yusuke1997/dblp-id-title huggingface dataset:
Time Series Representations with Hard-Coded Invariances
Optimal Robust Learning of Discrete Distributions from Batches
Revisiting Over-smoothing and Over-squashing Using Ollivier-Ricci Curvature
Decision-tree Induction from Time-series Data Based on a Standard-example Split Test
InfoSEM: A Deep Generative Model with Informative Priors for Gene Regulatory Network Inference
DRCT: Diffusion Reconstruction Contrastive Training towards Universal Detection of Diffusion Generated Images
Teaching Language Models to Critique via Reinforcement Learning
GEP-PG: Decoupling Exploration and Exploitation in Deep Reinforcement Learning Algorithms
Position: Application-Driven Innovation in Machine Learning
A Unified Theoretical Analysis of Private and Robust Offline Alignment: from RLHF to DPO
Evaluating the Implicit Midpoint Integrator for Riemannian Hamiltonian Monte Carlo
PAPM: A Physics-aware Proxy Model for Process Systems
Optimal Reinsertion: A New Search Operator for Accelerated and More Accurate Bayesian Network Structure Learning
Learning to select for a predefined ranking
Towards a Mechanistic Explanation of Diffusion Model Generalization
Optimal rates for stochastic convex optimization under Tsybakov noise condition
A maximum entropy approach to species distribution modeling
Hierarchical Programmatic Reinforcement Learning via Learning to Compose Programs
Fast active learning for pure exploration in reinforcement learning
The Case against Accuracy Estimation for Comparing Induction Algorithms
Fourier Controller Networks for Real-Time Decision-Making in Embodied Learning
NExT-GPT: Any-to-Any Multimodal LLM
Robust Learning-Augmented Caching: An Experimental Study
RULEBREAKERS: Challenging LLMs at the Crossroads between Formal Logic and Human-like Reasoning
Learning Time-Varying Multi-Region Brain Communications via Scalable Markovian Gaussian Processes
Discovering the Structure of Partial Differential Equations from Example Behaviour
Interpretable and Generalizable Graph Learning via Stochastic Attention Mechanism
Tabular Insights, Visual Impacts: Transferring Expertise from Tables to Images
Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Data
Why Has Predicting Downstream Capabilities of Frontier AI Models with Scale Remained Elusive?
Accelerated training of conditional random fields with stochastic gradient methods
RL-TOPS: An Architecture for Modularity and Re-Use in Reinforcement Learning
Not All Tokens Matter All The Time: Dynamic Token Aggregation Towards Efficient Detection Transformers
Knowledge Intensive Induction
The Differentiable Cross-Entropy Method
Learning with Multiple Complementary Labels
Propensity Matters: Measuring and Enhancing Balancing for Recommendation
Stochastic Blockmodels meet Graph Neural Networks
On Enhancing Expressive Power via Compositions of Single Fixed-Size ReLU Network
Input Warping for Bayesian Optimization of Non-Stationary Functions
MoE-RBench: Towards Building Reliable Language Models with Sparse Mixture-of-Experts
Compact Optimality Verification for Optimization Proxies
Stochastic Neighbor Compression
When Does Data Augmentation Help With Membership Inference Attacks?
Simulating Stages of Human Cognitive Development With Connectionist Models
Using Optimal Dependency-Trees for Combinational Optimization
Towards Practical Preferential Bayesian Optimization with Skew Gaussian Processes
Nonparametric Regression with Comparisons: Escaping the Curse of Dimensionality with Ordinal Information
Concept Discovery Through Utilization of Invariance Embedded in the Description Language
Relational Conformal Prediction for Correlated Time Series
High-Probability Bounds for Stochastic Optimization and Variational Inequalities: the Case of Unbounded Variance
Subgraph Matching Kernels for Attributed Graphs
Same Pre-training Loss, Better Downstream: Implicit Bias Matters for Language Models
Understanding Gradual Domain Adaptation: Improved Analysis, Optimal Path and Beyond
Invariant Slot Attention: Object Discovery with Slot-Centric Reference Frames
Learning Structured Latent Factors from Dependent Data:A Generative Model Framework from Information-Theoretic Perspective
A MFoM learning approach to robust multiclass multi-label text categorization
Safety-Aware Algorithms for Adversarial Contextual Bandit
Supervised Contrastive Learning from Weakly-Labeled Audio Segments for Musical Version Matching
Generalized Smooth Variational Inequalities: Methods with Adaptive Stepsizes
Agnostic Learning of Mixed Linear Regressions with EM and AM Algorithms
Convergence Problems of General-Sum Multiagent Reinforcement Learning
Non-Local Contrastive Objectives
Generalized Earley Parser: Bridging Symbolic Grammars and Sequence Data for Future Prediction
The Right Representation for Discovery: Finding the Conservation of Momentum
A Differential Entropy Estimator for Training Neural Networks
Precise Accuracy / Robustness Tradeoffs in Regression: Case of General Norms
Learning Transferable Visual Models From Natural Language Supervision
CountSketches, Feature Hashing and the Median of Three
Adaptive Regret of Convex and Smooth Functions
Variance as a Catalyst: Efficient and Transferable Semantic Erasure Adversarial Attack for Customized Diffusion Models
Graph Mixup with Soft Alignments
Predicting protein folds with structural repeats using a chain graph model
On the Implicit Bias of Dropout
Using Domain Knowledge to Improve Inductive Learning Algorithms for Diagnosis
Inducing Partially-Defined Instances with Evolutionary Algorithms
Flex3D: Feed-Forward 3D Generation with Flexible Reconstruction Model and Input View Curation
On primal and dual sparsity of Markov networks
Using Qualitative Models to Guide Inductive Learning
Nugget: Neural Agglomerative Embeddings of Text
Online Dense Subgraph Discovery via Blurred-Graph Feedback
Dimension Reduction Techniques for Training Polynomial Networks
Primphormer: Efficient Graph Transformers with Primal Representations
Instance Pruning as an Information Preserving Problem
Deep learning with COTS HPC systems
LIT: Learned Intermediate Representation Training for Model Compression
Synthetic Text Generation for Training Large Language Models via Gradient Matching
A Monte Carlo analysis of ensemble classification
Approximate Causal Effect Identification under Weak Confounding
Reducing Redundant Learning
Boosting a Strong Learner: Evidence Against the Minimum Margin
Better generalization with less data using robust gradient descent
Cover trees for nearest neighbor
Error Feedback Can Accurately Compress Preconditioners
CURL: Contrastive Unsupervised Representations for Reinforcement Learning
AutoSampling: Search for Effective Data Sampling Schedules
Issues in the Design of Operator Composition Systems
One Pass Late Fusion Multi-view Clustering
A Simple Reward-free Approach to Constrained Reinforcement Learning
Improving Screening Processes via Calibrated Subset Selection
Flora: Low-Rank Adapters Are Secretly Gradient Compressors
Likelihood Adjusted Semidefinite Programs for Clustering Heterogeneous Data
Hessian Aided Policy Gradient
Simultaneous Multi-Robot Motion Planning with Projected Diffusion Models
An Imitation Learning Approach for Cache Replacement
On the Interplay Between Misspecification and Sub-optimality Gap in Linear Contextual Bandits
Tell me why! Explanations support learning relational and causal structure
Value-Evolutionary-Based Reinforcement Learning
Policy Contrastive Imitation Learning
How to Explore with Belief: State Entropy Maximization in POMDPs
The Perils of Optimizing Learned Reward Functions: Low Training Error Does Not Guarantee Low Regret
Quadratically Regularized Subgradient Methods for Weakly Convex Optimization with Weakly Convex Constraints
Guaranteed Sparse Recovery under Linear Transformation
Pointwise Information Measures as Confidence Estimators in Deep Neural Networks: A Comparative Study
Scalable Differentiable Physics for Learning and Control
Supervised learning from multiple experts: whom to trust when everyone lies a bit
Margins, Shrinkage, and Boosting
CarbonNovo: Joint Design of Protein Structure and Sequence Using a Unified Energy-based Model
Compute Better Spent: Replacing Dense Layers with Structured Matrices
Scaling up Natural Gradient by Sparsely Factorizing the Inverse Fisher Matrix
Matrix Completion with Incomplete Side Information via Orthogonal Complement Projection
Learning Tree Conditional Random Fields
Learning Mixture Models with the Latent Maximum Entropy Principle
HarmoniCa: Harmonizing Training and Inference for Better Feature Caching in Diffusion Transformer Acceleration
State and parameter learning with PARIS particle Gibbs
Spectral clustering with inconsistent advice
A Theory of Non-Linear Feature Learning with One Gradient Step in Two-Layer Neural Networks
Leveraging Attractor Dynamics in Spatial Navigation for Better Language Parsing
Parallel Q-Learning: Scaling Off-policy Reinforcement Learning under Massively Parallel Simulation
Learning to Align the Source Code to the Compiled Object Code
Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam
A Tale of Two Efficient and Informative Negative Sampling Distributions
On the difficulty of unbiased alpha divergence minimization
Improving the Model Consistency of Decentralized Federated Learning
Dissecting Submission Limit in Desk-Rejections: A Mathematical Analysis of Fairness in AI Conference Policies
Learning Without State-Estimation in Partially Observable Markovian Decision Processes
Adversaries Can Misuse Combinations of Safe Models
In Defense of C4.5: Notes in Learning One-Level Decision Trees
Efficient First-Order Optimization on the Pareto Set for Multi-Objective Learning under Preference Guidance
Online Variance Reduction with Mixtures
Online Submodular Resource Allocation with Applications to Rebalancing Shared Mobility Systems
Cautious Regret Minimization: Online Optimization with Long-Term Budget Constraints
Synthesizing Programs for Images using Reinforced Adversarial Learning
Offline-to-Online Reinforcement Learning with Classifier-Free Diffusion Generation
Differentially Private Boxplots
Certifying Out-of-Domain Generalization for Blackbox Functions
DAG-Based Column Generation for Adversarial Team Games
The Good, The Bad, and Why: Unveiling Emotions in Generative AI
Deep Graph Representation Learning and Optimization for Influence Maximization
Fast Provably Robust Decision Trees and Boosting
Tackling covariate shift with node-based Bayesian neural networks
SinIR: Efficient General Image Manipulation with Single Image Reconstruction
On the Power of Localized Perceptron for Label-Optimal Learning of Halfspaces with Adversarial Noise
A Kernel Theory of Modern Data Augmentation
Toward Efficient and Accurate Covariance Matrix Estimation on Compressed Data
Improving Transformer World Models for Data-Efficient RL
Surface-VQMAE: Vector-quantized Masked Auto-encoders on Molecular Surfaces
Multiple-policy Evaluation via Density Estimation
Improving generalization by controlling label-noise information in neural network weights
Incorporating Causal Prior Knowledge as Path-Constraints in Bayesian Networks and Maximal Ancestral Graphs
All-in-one simulation-based inference
Advancing DRL Agents in Commercial Fighting Games: Training, Integration, and Agent-Human Alignment
Synthetic Prompting: Generating Chain-of-Thought Demonstrations for Large Language Models
Bivariate Causal Discovery using Bayesian Model Selection
PAC Learning with Improvements
Delaunay Graph: Addressing Over-Squashing and Over-Smoothing Using Delaunay Triangulation
Heterogeneous Data Game: Characterizing the Model Competition Across Multiple Data Sources
Learning as Optimization: Stochastic Generation of Multiple Knowledge
A practical generalization of Fourier-based learning
IMTS is Worth Time × Channel Patches: Visual Masked Autoencoders for Irregular Multivariate Time Series Prediction
Learning Temporal Causal Graphs for Relational Time-Series Analysis
Learning the piece-wise constant graph structure of a varying Ising model
A Deterministic Analysis of Noisy Sparse Subspace Clustering for Dimensionality-reduced Data
Model-Based Reinforcement Learning via Latent-Space Collocation
Learning High-Order Relationships of Brain Regions
An Initial Study of an Adaptive Hierarchical Vision System
Power-Law Escape Rate of SGD
Equivariant message passing for the prediction of tensorial properties and molecular spectra
Momentum Ensures Convergence of SIGNSGD under Weaker Assumptions
Efficient Value Iteration for s-rectangular Robust Markov Decision Processes
Distributed Nyström Kernel Learning with Communications
DE-COP: Detecting Copyrighted Content in Language Models Training Data
Sliding Puzzles Gym: A Scalable Benchmark for State Representation in Visual Reinforcement Learning
Decoupling Exploration and Exploitation in Multi-Armed Bandits
Geometry Informed Tokenization of Molecules for Language Model Generation
High Probability Guarantees for Nonconvex Stochastic Gradient Descent with Heavy Tails
Towards Understanding Sharpness-Aware Minimization
On Learning Sets of Symmetric Elements
From Local Structures to Size Generalization in Graph Neural Networks
ViP: A Differentially Private Foundation Model for Computer Vision
Tight Kernel Query Complexity of Kernel Ridge Regression and Kernel $k$-means Clustering
RankSim: Ranking Similarity Regularization for Deep Imbalanced Regression
Satori: Reinforcement Learning with Chain-of-Action-Thought Enhances LLM Reasoning via Autoregressive Search
Entropy-SGD optimizes the prior of a PAC-Bayes bound: Generalization properties of Entropy-SGD and data-dependent priors
Domain Adaptation under Target and Conditional Shift
DIS-CO: Discovering Copyrighted Content in VLMs Training Data
Local Expert Autoassociators for Anomaly Detection
A kernel view of the dimensionality reduction of manifolds
Differentiable Compositional Kernel Learning for Gaussian Processes
Skip the Equations: Learning Behavior of Personalized Dynamical Systems Directly From Data
Expressive 1-Lipschitz Neural Networks for Robust Multiple Graph Learning against Adversarial Attacks
Understanding Diffusion Models by Feynman's Path Integral
Exponentially Many Local Minima in Quantum Neural Networks
Exponential Family Variational Flow Matching for Tabular Data Generation
Meta-Learners for Partially-Identified Treatment Effects Across Multiple Environments
A Theoretical Justification for Asymmetric Actor-Critic Algorithms
Multidimensional Adaptive Coefficient for Inference Trajectory Optimization in Flow and Diffusion
Stochastic Resonance with Adaptive Fuzzy Systems
The Composition Theorem for Differential Privacy
Quadratically gated mixture of experts for incomplete data classification
Residual-Conditioned Optimal Transport: Towards Structure-Preserving Unpaired and Paired Image Restoration
Risk Bounds for Transferring Representations With and Without Fine-Tuning
Demystifying Dropout
Convergence and Trade-Offs in Riemannian Gradient Descent and Riemannian Proximal Point
Pixel Recurrent Neural Networks
SGD with Arbitrary Sampling: General Analysis and Improved Rates
Momentor: Advancing Video Large Language Model with Fine-Grained Temporal Reasoning
Recycling data for multi-agent learning
Stochastic Gradient Push for Distributed Deep Learning
Learning Task-Agnostic Embedding of Multiple Black-Box Experts for Multi-Task Model Fusion
Implicit Bias of Gradient Descent for Non-Homogeneous Deep Networks
Robust Unsupervised Learning via L-statistic Minimization
A Richer Theory of Convex Constrained Optimization with Reduced Projections and Improved Rates
Stationary Latent Weight Inference for Unreliable Observations from Online Test-Time Adaptation
Modelling Behavioural Diversity for Learning in Open-Ended Games
Pairwise Maximum Likelihood For Multi-Class Logistic Regression Model With Multiple Rare Classes
Geometry-Aware Instrumental Variable Regression
Understanding Plasticity in Neural Networks
Stabilizing Transformers for Reinforcement Learning
Cell-Free Latent Go-Explore
Curiosity-driven Exploration by Self-supervised Prediction
Connect Later: Improving Fine-tuning for Robustness with Targeted Augmentations
SkexGen: Autoregressive Generation of CAD Construction Sequences with Disentangled Codebooks
Active Fine-Tuning of Multi-Task Policies
Robust CLIP: Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Models
Sublinear quantum algorithms for training linear and kernel-based classifiers
TempoRL: Learning When to Act
Stochastic Quasi-Newton Langevin Monte Carlo
Unreproducible Research is Reproducible
Detecting statistical interactions with additive groves of trees
Improved Sleeping Bandits with Stochastic Action Sets and Adversarial Rewards
Fast margin maximization via dual acceleration
Efficient Learning in Large-Scale Combinatorial Semi-Bandits
Using Background Knowledge in Concept Formation
Cell2Sentence: Teaching Large Language Models the Language of Biology
Neural Clustering Processes
On Acceleration with Noise-Corrupted Gradients
How Graph Neural Networks Learn: Lessons from Training Dynamics
From Fourier to Neural ODEs: Flow Matching for Modeling Complex Systems
Safety Reasoning with Guidelines
Unsupervised Skill Discovery with Bottleneck Option Learning
Robust One-Bit Recovery via ReLU Generative Networks: Near-Optimal Statistical Rate and Global Landscape Analysis
Exact Inference in High-order Structured Prediction
Reinforcement Learning from Reachability Specifications: PAC Guarantees with Expected Conditional Distance
Rigging the Lottery: Making All Tickets Winners
Mitigating Over-Squashing in Graph Neural Networks by Spectrum-Preserving Sparsification
Frequencies vs. Biases: Machine Learning Problems in Natural Language Processing - Abstract
Comparing Stochastic Planning to the Acquisition of Increasingly Permissive Plans
An Infinite-Width Analysis on the Jacobian-Regularised Training of a Neural Network
Identifying biological perturbation targets through causal differential networks
MODULI: Unlocking Preference Generalization via Diffusion Models for Offline Multi-Objective Reinforcement Learning
Ringmaster ASGD: The First Asynchronous SGD with Optimal Time Complexity
Classification from Positive, Unlabeled and Biased Negative Data
Examining and Combating Spurious Features under Distribution Shift
Analysis of stochastic Lanczos quadrature for spectrum approximation
Can Large Language Models Understand Intermediate Representations in Compilers?
Smooth p-Wasserstein Distance: Structure, Empirical Approximation, and Statistical Applications
CasCast: Skillful High-resolution Precipitation Nowcasting via Cascaded Modelling
Learning Multi-View Neighborhood Preserving Projections
Scaling Tractable Probabilistic Circuits: A Systems Perspective
Machine Learning, Proceedings of the Twenty-Fifth International Conference (ICML 2008), Helsinki, Finland, June 5-9, 2008
Looking Beyond the Top-1: Transformers Determine Top Tokens in Order
Exploring the Landscape of Spatial Robustness
Fast Parametric Learning with Activation Memorization
RobustFill: Neural Program Learning under Noisy I/O
Algorithms for Optimizing the Ratio of Submodular Functions
The Non-IID Data Quagmire of Decentralized Machine Learning
An Explicit Frame Construction for Normalizing 3D Point Clouds
When does Privileged information Explain Away Label Noise?
Online Learning and Pricing with Reusable Resources: Linear Bandits with Sub-Exponential Rewards
Shape Constraints for Set Functions
HardCoRe-NAS: Hard Constrained diffeRentiable Neural Architecture Search
Fixed-point algorithms for learning determinantal point processes
Mini-Batch Primal and Dual Methods for SVMs
Specializing Smaller Language Models towards Multi-Step Reasoning
A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization
Message Passing Algorithms for the Dirichlet Diffusion Tree
Discovering a Zero (Zero-Vector Class of Machine Learning)
Compressing Neural Networks using the Variational Information Bottleneck
Modified MMI/MPE: a direct evaluation of the margin in speech recognition
Random Function Priors for Correlation Modeling
Kernels for Semi-Structured Data
Hierarchical Dirichlet Scaling Process
Greed is Still Good: Maximizing Monotone Submodular+Supermodular (BP) Functions
Non-stationary Online Convex Optimization with Arbitrary Delays
Overcoming Multi-step Complexity in Multimodal Theory-of-Mind Reasoning: A Scalable Bayesian Planner
Adapting two-class support vector classification methods to many class problems
PixelCNN Models with Auxiliary Variables for Natural Image Modeling
Learn to Vaccinate: Combining Structure Learning and Effective Vaccination for Epidemic and Outbreak Control
Online Pricing with Offline Data: Phase Transition and Inverse Square Law
Learning Functions in k-DNF from Reinforcement
Influence Maximization in Continuous Time Diffusion Networks
Collapse-Proof Non-Contrastive Self-Supervised Learning
A Generalized Loop Correction Method for Approximate Inference in Graphical Models
CRANE: Reasoning with constrained LLM generation
Private Federated Learning using Preference-Optimized Synthetic Data
Low-Rank Bottleneck in Multi-head Attention Models
Suboptimal Solution Path Algorithm for Support Vector Machine
Graph Homomorphism Convolution
Output Space Search for Structured Prediction
On the Interaction between Norm and Dimensionality: Multiple Regimes in Learning
Estimating the Number and Effect Sizes of Non-null Hypotheses
Distribution Free Prediction Sets for Node Classification
Hawkes Processes with Stochastic Excitations
Qualitative Multi-Armed Bandits: A Quantile-Based Approach
Sparse meta-Gaussian information bottleneck
The Benefits of Mixup for Feature Learning
A Symbolic Algorithm for Computing Coefficients' Accuracy in Regression
How Transformers Learn Regular Language Recognition: A Theoretical Study on Training Dynamics and Implicit Bias
Design for an Optimal Probe
Quantum Boosting
Learning Single Index Models with Diffusion Priors
Addressing the Curse of Imbalanced Training Sets: One-Sided Selection
Fast Computation of Wasserstein Barycenters
Pursuing Overall Welfare in Federated Learning through Sequential Decision Making
SinFusion: Training Diffusion Models on a Single Image or Video
No-regret learning in convex games
Physics of Language Models: Part 3.1, Knowledge Storage and Extraction
Online Selection Problems against Constrained Adversary
Thurstonian Boltzmann Machines: Learning from Multiple Inequalities
Few-Shot Neural Architecture Search
Parallel Droplet Control in MEDA Biochips using Multi-Agent Reinforcement Learning
Supervised clustering with support vector machines
Neural Continuous-Discrete State Space Models for Irregularly-Sampled Time Series
Fast and Flexible Inference of Joint Distributions from their Marginals
Gaussian Process Optimization with Mutual Information
On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning
From Self-Attention to Markov Models: Unveiling the Dynamics of Generative Transformers
Hyperparameters in Reinforcement Learning and How To Tune Them
POQD: Performance-Oriented Query Decomposer for Multi-vector retrieval
A Distributed Variational Inference Framework for Unifying Parallel Sparse Gaussian Process Regression Models
Predictable MDP Abstraction for Unsupervised Model-Based RL
Percentile optimization in uncertain Markov decision processes with application to efficient exploration
Class-Weighted Classification: Trade-offs and Robust Approaches
Prioritized Training on Points that are Learnable, Worth Learning, and not yet Learnt
Exploring the Limits of Model-Targeted Indiscriminate Data Poisoning Attacks
On PI Controllers for Updating Lagrange Multipliers in Constrained Optimization
Learning Symmetric Embeddings for Equivariant World Models
Low-loss connection of weight vectors: distribution-based approaches
Width and Depth Limits Commute in Residual Networks
Graph External Attention Enhanced Transformer
Stochastic Bouncy Particle Sampler
FedScale: Benchmarking Model and System Performance of Federated Learning at Scale
Climbing the Tower of Babel: Unsupervised Multilingual Learning
A Computationally Efficient Algorithm for Infinite-Horizon Average-Reward Linear MDPs
BetterV: Controlled Verilog Generation with Discriminative Guidance