yusuke1997/dblp-id-title huggingface dataset:
Understanding Geometry of Encoder-Decoder CNNs
Scaling Properties of Deep Residual Networks
Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speech
Reference Neural Operators: Learning the Smooth Dependence of Solutions of PDEs on Geometric Deformations
Inferring DQN structure for high-dimensional continuous control
Stationarity without mean reversion in improper Gaussian processes
Online Isolation Forest
A Provable Generalized Tensor Spectral Method for Uniform Hypergraph Partitioning
To Smooth or Not? When Label Smoothing Meets Noisy Labels
Regularizing with Pseudo-Negatives for Continual Self-Supervised Learning
ETTA: Elucidating the Design Space of Text-to-Audio Models
Median Matrix Completion: from Embarrassment to Optimality
Discrete Deep Feature Extraction: A Theory and New Architectures
Bigger, Better, Faster: Human-level Atari with human-level efficiency
Realistic Unsupervised CLIP Fine-tuning with Universal Entropy Optimization
Reducing Item Discrepancy via Differentially Private Robust Embedding Alignment for Privacy-Preserving Cross Domain Recommendation
Learning Strategic Language Agents in the Werewolf Game with Iterative Latent Space Policy Optimization
Educating Text Autoencoders: Latent Representation Guidance via Denoising
Preference learning made easy: Everything should be understood through win rate
The Price of Linear Time: Error Analysis of Structured Kernel Interpolation
Deriving Neural Architectures from Sequence and Graph Kernels
Unified Fourier-based Kernel and Nonlinearity Design for Equivariant Networks on Homogeneous Spaces
PredRNN++: Towards A Resolution of the Deep-in-Time Dilemma in Spatiotemporal Predictive Learning
Asymptotically Optimal and Computationally Efficient Average Treatment Effect Estimation in A/B testing
Large-margin Weakly Supervised Dimensionality Reduction
Enhancing Vision Transformer: Amplifying Non-Linearity in Feedforward Network Module
Robust probabilistic projections
Generating Accurate Rule Sets Without Global Optimization
Data Summarization at Scale: A Two-Stage Submodular Approach
Subspace Optimization for Large Language Models with Convergence Guarantees
Stochastic Optimization with Arbitrary Recurrent Data Sampling
Understanding Server-Assisted Federated Learning in the Presence of Incomplete Client Participation
Augmented World Models Facilitate Zero-Shot Dynamics Generalization From a Single Offline Environment
Proposer-Agent-Evaluator (PAE): Autonomous Skill Discovery For Foundation Model Internet Agents
A Tight Integration of Deductive Learning
Cost-efficient Collaboration between On-device and Cloud Language Models
A Modular Analysis of Provable Acceleration via Polyak's Momentum: Training a Wide ReLU Network and a Deep Linear Network
Choice Set Optimization Under Discrete Choice Models of Group Decisions
LatentGNN: Learning Efficient Non-local Relations for Visual Recognition
Consideration of Risk in Reinforcement Learning
Acquisition of Stand-up Behavior by a Real Robot using Hierarchical Reinforcement Learning
Rethinking Independent Cross-Entropy Loss For Graph-Structured Data
Adversarial Robustness Against the Union of Multiple Perturbation Models
Revisiting Label Smoothing and Knowledge Distillation Compatibility: What was Missing?
A Generalized Reinforcement-Learning Model: Convergence and Applications
Test-Time Selective Adaptation for Uni-Modal Distribution Shift in Multi-Modal Data
Latent Normalizing Flows for Discrete Sequences
UMD: Unsupervised Model Detection for X2X Backdoor Attacks
A Coherent Interpretation of AUC as a Measure of Aggregated Classification Performance
Indirectly Parameterized Concrete Autoencoders
Shuffle Private Linear Contextual Bandits
BERT and PALs: Projected Attention Layers for Efficient Adaptation in Multi-Task Learning
Marginalization is not Marginal: No Bad VAE Local Minima when Learning Optimal Sparse Representations
Distributed Asynchronous Optimization with Unbounded Delays: How Slow Can You Go?
EvoControl: Multi-Frequency Bi-Level Control for High-Frequency Continuous Control
Locally Private k-Means in One Round
An Inference-Based Policy Gradient Method for Learning Options
Policy Guided Tree Search for Enhanced LLM Reasoning
Proceedings of the 30th International Conference on Machine Learning, ICML 2013, Atlanta, GA, USA, 16-21 June 2013
Entropy-based criterion in categorical clustering
Reinforcement Learning with Prototypical Representations
Graph As Point Set
BoxLM: Unifying Structures and Semantics of Medical Concepts for Diagnosis Prediction in Healthcare
Semismooth Newton Algorithm for Efficient Projections onto ℓ 1, ∞ -norm Ball
From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection
Detecting Influence Structures in Multi-Agent Reinforcement Learning
GenZSL: Generative Zero-Shot Learning Via Inductive Variational Autoencoder
A Joint Model of Language and Perception for Grounded Attribute Learning
Tractable nonparametric Bayesian inference in Poisson processes with Gaussian process intensities
Robust Consensus Anchor Learning for Efficient Multi-view Subspace Clustering
On one method of non-diagonal regularization in sparse Bayesian learning
High-Probability Bound for Non-Smooth Non-Convex Stochastic Optimization with Heavy Tails
WeGeFT: Weight‑Generative Fine-Tuning for Multi-Faceted Efficient Adaptation of Large Models
Bootstrapping Fisher Market Equilibrium and First-Price Pacing Equilibrium
Omni-Granular Ego-Semantic Propagation for Self-Supervised Graph Representation Learning
Correct-N-Contrast: a Contrastive Approach for Improving Robustness to Spurious Correlations
Why Is Spatial Reasoning Hard for VLMs? An Attention Mechanism Perspective on Focus Areas
Total Variation, Cheeger Cuts
Federated Oriented Learning: A Practical One-Shot Personalized Federated Learning Framework
Parameterized Algorithms for the Matrix Completion Problem
The Catalog Problem: Clustering and Ordering Variable-Sized Sets
MD tree: a model-diagnostic tree grown on loss landscape
Weight matrices compression based on PDB model in deep neural networks
Unveiling Markov heads in Pretrained Language Models for Offline Reinforcement Learning
Learning Fused State Representations for Control from Multi-View Observations
Noise-Tolerant Recursive Best-First Induction
CLIMB: Data Foundations for Large Scale Multimodal Clinical Foundation Models
Flextron: Many-in-One Flexible Large Language Model
Towards Detection of Faulty Traffic Sensors in Real-Time
Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement Learning
COLA: Consistent Learning with Opponent-Learning Awareness
Bayesian sparse sampling for on-line reward optimization
ARMS: Antithetic-REINFORCE-Multi-Sample Gradient for Binary Variables
Scaling Reinforcement Learning Algorithms by Learning Variable Temporal Resolution Models
Top-down particle filtering for Bayesian decision trees
Cost-Optimal Learning of Causal Graphs
SeedLoRA: A Fusion Approach to Efficient LLM Fine-Tuning
Dual Perspective of Label-Specific Feature Learning for Multi-Label Classification
Learning Regions of Interest for Bayesian Optimization with Adaptive Level-Set Estimation
Modeling Temporal Data as Continuous Functions with Stochastic Process Diffusion
Decentralized SGD and Average-direction SAM are Asymptotically Equivalent
SVM optimization: inverse dependence on training set size
Look Ahead or Look Around? A Theoretical Comparison Between Autoregressive and Masked Pretraining
CARTE: Pretraining and Transfer for Tabular Learning
Large-Scale Graph Neural Architecture Search
Alberta Wells Dataset: Pinpointing Oil and Gas Wells from Satellite Imagery
Differentially Private Correlation Clustering
Intersecting singularities for multi-structured estimation
Constructive Feature Learning and the Development of Visual Expertise
Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval
FedNew: A Communication-Efficient and Privacy-Preserving Newton-Type Method for Federated Learning
Discriminative versus generative parameter and structure learning of Bayesian network classifiers
TabICL: A Tabular Foundation Model for In-Context Learning on Large Data
NeRFool: Uncovering the Vulnerability of Generalizable Neural Radiance Fields against Adversarial Perturbations
Spherical Hamiltonian Monte Carlo for Constrained Target Distributions
Distributed Traffic Flow Prediction with Label Proportions: From in-Network towards High Performance Computation with MPI
A Universal Class of Sharpness-Aware Minimization Algorithms
Constrain Alignment with Sparse Autoencoders
Efficient learning of Naive Bayes classifiers under class-conditional classification noise
Emotional Face-to-Speech
BILBO: BILevel Bayesian Optimization
Using Perturbation to Improve Goodness-of-Fit Tests based on Kernelized Stein Discrepancy
Image Restoration with Mean-Reverting Stochastic Differential Equations
Scalable Set Encoding with Universal Mini-Batch Consistency and Unbiased Full Set Gradient Approximation
Cross Language Text Classification via Subspace Co-regularized Multi-view Learning
Maxout Networks
Differential Privacy, Linguistic Fairness, and Training Data Influence: Impossibility and Possibility Theorems for Multilingual Language Models
WARM: On the Benefits of Weight Averaged Reward Models
Less is More: Federated Graph Learning with Alleviating Topology Heterogeneity from A Causal Perspective
Feature-Critic Networks for Heterogeneous Domain Generalization
Particle Denoising Diffusion Sampler
Proceedings of the Sixth International Workshop on Machine Learning (ML 1989), Cornell University, Ithaca, New York, USA, June 26-27, 1989
PINA: Leveraging Side Information in eXtreme Multi-label Classification via Predicted Instance Neighborhood Aggregation
Learning Classifiers That Induce Markets
State-Regularized Recurrent Neural Networks
Meta-learning for Mixed Linear Regression
Learning to Select Good Title Words: An New Approach based on Reverse Information Retrieval
Taming the Curse of Dimensionality: Discrete Integration by Hashing and Optimization
Explanation Based Learning as Constrained Search
Zeroth-Order Methods for Constrained Nonconvex Nonsmooth Stochastic Optimization
Practical Almost-Linear-Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering
Optimizing Temperature for Language Models with Multi-Sample Inference
Double Reinforcement Learning for Efficient and Robust Off-Policy Evaluation
Sparse Reinforcement Learning via Convex Optimization
Characterizing the Representer Theorem
Characterizing and Overcoming the Greedy Nature of Learning in Multi-modal Deep Neural Networks
Stochastic Gauss-Newton Algorithms for Nonconvex Compositional Optimization
SpENCNN: Orchestrating Encoding and Sparsity for Fast Homomorphically Encrypted Neural Network Inference
Binary embeddings with structured hashed projections
Probabilistic Boolean Tensor Decomposition
Bayesian Optimisation over Multiple Continuous and Categorical Inputs
AdvI2I: Adversarial Image Attack on Image-to-Image Diffusion Models
Improving Policy Optimization with Generalist-Specialist Learning
A Unified Framework for Learning with Nonlinear Model Classes from Arbitrary Linear Samples
Multi-task feature and kernel selection for SVMs
Difference Advantage Estimation for Multi-Agent Policy Gradients
Strategic Classification is Causal Modeling in Disguise
Generating images with sparse representations
When and How Does Known Class Help Discover Unknown Ones? Provable Understanding Through Spectral Analysis
NeWRF: A Deep Learning Framework for Wireless Radiation Field Reconstruction and Channel Prediction
Energy-Efficient Gaussian Processes Using Low-Precision Arithmetic
OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning
Classification using discriminative restricted Boltzmann machines
Lazy Partial Evaluation: An Integration of Explanation-Based Generalization and Partial Evaluation
Tight and fast generalization error bound of graph embedding in metric space
Listening to the noise: Blind Denoising with Gibbs Diffusion
Maximum likelihood rule ensembles
Stochastic Encodings for Active Feature Acquisition
K-means clustering using random matrix sparsification
Asymptotic Bayesian generalization error when training and test distributions are different
Multiplicative Normalizing Flows for Variational Bayesian Neural Networks
Large Language Models to Diffusion Finetuning
What Are Bayesian Neural Network Posteriors Really Like?
Aggregating Ordinal Labels from Crowds by Minimax Conditional Entropy
Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing
Standardized Interpretable Fairness Measures for Continuous Risk Scores
Action Refinement in Reinforcement Learning by Probability Smoothing
Graphical Models Meet Bandits: A Variational Thompson Sampling Approach
Stochastic Variance Reduction for Nonconvex Optimization
A Mathematical Framework for AI-Human Integration in Work
The Effect of the Input Density Distribution on Kernel-based Classifiers
Generalisation error in learning with random features and the hidden manifold model
Stability and Generalization for Stochastic Recursive Momentum-based Algorithms for (Strongly-)Convex One to K-Level Stochastic Optimizations
Linkage and Autocorrelation Cause Feature Selection Bias in Relational Learning
What Dense Graph Do You Need for Self-Attention?
Active Learning for Matching Problems
Do Efficient Transformers Really Save Computation?
Exploration by Optimization with Hybrid Regularizers: Logarithmic Regret with Adversarial Robustness in Partial Monitoring
A Flexible Latent Space Model for Multilayer Networks
Generalization Guarantees for Sparse Kernel Approximation with Entropic Optimal Features
Self-Correcting Self-Consuming Loops for Generative Model Training
Cross-Domain 3D Equivariant Image Embeddings
Learning Generative Models across Incomparable Spaces
Cooperative Online Learning in Stochastic and Adversarial MDPs
Modeling Temporal Evolution and Multiscale Structure in Networks
DPZero: Private Fine-Tuning of Language Models without Backpropagation
Forward Operator Estimation in Generative Models with Kernel Transfer Operators
Universal Joint Approximation of Manifolds and Densities by Simple Injective Flows
Learning Submodular Losses with the Lovasz Hinge
Feynman-Kac Correctors in Diffusion: Annealing, Guidance, and Product of Experts
Online Unrelated Machine Load Balancing with Predictions Revisited
GraphCleaner: Detecting Mislabelled Samples in Popular Graph Learning Benchmarks
Learning Causal Dynamics Models in Object-Oriented Environments
When Sparsity Meets Contrastive Models: Less Graph Data Can Bring Better Class-Balanced Representations
SpikeLM: Towards General Spike-Driven Language Modeling via Elastic Bi-Spiking Mechanisms
Can Gaussian Sketching Converge Faster on a Preconditioned Landscape?
Leveraging VLM-Based Pipelines to Annotate 3D Objects
Demystifying Disagreement-on-the-Line in High Dimensions
The Minimum Description Length Principle and Categorical Theories
Differentially Private Decentralized Learning with Random Walks
SPACE: Your Genomic Profile Predictor is a Powerful DNA Foundation Model
Linearization Turns Neural Operators into Function-Valued Gaussian Processes
Entropic Graph-based Posterior Regularization
Settling the Reward Hypothesis
Simple and near-optimal algorithms for hidden stratification and multi-group learning
On Differential Privacy for Adaptively Solving Search Problems via Sketching
Training Linear Neural Networks: Non-Local Convergence and Complexity Results
Towards Understanding Generalization of Graph Neural Networks
A Connection between One-Step RL and Critic Regularization in Reinforcement Learning
The Kernel-Adatron Algorithm: A Fast and Simple Learning Procedure for Support Vector Machines
Multi-group Learning for Hierarchical Groups
Large-scale Distributed Dependent Nonparametric Trees
Consensus Control for Decentralized Deep Learning
Myopic Posterior Sampling for Adaptive Goal Oriented Design of Experiments
On Bayesian PCA: Automatic Dimensionality Selection and Analytic Solution
Label Distribution Learning Machine
Domain Adaptation with Asymmetrically-Relaxed Distribution Alignment
Improving LLM Safety Alignment with Dual-Objective Optimization
Message Passing Stein Variational Gradient Descent
Tutorial summary: Survey of boosting from an optimization perspective
Regularization of Neural Networks using DropConnect
Plausible Token Amplification for Improving Accuracy of Differentially Private In-Context Learning Based on Implicit Bayesian Inference
The Underlying Universal Statistical Structure of Natural Datasets
Provable Risk-Sensitive Distributional Reinforcement Learning with General Function Approximation
Stable Estimation of Heterogeneous Treatment Effects
Private Statistical Estimation of Many Quantiles
PAL: Program-aided Language Models
Reward-Free RL is No Harder Than Reward-Aware RL in Linear Markov Decision Processes
Ladder Capsule Network
Combining Nearest Neighbor Classifiers Through Multiple Feature Subsets
Differentiable Annealed Importance Sampling Minimizes The Jensen-Shannon Divergence Between Initial and Target Distribution
Sketched Ridgeless Linear Regression: The Role of Downsampling
Task-aware Privacy Preservation for Multi-dimensional Data
Interpolation-based Q-learning
Predicting Lagrangian Multipliers for Mixed Integer Linear Programs
More Data Can Expand The Generalization Gap Between Adversarially Robust and Standard Models
Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks
xxAI - Beyond Explainable Artificial Intelligence
Multitask Learning: A Knowledge-Based Source of Inductive Bias
Distributed Contextual Linear Bandits with Minimax Optimal Communication Cost
Generalization Properties and Implicit Regularization for Multiple Passes SGM
On the Forward Invariance of Neural ODEs
Position Prediction as an Effective Pretraining Strategy
Local algorithms for interactive clustering
Monotonic Robust Policy Optimization with Model Discrepancy
Expectation Maximization for Weakly Labeled Data
Model-Bellman Inconsistency for Model-based Offline Reinforcement Learning
Fast inference and learning in large-state-space HMMs
Efficient learning algorithms for changing environments
InfoCons: Identifying Interpretable Critical Concepts in Point Clouds via Information Theory
The lasso, persistence, and cross-validation
Round Robin Rule Learning
Submodular Order Functions and Assortment Optimization
Fast time series classification using numerosity reduction
Cognitive model priors for predicting human decisions
Quantized Distributed Training of Large Models with Convergence Guarantees
Online Learning in Stackelberg Games with an Omniscient Follower
Efficient Error Certification for Physics-Informed Neural Networks
A new approach to data driven clustering
Probabilistic Time Series Modeling with Decomposable Denoising Diffusion Model
Provably Efficient Model-based Policy Adaptation
Distributed Batch Gaussian Process Optimization
High Probability Convergence of Stochastic Gradient Methods
Spectral clustering for multi-type relational data
Guided Structural Inference: Leveraging Priors with Soft Gating Mechanisms
Trust Regions for Explanations via Black-Box Probabilistic Certification
Label Ranking under Ambiguous Supervision for Learning Semantic Correspondences
Pruner-Zero: Evolving Symbolic Pruning Metric From Scratch for Large Language Models
Robust Structured Estimation with Single-Index Models
Nemotron-CORTEXA: Enhancing LLM Agents for Software Engineering Tasks via Improved Localization and Solution Diversity
Anomaly Detection over Noisy Data using Learned Probability Distributions
The asymptotics of semi-supervised learning in discriminative probabilistic models
C-MinHash: Improving Minwise Hashing with Circulant Permutation
simple diffusion: End-to-end diffusion for high resolution images
Improving l1-Certified Robustness via Randomized Smoothing by Leveraging Box Constraints
Structure Your Data: Towards Semantic Graph Counterfactuals
A new similarity measure for covariate shift with applications to nonparametric regression
A Quantitative Study of Hypothesis Selection
Fast Newton-type Methods for Total Variation Regularization
Modeling Musical Influence with Topic Models
You Always Recognize Me (YARM): Robust Texture Synthesis Against Multi-View Corruption
Learning with Discrete Multi-Valued Neurons
Generalization Bound and New Algorithm for Clean-Label Backdoor Attack
A Graph to Graphs Framework for Retrosynthesis Prediction
Latent Score-Based Reweighting for Robust Classification on Imbalanced Tabular Data
Robust Pricing in Dynamic Mechanism Design
Modeling Information Propagation with Survival Theory
Enabling Few-Shot Learning with PID Control: A Layer Adaptive Optimizer
NysADMM: faster composite convex optimization via low-rank approximation
K-means in space: a radiation sensitivity evaluation
Estimating the Permanent by Nesting Importance Sampling
How Flawed Is ECE? An Analysis via Logit Smoothing
Emergence in non-neural models: grokking modular arithmetic via average gradient outer product
Impact of Decentralized Learning on Player Utilities in Stackelberg Games
Chasing Convex Functions with Long-term Constraints
Spurious Local Minima are Common in Two-Layer ReLU Neural Networks
Learning to compress images and videos
Branchformer: Parallel MLP-Attention Architectures to Capture Local and Global Context for Speech Recognition and Understanding
Blended Conditonal Gradients
Proximal Exploration for Model-guided Protein Sequence Design
Learning optimally diverse rankings over large document collections
Likelihood-free MCMC with Amortized Approximate Ratio Estimators
Transformers Get Stable: An End-to-End Signal Propagation Theory for Language Models
Compressed-VFL: Communication-Efficient Learning with Vertically Partitioned Data
Rethinking the Flat Minima Searching in Federated Learning
On Coresets for Regularized Regression
Hypothesis Filtering: A Practical Approach to Reliable Learning
Generalized Smooth Bilevel Optimization with Nonconvex Lower-Level
A Fast Optimistic Method for Monotone Variational Inequalities
Robustness and Accuracy Could Be Reconcilable by (Proper) Definition
Generalizing Knowledge Graph Embedding with Universal Orthogonal Parameterization
Integer Programming for Generalized Causal Bootstrap Designs
Optimization, Fast and Slow: Optimally Switching between Local and Bayesian Optimization
Learning Fast Approximations of Sparse Coding
Accelerated, Optimal and Parallel: Some results on model-based stochastic optimization
Learning Graph Representation via Graph Entropy Maximization
Tree-Independent Dual-Tree Algorithms
PlaySlot: Learning Inverse Latent Dynamics for Controllable Object-Centric Video Prediction and Planning
Towards Automatically-Tuned Neural Networks
Positional Encoding meets Persistent Homology on Graphs
Kona: An Efficient Privacy-Preservation Framework for KNN Classification by Communication Optimization
Scalable Normalizing Flows for Permutation Invariant Densities
Diagonal Symmetrization of Neural Network Solvers for the Many-Electron Schrödinger Equation
Meta-Learning Neural Bloom Filters
Effective Minkowski Dimension of Deep Nonparametric Regression: Function Approximation and Statistical Theories
On Learning More Concepts
On No-Regret Learning, Fictitious Play, and Nash Equilibrium
Improved SVRG for Non-Strongly-Convex or Sum-of-Non-Convex Objectives
Implicit Bias of Linear Equivariant Networks
An Analysis for Reasoning Bias of Language Models with Small Initialization
CKGConv: General Graph Convolution with Continuous Kernels
UltraTWD: Optimizing Ultrametric Trees for Tree-Wasserstein Distance
POEM: Out-of-Distribution Detection with Posterior Sampling
Meta-Gradient Boosted Decision Tree Model for Weight and Target Learning
Learning Survival Distributions with the Asymmetric Laplace Distribution
Large-Scale Learning of Embeddings with Reconstruction Sampling
Dueling Convex Optimization with General Preferences
Fairness on Principal Stratum: A New Perspective on Counterfactual Fairness
Open Materials Generation with Stochastic Interpolants
Lookbehind-SAM: k steps back, 1 step forward
Invited talk: Drifting games, boosting and online learning