiting Time
No-substitution k-means Clustering with Adversarial Order
Polynomial-Time MAT Learning of Multilinear Logic Programs
Extended Stochastic Complexity and Minimax Relative Loss Analysis
On the Role of Interpretive Analogy in Learning
PAC Learning under Helpful Distributions
Universal Convergence of Semimeasures on Individual Random Sequences
On the Duality Between Mechanistic Learners and What it is They Learn
Faster Perturbed Stochastic Gradient Methods for Finding Local Minima
A Note on Learning DNF Formulas Using Equivalence and Incomplete Membership Queries
Efficient Matrix Sensing Using Rank-1 Gaussian Measurements
Feedforward Neural Networks in Reinforcement Learning Applied to High-Dimensional Motor Control
Sequential prediction under log-loss with side information
When and why randomised exploration works (in linear bandits)
On Aggregating Teams of Learning Machines
Optimal Regret Bounds for Collaborative Learning in Bandits
Erratum: Learning without Coding
Clustering with Interactive Feedback
Offline to Online Conversion
A Note on the Generalization Performance of Kernel Classifiers with Margin
Learning of Finite Unions of Tree Patterns with Repeated Internal Structured Variables from Queries
Partially Interpretable Models with Guarantees on Coverage and Accuracy
The Last-Step Minimax Algorithm
A Safe Approximation for Kolmogorov Complexity
Online Recommendations for Agents with Discounted Adaptive Preferences
Variance-Aware Regret Bounds for Undiscounted Reinforcement Learning in MDPs
Subsampling in Smoothed Range Spaces
Differentially Private Multi-Sampling from Distributions
Contrast Pattern Mining and Its Application for Building Robust Classifiers
Stable Sample Compression Schemes: New Applications and an Optimal SVM Margin Bound
Solutions to Open Questions for Non-U-Shaped Learning with Memory Limitations
The Subsumption Lattice and Query Learning
Interplay of minimax estimation and minimax support recovery under sparsity
On Learnability wih Computable Learners
On Iterative Algorithms with an Information Geometry Background
An Online Feasible Point Method for Benign Generalized Nash Equilibrium Problems
Neural Discriminant Analysis
Online Linear Optimization with Sparsity Constraints
Supermartingales in Prediction with Expert Advice
Primal-Dual Algorithms with Predictions for Online Bounded Allocation and Ad-Auctions Problems
Solving Bernoulli Rank-One Bandits with Unimodal Thompson Sampling
Best-of-Both-Worlds Algorithms for Partial Monitoring
Learning Erasing Pattern Languages with Queries
Computation with Sequences of Assemblies in a Model of the Brain
Extending Elementary Formal Systems
A Non-Trivial Algorithm Enumerating Relevant Features over Finite Fields
Compactness and Learning of Classes of Unions of Erasing Regular Pattern Languages
Feedback graph regret bounds for Thompson Sampling and UCB
An interactive knowledge transfer model and analysis of "Mastermind" game
A Hilbert Space Embedding for Distributions
Reliable Active Apprenticeship Learning
Adversarial Interpretation of Bayesian Inference
Online Non-Convex Learning: Following the Perturbed Leader is Optimal
Unsupervised Model-Free Representation Learning
Complexity of Pattern Classes and Lipschitz Property
Understanding Aggregations of Proper Learners in Multiclass Classification
Dealing with Unknown Variances in Best-Arm Identification
Polynomial-Time Inference of Pattern Languages
Follow-the-Perturbed-Leader Achieves Best-of-Both-Worlds for Bandit Problems
On Minimaxity of Follow the Leader Strategy in the Stochastic Setting
On the Complexity of Training a Single Perceptron with Programmable Synaptic Delays
Learning DNF by Statistical and Proper Distance Queries Under the Uniform Distribution
Deductive Plan Generation
Localization of VC Classes: Beyond Local Rademacher Complexities
Universal Prediction of Selected Bits
Self-Directed Node Classification on Graphs
Induction of Constraint Logic Programs
Exact Learning via Teaching Assistants (Extended Abstract)
Lower Bounds for the Complexity of Learning Half-Spaces with Membership Queries
A New Framework for Discovering Knowledge from Two-Dimensional Structured Data Using Layout Formal Graph System
Adaptive and Optimal Online Linear Regression on ℓ1-Balls
Learning Unknown Graphs
Learnability of Translations from Positive Examples
Lower Bounds on Learning Random Structures with Statistical Queries
Robust Inference of Relevant Attributes
Competitive ratio vs regret minimization: achieving the best of both worlds
Some Improved Sample Complexity Bounds in the Probabilistic PAC Learning Model
Cost-Free Fairness in Online Correlation Clustering
Learning Real Polynomials with a Turing Machine
Finding Tree Patterns Consistent with Positive and Negative Examples Using Queries
On the Hardness of Learning One Hidden Layer Neural Networks
Reconstructing Ultrametric Trees from Noisy Experiments
Algorithmic Learning Theory, ALT 2020, 8-11 February 2020, San Diego, CA, USA
Genetic Fitness Optimization Using Rapidly Mixing Markov Chains
Efficient Learning of Real Time Two-Counter Automata (Extended Abstract)
Social Learning in Non-Stationary Environments
Lipschitz Bandits without the Lipschitz Constant
Not All Learnable Distribution Classes are Privately Learnable
Algorithmic Learning Theory - 27th International Conference, ALT 2016, Bari, Italy, October 19-21, 2016, Proceedings
Estimating Sparse Discrete Distributions Under Privacy and Communication Constraints
Non-adaptive Learning of a Hidden Hypergraph
Large Margin Classification for Moving Targets
Noninteractive Locally Private Learning of Linear Models via Polynomial Approximations
Efficient Global Planning in Large MDPs via Stochastic Primal-Dual Optimization
Bayesian Methods for Low-Rank Matrix Estimation: Short Survey and Theoretical Study
Learning of Ordered Tree Languages with Height-Bounded Variables Using Queries
Identifiability of Subspaces and Homomorphic Images of Zero-Reversible Languages
Solomonoff Induction Violates Nicod's Criterion
Submodular Learning and Covering with Response-Dependent Costs
The Missing Consistency Theorem for Bayesian Learning: Stochastic Model Selection
On the Evolution of Monotone Conjunctions: Drilling for Best Approximations
A Random Sampling Technique for Training Support Vector Machines
Enhanced H-Consistency Bounds
Learning with Rejection
On Learning Graphs with Edge-Detecting Queries
Refutably Probably Approximately Correct Learning
Information Preserving Dimensionality Reduction
Boosting First-Order Learning
Algorithmic Learning Theory, ALT 2018, 7-9 April 2018, Lanzarote, Canary Islands, Spain
Intervention Efficient Algorithms for Approximate Learning of Causal Graphs
Full Information Game with Gains and Losses
Faster Near-Optimal Reinforcement Learning: Adding Adaptiveness to the E 3 Algorithm
Synthesizing Noise-Tolerant Language Learners
Domains of Attraction in Autoassociative Memory Networks for Character Pattern Recognition
Making Online Decisions with Bounded Memory
Average-Case Analysis of Classification Algorithms for Boolean Functions and Decision Trees
Tight bounds for maximum ℓ 1 -margin classifiers
Fast Spectral Clustering via the Nyström Method
Learning non-parametric densities by finite-dimensional parametric hypotheses
The Two Faces of Active Learning
Absolute Versus Probabilistic Classification in a Logical Setting
A Deep Conditioning Treatment of Neural Networks
Learning Recursive Concepts with Anomalies
Improving Adaptive Online Learning Using Refined Discretization
Efficient Learning with Arbitrary Covariate Shift
Private Stochastic Optimization with Large Worst-Case Lipschitz Parameter: Optimal Rates for (Non-Smooth) Convex Losses and Extension to Non-Convex Losses
A Pathology of Bottom-Up Hill-Climbing in Inductive Rule Learning
A Spectral Approach for Probabilistic Grammatical Inference on Trees
A Note on a Scale-Sensitive Dimension of Linear Bounded Functionals in Banach Spaces
Exact Learning from Membership Queries: Some Techniques, Results and New Directions
Beyond Bernoulli: Generating Random Outcomes that cannot be Distinguished from Nature
Lower Bounds on the Total Variation Distance Between Mixtures of Two Gaussians
Learning Simple Deterministic Finite-Memory Automata
Online Allocation with Risk Information
Separating Models of Learning with Faulty Teachers
Towards Empirical Process Theory for Vector-Valued Functions: Metric Entropy of Smooth Function Classes
Semi-supervised Group DRO: Combating Sparsity with Unlabeled Data
Learning Rational Stochastic Tree Languages
Learning and Classifying
Polynomial Time Algorithms for Learning k -Reversible Languages and Pattern Languages with Correction Queries
Algebraic Structure of Some Learning Systems
On the Computability of Solomonoff Induction and Knowledge-Seeking
Robust guarantees for learning an autoregressive filter
Lower Bounds on Individual Sequence Regret
Asymptotic Degradation of Linear Regression Estimates with Strategic Data Sources
Constructive Induction for Recursive Programs
Universal Bias Reduction in Estimation of Smooth Additive Function in High Dimensions
Learning Sparse Linear Combinations of Basis Functions over a Finite Domain
Structure Learning of ${H}$-colorings
Lifelong Learning in Costly Feature Spaces
Intrinsic Complexity of Partial Learning
Approximate Representer Theorems in Non-reflexive Banach Spaces
Self-Tuning Bandits over Unknown Covariate-Shifts
Noisy Inference and Oracles
Inference of omega-Languages from Prefixes
Algorithmic Learning Theory - 22nd International Conference, ALT 2011, Espoo, Finland, October 5-7, 2011. Proceedings
Prediction with Expert Advice under Discounted Loss
Probability Theory for the Brier Game
Optimal Collusion-Free Teaching
Improved Generalization Bounds for Robust Learning
Faster Hoeffding Racing: Bernstein Races via Jackknife Estimates
An Efficient Exact Learning Algorithm for Ordered Binary Decision Diagrams
Algorithmic Learning Theory, 16th International Conference, ALT 2005, Singapore, October 8-11, 2005, Proceedings
Mining Heterogeneous Information Networks by Exploring the Power of Links
Generalizing Labeled and Unlabeled Sample Compression to Multi-label Concept Classes
Efficient Data Representations That Preserve Information
Analysis of Case-Based Representability of Boolean Functions by Monotone Theory
Sparsity, variance and curvature in multi-armed bandits
Robust Algorithms for Online k-means Clustering
Learning Content Sequencing in an Educational Environment According to Student Needs
Inductive Resolution
The Complexity of Learning Concept Classes with Polynomial General Dimension
Language Learning under Various Types of Constraint Combinations
The VC-Dimensions of Finite Automata with n States
The Function-Acquisition Paradigm in a Knowledge-Based Concept-Synthesis Environment
Episodic Reinforcement Learning in Finite MDPs: Minimax Lower Bounds Revisited
Consistency of Feature Markov Processes
Deranomized Learning of Boolean Functions
On Monotonic Strategies for Learning r.e. Languages
Confident and Consistent Partial Learning of Recursive Functions
Optimal $δ$-Correct Best-Arm Selection for Heavy-Tailed Distributions
Learning in Friedberg Numberings
Competitive Learning by Entropy Minimization
Invention and Artificial Intelligence
Average-Case Information Complexity of Learning
Distinguishing Relational Pattern Languages With a Small Number of Short Strings
Mind Change Complexity of Inferring Unbounded Unions of Pattern Languages from Positive Data
Boundary Crossing for General Exponential Families
Probabilistic Language Learning Under Monotonicity Constraints
Contrastive learning, multi-view redundancy, and linear models
A minimax and asymptotically optimal algorithm for stochastic bandits
On the VC-Dimension of Depth Four Threshold Circuits and the Complexity of Boolean-Valued Functions
Efficient Inductive Inference of Primitive Prologs from Positive Data
An Algebraic Perspective on Boolean Function Learning
Approximation of the Optimal ROC Curve and a Tree-Based Ranking Algorithm
Upper-Confidence-Bound Algorithms for Active Learning in Multi-armed Bandits
Characterizing the implicit bias via a primal-dual analysis
Asymptotically Optimal Strategies For Combinatorial Semi-Bandits in Polynomial Time
Adaptive Reward-Free Exploration