yusuke1997/dblp-id-title huggingface dataset:
Curriculum Reinforcement Learning via Constrained Optimal Transport
Variational Learning is Effective for Large Deep Networks
Decision tree and instance-based learning for label ranking
Variance-Reduced Forward-Reflected-Backward Splitting Methods for Nonmonotone Generalized Equations
Learning Representations for Counterfactual Inference
Query Learning with Large Margin Classifiers
Improved Modelling of Federated Datasets using Mixtures-of-Dirichlet-Multinomials
Graph transduction via alternating minimization
MSA Transformer
Active learning for sampling in time-series experiments with application to gene expression analysis
Near-Bayesian exploration in polynomial time
Approximation properties of DBNs with binary hidden units and real-valued visible units
Kernelizing the output of tree-based methods
Variational Inference for Sequential Distance Dependent Chinese Restaurant Process
Visual Abstraction: A Plug-and-Play Approach for Text-Visual Retrieval
DAG-GNN: DAG Structure Learning with Graph Neural Networks
Dynamic Covariance Models for Multivariate Financial Time Series
Position: Open-Endedness is Essential for Artificial Superhuman Intelligence
Minimax Rate for Learning From Pairwise Comparisons in the BTL Model
Consistent Diffusion Meets Tweedie: Training Exact Ambient Diffusion Models with Noisy Data
Neuron birth-death dynamics accelerates gradient descent and converges asymptotically
Learn to Accumulate Evidence from All Training Samples: Theory and Practice
The f-Adjusted Graph Laplacian: a Diagonal Modification with a Geometric Interpretation
Revisiting Simple Regret: Fast Rates for Returning a Good Arm
Constrained Optimization with Dynamic Bound-scaling for Effective NLP Backdoor Defense
Decision-Focused Learning: Through the Lens of Learning to Rank
Learning Search Control Rules for Planning: An Inductive Approach
Self-Alignment of Large Language Models via Monopolylogue-based Social Scene Simulation
Local Minima Embedding
User-defined Event Sampling and Uncertainty Quantification in Diffusion Models for Physical Dynamical Systems
Proceedings of the Fifteenth International Conference on Machine Learning (ICML 1998), Madison, Wisconsin, USA, July 24-27, 1998
Learning Mallows Models with Pairwise Preferences
Validating Mechanistic Interpretations: An Axiomatic Approach
Human-Aligned Image Models Improve Visual Decoding from the Brain
Cut your Losses with Squentropy
Reinforcement Learning for Non-Stationary Markov Decision Processes: The Blessing of (More) Optimism
Discovering Context Effects from Raw Choice Data
Estimating the Hessian by Back-propagating Curvature
Submodular meets Spectral: Greedy Algorithms for Subset Selection, Sparse Approximation and Dictionary Selection
Deep Linear Network Training Dynamics from Random Initialization: Data, Width, Depth, and Hyperparameter Transfer
A Functional Perspective on Learning Symmetric Functions with Neural Networks
Attention-Based Recurrence for Multi-Agent Reinforcement Learning under Stochastic Partial Observability
Scalable training of L 1 -regularized log-linear models
A/B Testing in Network Data with Covariate-Adaptive Randomization
Double Trouble in Double Descent: Bias and Variance(s) in the Lazy Regime
Context-Guided Diffusion for Out-of-Distribution Molecular and Protein Design
Reducing Balancing Error for Causal Inference via Optimal Transport
Hardware and Software Platform Inference
Multilinear Latent Conditioning for Generating Unseen Attribute Combinations
SFC: Achieve Accurate Fast Convolution under Low-precision Arithmetic
Reward Model Learning vs. Direct Policy Optimization: A Comparative Analysis of Learning from Human Preferences
Fair Clustering via Alignment
Predicting the Susceptibility of Examples to Catastrophic Forgetting
Latent Gaussian Processes for Distribution Estimation of Multivariate Categorical Data
Sketching Meets Differential Privacy: Fast Algorithm for Dynamic Kronecker Projection Maintenance
Boosting with incomplete information
SNS-Bench: Defining, Building, and Assessing Capabilities of Large Language Models in Social Networking Services
Generalizing from Case studies: A Case Study
Generating Programmatic Referring Expressions via Program Synthesis
Calibration Bottleneck: Over-compressed Representations are Less Calibratable
Robust Imitation Learning against Variations in Environment Dynamics
Hide & Seek: Transformer Symmetries Obscure Sharpness & Riemannian Geometry Finds It
Adaptively Perturbed Mirror Descent for Learning in Games
Infusing Lattice Symmetry Priors in Attention Mechanisms for Sample-Efficient Abstract Geometric Reasoning
Reinforcement Learning for Molecular Design Guided by Quantum Mechanics
Training Characteristic Functions with Reinforcement Learning: XAI-methods play Connect Four
REvolveR: Continuous Evolutionary Models for Robot-to-robot Policy Transfer
WebLINX: Real-World Website Navigation with Multi-Turn Dialogue
Sawtooth Factorial Topic Embeddings Guided Gamma Belief Network
Efficient Feature Selection in Conceptual Clustering
A Reduction Framework for Distributionally Robust Reinforcement Learning under Average Reward
Towards Efficient Exact Optimization of Language Model Alignment
Customizing the Inductive Biases of Softmax Attention using Structured Matrices
Safety Alignment Can Be Not Superficial With Explicit Safety Signals
Cost-sensitive Multiclass Classification Risk Bounds
Active Learning for Top-K Rank Aggregation from Noisy Comparisons
High-dimensional Linear Bandits with Knapsacks
Memory (and Time) Efficient Sequential Monte Carlo
Rate Distortion For Model Compression: From Theory To Practice
Flexible Residual Binarization for Image Super-Resolution
Neurally-Guided Structure Inference
Context-Informed Neural ODEs Unexpectedly Identify Broken Symmetries: Insights from the Poincaré-Hopf Theorem
Overcoming Simplicity Bias in Deep Networks using a Feature Sieve
Learning Prescriptive ReLU Networks
Which Frequencies do CNNs Need? Emergent Bottleneck Structure in Feature Learning
Learning inverse folding from millions of predicted structures
Functional Output Regression with Infimal Convolution: Exploring the Huber and ε-insensitive Losses
Memory Efficient Online Meta Learning
Fuzzy Substructure Discovery
Dropout Reduces Underfitting
Multiple Instance Learning with Manifold Bags
A Unified Optimization Framework of ANN-SNN Conversion: Towards Optimal Mapping from Activation Values to Firing Rates
Jacobian Regularizer-based Neural Granger Causality
Streaming Sparse Principal Component Analysis
Robustness Verification for Contrastive Learning
Efficient Multi-modal Long Context Learning for Training-free Adaptation
Outlier-robust Kalman Filtering through Generalised Bayes
Factored-Reward Bandits with Intermediate Observations
Learning to Reweight Examples for Robust Deep Learning
Policy Regularization on Globally Accessible States in Cross-Dynamics Reinforcement Learning
Fast Rates in Time-Varying Strongly Monotone Games
Towards Understanding Fine-Tuning Mechanisms of LLMs via Circuit Analysis
Causal Inference Through the Structural Causal Marginal Problem
Dequantified Diffusion-Schrödinger Bridge for Density Ratio Estimation
Retroformer: Pushing the Limits of End-to-end Retrosynthesis Transformer
Analysis and Optimization of Graph Decompositions by Lifted Multicuts
Gromov-Wasserstein Learning for Graph Matching and Node Embedding
Strategic A/B testing via Maximum Probability-driven Two-armed Bandit
Learning Latent Dynamic Robust Representations for World Models
Discrete Key-Value Bottleneck
DUNIA: Pixel-Sized Embeddings via Cross-Modal Alignment for Earth Observation Applications
The Flan Collection: Designing Data and Methods for Effective Instruction Tuning
Consistent Nonparametric Methods for Network Assisted Covariate Estimation
Neural Stochastic Differential Games for Time-series Analysis
SAFE: Finding Sparse and Flat Minima to Improve Pruning
Decision-Theoretic Learning in an Action System
Generalized skewing for functions with continuous and nominal attributes
Improved and Oracle-Efficient Online ℓ1-Multicalibration
Armijo Line-search Can Make (Stochastic) Gradient Descent Provably Faster
Augment and Reduce: Stochastic Inference for Large Categorical Distributions
Sparse Double Descent: Where Network Pruning Aggravates Overfitting
Boosting multi-step autoregressive forecasts
Fast boosting using adversarial bandits
On Well-posedness and Minimax Optimal Rates of Nonparametric Q-function Estimation in Off-policy Evaluation
Generative Enzyme Design Guided by Functionally Important Sites and Small-Molecule Substrates
Team Belief DAG: Generalizing the Sequence Form to Team Games for Fast Computation of Correlated Team Max-Min Equilibria via Regret Minimization
Learning Probabilistic Models for Decision-Theoretic Navigation of Mobile Robots
Align Your Steps: Optimizing Sampling Schedules in Diffusion Models
Does Generation Require Memorization? Creative Diffusion Models using Ambient Diffusion
A reproducing kernel Hilbert space framework for pairwise time series distances
Steerable 3D Spherical Neurons
Learning Distance for Sequences by Learning a Ground Metric
Model-based Reinforcement Learning with Scalable Composite Policy Gradient Estimators
Active learning for directed exploration of complex systems
Stay-Positive: A Case for Ignoring Real Image Features in Fake Image Detection
FDGen: A Fairness-Aware Graph Generation Model
Faster Fundamental Graph Algorithms via Learned Predictions
Semi-supervised graph clustering: a kernel approach
Achieving Minimax Rates in Pool-Based Batch Active Learning
Public Data-Assisted Mirror Descent for Private Model Training
Towards Black-box Iterative Machine Teaching
\(\propto\)SVM for Learning with Label Proportions
Efficient Training of Robust Decision Trees Against Adversarial Examples
PolySketchFormer: Fast Transformers via Sketching Polynomial Kernels
Multi-View Clustering and Feature Learning via Structured Sparsity
Weak Proxies are Sufficient and Preferable for Fairness with Missing Sensitive Attributes
Discriminative Adversarial Search for Abstractive Summarization
Low-Switching Policy Gradient with Exploration via Online Sensitivity Sampling
Scaling Out-of-Distribution Detection for Real-World Settings
A Generalizable Physics-Enhanced State Space Model for Long-Term Dynamics Forecasting in Complex Environments
A Differentiable Point Process with Its Application to Spiking Neural Networks
A General Representation-Based Approach to Multi-Source Domain Adaptation
Probabilistic Group Mask Guided Discrete Optimization for Incremental Learning
Local dependent components
QUEST: Query-Aware Sparsity for Efficient Long-Context LLM Inference
Sublinear Time Nearest Neighbor Search over Generalized Weighted Space
Differential Coding for Training-Free ANN-to-SNN Conversion
Multi-agent Learning Experiments on Repeated Matrix Games
CURI: A Benchmark for Productive Concept Learning Under Uncertainty
A Theoretical and Empirical Study of a Noise-Tolerant Algorithm to Learn Geormetric Patterns
Concept boundary detection for speeding up SVMs
Free to Choose: Investigating the Sample Complexity of Active Learning of Real Valued Functions
Discovering Hierarchy in Reinforcement Learning with HEXQ
Stein Variational Message Passing for Continuous Graphical Models
Expert Learning through Generalized Inverse Multiobjective Optimization: Models, Insights, and Algorithms
Provable Length Generalization in Sequence Prediction via Spectral Filtering
Online learning of conditionally I.I.D. data
Spectral Clustering with Graph Neural Networks for Graph Pooling
On the Error-Propagation of Inexact Hotelling's Deflation for Principal Component Analysis
Leveraging Node Attributes for Incomplete Relational Data
FlexiClip: Locality-Preserving Free-Form Character Animation
sciLaMA: A Single-Cell Representation Learning Framework to Leverage Prior Knowledge from Large Language Models
Efficient Bias-Span-Constrained Exploration-Exploitation in Reinforcement Learning
Context-Aware Online Collective Inference for Templated Graphical Models
How rotational invariance of common kernels prevents generalization in high dimensions
On the Necessity of Irrelevant Variables
Projection Optimization: A General Framework for Multi-Objective and Multi-Group RLHF
Fair Neighbor Embedding
Conditioning Diffusions Using Malliavin Calculus
Q2: Memory-Based Active Learning for Optimizing Noisy Continuous Functions
Exploiting Relations Among Concepts to Acquire Weakly Labeled Training Data
AdaDecode: Accelerating LLM Decoding with Adaptive Layer Parallelism
Equivalence is All: A Unified View for Self-supervised Graph Learning
Enhancing Treatment Effect Estimation via Active Learning: A Counterfactual Covering Perspective
Even Faster Accelerated Coordinate Descent Using Non-Uniform Sampling
Double-Loop Unadjusted Langevin Algorithm
Inference-Time Decomposition of Activations (ITDA): A Scalable Approach to Interpreting Large Language Models
What Language Model Architecture and Pretraining Objective Works Best for Zero-Shot Generalization?
Classification under Nuisance Parameters and Generalized Label Shift in Likelihood-Free Inference
Experimental Knowledge Acquisition for Planning
Identification of the Adversary from a Single Adversarial Example
Double Sampling Randomized Smoothing
Semi-Supervised Classification Based on Classification from Positive and Unlabeled Data
Understanding the Impact of Introducing Constraints at Inference Time on Generalization Error
Online learning with kernel losses
How to Fill the Optimum Set? Population Gradient Descent with Harmless Diversity
Careful with that Scalpel: Improving Gradient Surgery with an EMA
Conditional Sparse Coding and Grouped Multivariate Regression
Beyond Finite Layer Neural Networks: Bridging Deep Architectures and Numerical Differential Equations
Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging
Stealthy Imitation: Reward-guided Environment-free Policy Stealing
Learning From Entailment: An Application to Propositional Horn Sentences
EiG-Search: Generating Edge-Induced Subgraphs for GNN Explanation in Linear Time
The Sharpness Disparity Principle in Transformers for Accelerating Language Model Pre-Training
Quantifying Availability and Discovery in Recommender Systems via Stochastic Reachability
LongVU: Spatiotemporal Adaptive Compression for Long Video-Language Understanding
Second-Order Kernel Online Convex Optimization with Adaptive Sketching
Coherent Matrix Completion
Code as Reward: Empowering Reinforcement Learning with VLMs
Learning Interaction Kernels for Agent Systems on Riemannian Manifolds
Monotone, Bi-Lipschitz, and Polyak-Łojasiewicz Networks
Plan Your Target and Learn Your Skills: Transferable State-Only Imitation Learning via Decoupled Policy Optimization
Recursive Partitioning for Personalization using Observational Data
The Limits of Predicting Agents from Behaviour
Distillation of Discrete Diffusion through Dimensional Correlations
Efficient Rule Ensemble Learning using Hierarchical Kernels
Lazy Agents: A New Perspective on Solving Sparse Reward Problem in Multi-agent Reinforcement Learning
Constrained Optimization via Exact Augmented Lagrangian and Randomized Iterative Sketching
Communication-Efficient Distributed Optimization with Quantized Preconditioners
Algorithms for portfolio management based on the Newton method
Learning to Trust Bellman Updates: Selective State-Adaptive Regularization for Offline RL
Predictor-Corrector Policy Optimization
Learning Comprehensible Descriptions of Multivariate Time Series
A probabilistic framework for multi-view feature learning with many-to-many associations via neural networks
On the Effects of Artificial Data Modification
Active Learning for Accurate Estimation of Linear Models
Difference of submodular minimization via DC programming
The dynamic hierarchical Dirichlet process
Reinforcement Learning with General Utilities: Simpler Variance Reduction and Large State-Action Space
Genetic Programming and Deductive-Inductive Learning: A Multi-Strategy Approach
Is Generator Conditioning Causally Related to GAN Performance?
Detecting Biomedical Named Entities in COVID-19 Texts
Bayesian Optimal Active Search and Surveying
GRATH: Gradual Self-Truthifying for Large Language Models
PPDiff: Diffusing in Hybrid Sequence-Structure Space for Protein-Protein Complex Design
Random Matrix Improved Covariance Estimation for a Large Class of Metrics
Efficient ANN-SNN Conversion with Error Compensation Learning
A Graph is Worth K Words: Euclideanizing Graph using Pure Transformer
Distribution to Distribution Regression
AutoAL: Automated Active Learning with Differentiable Query Strategy Search
A Game-Theoretic Framework for Managing Risk in Multi-Agent Systems
CtrlFormer: Learning Transferable State Representation for Visual Control via Transformer
VinT-6D: A Large-Scale Object-in-hand Dataset from Vision, Touch and Proprioception
BAGEL: Bootstrapping Agents by Guiding Exploration with Language
On the Analysis of Complex Backup Strategies in Monte Carlo Tree Search
Policy-conditioned Environment Models are More Generalizable
AutoOS: Make Your OS More Powerful by Exploiting Large Language Models
Optimal Recurrent Network Topologies for Dynamical Systems Reconstruction
Bayesian Nonparametrics for Offline Skill Discovery
Olica: Efficient Structured Pruning of Large Language Models without Retraining
On the Number of Linear Regions of Convolutional Neural Networks
Compressing tree ensembles through Level-wise Optimization and Pruning
Integrated Hardware Architecture and Device Placement Search
Learning Intuitive Policies Using Action Features
Understanding MCMC Dynamics as Flows on the Wasserstein Space
Adaptive Stochastic Natural Gradient Method for One-Shot Neural Architecture Search
Multiple Instance Regression
Information Filters and Their Implementation in the SYLLOG System
On the Difficulty of Defending Self-Supervised Learning against Model Extraction
Average Sensitivity of Hierarchical k-Median Clustering
Enforcing Hard Constraints with Soft Barriers: Safe Reinforcement Learning in Unknown Stochastic Environments
UPOCR: Towards Unified Pixel-Level OCR Interface
Weighted Order Statistic Classifiers with Large Rank-Order Margin
Classes of Multiagent Q-learning Dynamics with epsilon-greedy Exploration
A Physics-Based Model Prior for Object-Oriented MDPs
Long Range Propagation on Continuous-Time Dynamic Graphs
Semi-Autoregressive Energy Flows: Exploring Likelihood-Free Training of Normalizing Flows
Distribution-Free Calibration Guarantees for Histogram Binning without Sample Splitting
MultiPDENet: PDE-embedded Learning with Multi-time-stepping for Accelerated Flow Simulation
Test-Time Adaptation for Online Vision-Language Navigation with Feedback-based Reinforcement Learning
Beyond Cropped Regions: New Benchmark and Corresponding Baseline for Chinese Scene Text Retrieval in Diverse Layouts
Achieving Lossless Gradient Sparsification via Mapping to Alternative Space in Federated Learning
Asymptotics of feature learning in two-layer networks after one gradient-step
Handling Heterogeneous Curvatures in Bandit LQR Control
Generalized Recursive Splitting Algorithms for Learning Hybrid Concepts
Equivariant Networks for Pixelized Spheres
Anytime Marginal MAP Inference
Video-Enhanced Offline Reinforcement Learning: A Model-Based Approach
Leveraging Label Non-Uniformity for Node Classification in Graph Neural Networks
Modularized Self-Reflected Video Reasoner for Multimodal LLM with Application to Video Question Answering
Conditional Common Entropy for Instrumental Variable Testing and Partial Identification
MOKD: Cross-domain Finetuning for Few-shot Classification via Maximizing Optimized Kernel Dependence
Approximate Bayesian Computation with Domain Expert in the Loop
Bisection-Based Pricing for Repeated Contextual Auctions against Strategic Buyer
Nonparametric Hamiltonian Monte Carlo
Generating Chain-of-Thoughts with a Pairwise-Comparison Approach to Searching for the Most Promising Intermediate Thought
Mixture Models for Diverse Machine Translation: Tricks of the Trade
On the Asymptotic Distribution of the Minimum Empirical Risk
A Kernel Independence Test for Random Processes
Vector-Valued Control Variates
Bayesian Conditional Cointegration
Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models
Refining the Wrapper Approach - Smoothed Error Estimates for Feature Selection
Reward-Free Exploration for Reinforcement Learning
DPCore: Dynamic Prompt Coreset for Continual Test-Time Adaptation
Hybrid Inverse Reinforcement Learning
Risk Aware Benchmarking of Large Language Models
Deeply-Debiased Off-Policy Interval Estimation
HousE: Knowledge Graph Embedding with Householder Parameterization
DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning
On the Tractability of Learning from Incomplete Theories
Protein Conformation Generation via Force-Guided SE(3) Diffusion Models
Dropout: Explicit Forms and Capacity Control
DSP: Dynamic Sequence Parallelism for Multi-Dimensional Transformers
Dynamical Linear Bandits
Predict and Constrain: Modeling Cardinality in Deep Structured Prediction
Hierarchical Integral Probability Metrics: A distance on random probability measures with low sample complexity
Matrix Estimation for Individual Fairness
EqR: Equivariant Representations for Data-Efficient Reinforcement Learning
Heterogeneous Sufficient Dimension Reduction and Subspace Clustering
Distributed Estimation of Generalized Matrix Rank: Efficient Algorithms and Lower Bounds
An Asymptotic Test for Conditional Independence using Analytic Kernel Embeddings
Two Manifold Problems with Applications to Nonlinear System Identification
Enhancing Graph Invariant Learning from a Negative Inference Perspective
Efficiently Learning Adversarially Robust Halfspaces with Noise
Greedy Layerwise Learning Can Scale To ImageNet
Null space versus orthogonal linear discriminant analysis
Adaptive Conformal Inference by Betting
The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks
Bipartite Ranking From Multiple Labels: On Loss Versus Label Aggregation
Neural Inverse Operators for Solving PDE Inverse Problems
Large Scale Dataset Distillation with Domain Shift
A Strategy for Ranking Optimization Methods using Multiple Criteria
Coordinated Dynamic Bidding in Repeated Second-Price Auctions with Budgets
Beyond the Pareto Efficient Frontier: Constraint Active Search for Multiobjective Experimental Design
On the Expressive Power of Deep Neural Networks
Blurs Behave Like Ensembles: Spatial Smoothings to Improve Accuracy, Uncertainty, and Robustness
Efficient Deviation Types and Learning for Hindsight Rationality in Extensive-Form Games
CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learning
Nearly Optimal Algorithms for Contextual Dueling Bandits from Adversarial Feedback
A Zeroth-Order Block Coordinate Descent Algorithm for Huge-Scale Black-Box Optimization
CogReact: A Reinforced Framework to Model Human Cognitive Reaction Modulated by Dynamic Intervention
Disentangling by Factorising
Recognition and Exploitation of Contextual CLues via Incremental Meta-Learning
Convergence of the EM Algorithm for Gaussian Mixtures with Unbalanced Mixing Coefficients
Value Alignment Verification
Optimal Mini-Batch and Step Sizes for SAGA
Horizontally Scalable Submodular Maximization
Unaligned Supervision for Automatic Music Transcription in The Wild
Integrated Defense for Resilient Graph Matching
Multi-objective Bayesian Optimization using Pareto-frontier Entropy
Learning Distribution-wise Control in Representation Space for Language Models
A Subquadratic Time Algorithm for Robust Sparse Mean Estimation
Measuring Non-Expert Comprehension of Machine Learning Fairness Metrics
Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations
Privacy-Preserving Instructions for Aligning Large Language Models
A brief Review of the ChaLearn AutoML Challenge: Any-time Any-dataset Learning without Human Intervention
SpikeVideoFormer: An Efficient Spike-Driven Video Transformer with Hamming Attention and O(T) Complexity
An SE-tree based Characterization of the Induction Problem
On the Power of Foundation Models
Online Learning with Local Permutations and Delayed Feedback