yusuke1997/dblp-id-title huggingface dataset:
An Error Analysis of Flow Matching for Deep Generative Modeling
Learning Stable Classifiers by Transferring Unstable Features
Multi-View Graph Clustering via Node-Guided Contrastive Encoding
Contradiction Retrieval via Contrastive Learning with Sparsity
Near-Optimal Algorithms for Explainable k-Medians and k-Means
Enhancing Decision-Making of Large Language Models via Actor-Critic
HarmoDT: Harmony Multi-Task Decision Transformer for Offline Reinforcement Learning
MusicFlow: Cascaded Flow Matching for Text Guided Music Generation
Correlation Clustering and Biclustering with Locally Bounded Errors
Intra-Option Learning about Temporally Abstract Actions
Modeling Latent Variable Uncertainty for Loss-based Learning
Rigid Body Flows for Sampling Molecular Crystal Structures
Circuit-Based Intrinsic Methods to Detect Overfitting
Generalization Bounds via Meta-Learned Model Representations: PAC-Bayes and Sample Compression Hypernetworks
Temporal Multiresolution Graph Neural Networks For Epidemic Prediction
On the Effect of Instance Representation on Generalization
Near-Optimal Reinforcement Learning in Polynominal Time
Data-Copying in Generative Models: A Formal Framework
Learning Configurations for Data-Driven Multi-Objective Optimization
Robust Task Representations for Offline Meta-Reinforcement Learning via Contrastive Learning
From Vision to Audio and Beyond: A Unified Model for Audio-Visual Representation and Generation
Robust Data-driven Prescriptiveness Optimization
Adversarial camera stickers: A physical camera-based attack on deep learning systems
Online Laplacian-Based Representation Learning in Reinforcement Learning
Towards Unbiased Training in Federated Open-world Semi-supervised Learning
Rethinking External Slow-Thinking: From Snowball Errors to Probability of Correct Reasoning
Understanding the Skill Gap in Recurrent Language Models: The Role of the Gather-and-Aggregate Mechanism
Estimating Generalization under Distribution Shifts via Domain-Invariant Representations
Directed Acyclic Transformer for Non-Autoregressive Machine Translation
Size-Invariant Graph Representations for Graph Classification Extrapolations
ROME is Forged in Adversity: Robust Distilled Datasets via Information Bottleneck
Learning Mixtures of Gaussian Processes through Random Projection
IL-SOAR : Imitation Learning with Soft Optimistic Actor cRitic
SKICAT: A Machine Learning System for Automated Cataloging of Large Scale Sky Surveys
CCM: Real-Time Controllable Visual Content Creation Using Text-to-Image Consistency Models
Memory Efficient Neural Processes via Constant Memory Attention Block
Exploring the Complexity of Deep Neural Networks through Functional Equivalence
Neural Feature Matching in Implicit 3D Representations
Using Abstract Models of Behaviours to Automatically Generate Reinforcement Learning Hierarchies
Sparse Bayesian nonparametric regression
Meta-SAGE: Scale Meta-Learning Scheduled Adaptation with Guided Exploration for Mitigating Scale Shift on Combinatorial Optimization
Magnitude-preserving ranking algorithms
Benefits of Early Stopping in Gradient Descent for Overparameterized Logistic Regression
Probabilistic tangent subspace: a unified view
Lookahead When It Matters: Adaptive Non-causal Transformers for Streaming Neural Transducers
A Primal-Dual Algorithm for Offline Constrained Reinforcement Learning with Linear MDPs
Matroid Semi-Bandits in Sublinear Time
Impossible Videos
Fine-grained Local Sensitivity Analysis of Standard Dot-Product Self-Attention
Adaptive Three Operator Splitting
Diffusion Source Identification on Networks with Statistical Confidence
Two-timescale Derivative Free Optimization for Performative Prediction with Markovian Data
LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations
Refinement of Approximate Reasoning-based Controllers by Reinforcement Learning
The Butterfly Effect: Neural Network Training Trajectories Are Highly Sensitive to Initial Conditions
Improved Denoising Diffusion Probabilistic Models
C-RAG: Certified Generation Risks for Retrieval-Augmented Language Models
Combining Reinforcement Learning with a Local Control Algorithm
Minibatch Gibbs Sampling on Large Graphical Models
QT-DoG: Quantization-Aware Training for Domain Generalization
Off-Policy Confidence Sequences
Comparison-Based Learning with Rank Nets
AI Control: Improving Safety Despite Intentional Subversion
Retrieval Augmented Zero-Shot Enzyme Generation for Specified Substrate
Implicit Regularization in Feedback Alignment Learning Mechanisms for Neural Networks
Adaptive Online Experimental Design for Causal Discovery
Federated Learning with Partial Model Personalization
Multi-task Representation Learning for Pure Exploration in Linear Bandits
Efficient LiDAR Reflectance Compression via Scanning Serialization
Margin-based sampling in high dimensions: When being active is less efficient than staying passive
Data Poisoning Attacks against Conformal Prediction
Learning non-redundant codebooks for classifying complex objects
Toward Compositional Generalization in Object-Oriented World Modeling
Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event
Optimizing Hyperparameters with Conformal Quantile Regression
Putnam-AXIOM: A Functional & Static Benchmark for Measuring Higher Level Mathematical Reasoning in LLMs
Reconstructing Nonlinear Dynamical Systems from Multi-Modal Time Series
Variational Inference for Gaussian Process Modulated Poisson Processes
Learning for Edge-Weighted Online Bipartite Matching with Robustness Guarantees
Which Features are Learnt by Contrastive Learning? On the Role of Simplicity Bias in Class Collapse and Feature Suppression
Fast Sampling-Based Sketches for Tensors
DNNR: Differential Nearest Neighbors Regression
FriendlyCore: Practical Differentially Private Aggregation
HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusal
Vector-Space Markov Random Fields via Exponential Families
Nearly Optimal Algorithms with Sublinear Computational Complexity for Online Kernel Regression
Single Point Transductive Prediction
Position: C∗-Algebraic Machine Learning - Moving in a New Direction
Restarted Bayesian Online Change-point Detector achieves Optimal Detection Delay
Breadth-First Exploration on Adaptive Grid for Reinforcement Learning
Generating Language Corrections for Teaching Physical Control Tasks
ContraBAR: Contrastive Bayes-Adaptive Deep RL
H-Likelihood Approach to Deep Neural Networks with Temporal-Spatial Random Effects for High-Cardinality Categorical Features
Parameter-Free Convex Learning through Coin Betting
An Iterative, Sketching-based Framework for Ridge Regression
Diffusion Models Encode the Intrinsic Dimension of Data Manifolds
Telling cause from effect in deterministic linear dynamical systems
Provably Efficient Learning of Transferable Rewards
Learning Temporally AbstractWorld Models without Online Experimentation
Preventing Model Collapse in Gaussian Process Latent Variable Models
New Bounds for Sparse Variational Gaussian Processes
Multinomial Logit Bandit with Low Switching Cost
Integration-free Kernels for Equivariant Gaussian Process Modelling
Fast and Provable Nonconvex Tensor RPCA
Perceptually Constrained Precipitation Nowcasting Model
Improved Variational Autoencoders for Text Modeling using Dilated Convolutions
Solving Satisfiability Modulo Counting Exactly with Probabilistic Circuits
Optimal Non-parametric Learning in Repeated Contextual Auctions with Strategic Buyer
Neural-Pull: Learning Signed Distance Function from Point clouds by Learning to Pull Space onto Surface
Approximate Value Trees in Structured Dynamic Programming
Decision-aware Training of Spatiotemporal Forecasting Models to Select a Top-K Subset of Sites for Intervention
On the Complexity of Approximating Wasserstein Barycenters
Dynamic Learning with Frequent New Product Launches: A Sequential Multinomial Logit Bandit Problem
TIPS: Topologically Important Path Sampling for Anytime Neural Networks
DNCs Require More Planning Steps
DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Training
Learning Heteroscedastic Models by Convex Programming under Group Sparsity
Explaining the Predictions of Unsupervised Learning Models
Homomorphism AutoEncoder - Learning Group Structured Representations from Observed Transitions
Contrastive Learning with Simplicial Convolutional Networks for Short-Text Classification
Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks
Dueling Bandits with Weak Regret
Metadata Conditioning Accelerates Language Model Pre-training
From Geometry to Causality- Ricci Curvature and the Reliability of Causal Inference on Networks
Ensemble selection from libraries of models
Racing Thompson: an Efficient Algorithm for Thompson Sampling with Non-conjugate Priors
EEG-Language Pretraining for Highly Label-Efficient Clinical Phenotyping
Extrapolation for Large-batch Training in Deep Learning
Generative Adversarial User Model for Reinforcement Learning Based Recommendation System
Label-Only Membership Inference Attacks
Unlocking the Power of Rehearsal in Continual Learning: A Theoretical Perspective
Time-Regularized Interrupting Options (TRIO)
Watermarks in the Sand: Impossibility of Strong Watermarking for Language Models
Convolutional and Residual Networks Provably Contain Lottery Tickets
On Calibration of Modern Neural Networks
Structure preserving embedding
Box Facets and Cut Facets of Lifted Multicut Polytopes
Sequential Kernel Goodness-of-fit Testing
Emergent Equivariance in Deep Ensembles
Passive Distance Learning for Robot Navigation
Least Squares Revisited: Scalable Approaches for Multi-class Prediction
The Ideal Continual Learner: An Agent That Never Forgets
mPLUG-2: A Modularized Multi-modal Foundation Model Across Text, Image and Video
Recurrent Distance Filtering for Graph Representation Learning
Ranked from Within: Ranking Large Multimodal Models Without Labels
Reinforcement Learning for Cost-Aware Markov Decision Processes
Random Matrix Theory Proves that Deep Learning Representations of GAN-data Behave as Gaussian Mixtures
Speeding up k-means by approximating Euclidean distances via block vectors
PEARL: Zero-shot Cross-task Preference Alignment and Robust Reward Learning for Robotic Manipulation
Linear-Time Gromov Wasserstein Distances using Low Rank Couplings and Costs
Dialog Inpainting: Turning Documents into Dialogs
Cross-domain Imitation from Observations
Average Case Analysis of Conjunctive Learning Algorithms
Sampling-based Nyström Approximation and Kernel Quadrature
COKE: Core Kernel for More Efficient Approximation of Kernel Weights in Multiple Kernel Clustering
LangTime: A Language-Guided Unified Model for Time Series Forecasting with Proximal Policy Optimization
Deep Reasoning Networks for Unsupervised Pattern De-mixing with Constraint Reasoning
Detecting Concept Drift with Support Vector Machines
A Hierarchical Adaptive Multi-Task Reinforcement Learning Framework for Multiplier Circuit Design
COAT: Measuring Object Compositionality in Emergent Representations
UneVEn: Universal Value Exploration for Multi-Agent Reinforcement Learning
Using Unlabelled Data for Text Classification through Addition of Cluster Parameters
Mind the Gap: A Practical Attack on GGUF Quantization
No One Idles: Efficient Heterogeneous Federated Learning with Parallel Edge and Server Computation
On Learning Sparsely Used Dictionaries from Incomplete Samples
Toward Large Kernel Models
Quantifying Memory Utilization with Effective State-Size
Context-Aware Bayesian Network Actor-Critic Methods for Cooperative Multi-Agent Reinforcement Learning
Fast Bounded Online Gradient Descent Algorithms for Scalable Kernel-Based Online Learning
LapSum - One Method to Differentiate Them All: Ranking, Sorting and Top-k Selection
Retro*: Learning Retrosynthetic Planning with Neural Guided A* Search
Few-shot Domain Adaptation by Causal Mechanism Transfer
Adapting k-means Algorithms for Outliers
A Box-Constrained Approach for Hard Permutation Problems
The Role of Machine Learning in Business Optimization
Revealing Common Statistical Behaviors in Heterogeneous Populations
Annealed Flow Transport Monte Carlo
Perturbation Analysis of Neural Collapse
Learning Proof Heuristics by Adaptive Parameters
RE-Bench: Evaluating Frontier AI R&D Capabilities of Language Model Agents against Human Experts
Data Structures for Density Estimation
Analytical Construction on Geometric Architectures: Transitioning from Static to Temporal Link Prediction
Kernel Logistic Regression Approximation of an Understandable ReLU Neural Network
Reward Translation via Reward Machine in Semi-Alignable MDPs
A General Graph Spectral Wavelet Convolution via Chebyshev Order Decomposition
Coder Reviewer Reranking for Code Generation
Scaling Inference-Efficient Language Models
Self-Concordant Analysis of Frank-Wolfe Algorithms
Deterministic Policy Gradient Algorithms
Using AI Uncertainty Quantification to Improve Human Decision-Making
Deep Neural Cellular Potts Models
A3S: A General Active Clustering Method with Pairwise Constraints
Bayesian Watermark Attacks
Globally Optimal Gradient Descent for a ConvNet with Gaussian Inputs
Premier-TACO is a Few-Shot Policy Learner: Pretraining Multitask Representation via Temporal Action-Driven Contrastive Loss
Using Natural Language Processing and discourse Features to Identify Understanding Errors
HEMET: A Homomorphic-Encryption-Friendly Privacy-Preserving Mobile Neural Network Architecture
Low-Variance Gradient Estimation in Unrolled Computation Graphs with ES-Single
Controllable Neural Symbolic Regression
Individual Contributions as Intrinsic Exploration Scaffolds for Multi-agent Reinforcement Learning
Revealing Weaknesses in Text Watermarking Through Self-Information Rewrite Attacks
High-Order Contrastive Learning with Fine-grained Comparative Levels for Sparse Ordinal Tensor Completion
Connecting Optimal Ex-Ante Collusion in Teams to Extensive-Form Correlation: Faster Algorithms and Positive Complexity Results
Hierarchical Policy Gradient Algorithms
Optimal Algorithms for Lipschitz Bandits with Heavy-tailed Rewards
Isolated Causal Effects of Natural Language
Faster Algorithms for Learning Convex Functions
Planning, Fast and Slow: Online Reinforcement Learning with Action-Free Offline Data via Multiscale Planners
Proceedings of the 33nd International Conference on Machine Learning, ICML 2016, New York City, NY, USA, June 19-24, 2016
Variational Inference for Stick-Breaking Beta Process Priors
From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification
How Do Images Align and Complement LiDAR? Towards a Harmonized Multi-modal 3D Panoptic Segmentation
Recovering Labels from Local Updates in Federated Learning
Fast Stochastic Bregman Gradient Methods: Sharp Analysis and Variance Reduction
A note on mixtures of experts for multiclass responses: approximation rate and Consistent Bayesian Inference
Speculate, then Collaborate: Fusing Knowledge of Language Models during Decoding
CTBench: A Library and Benchmark for Certified Training
Testing Sparsity over Known and Unknown Bases
Online feature selection for pixel classification
Efficient Decomposed Learning for Structured Prediction
Eligibility Traces for Off-Policy Policy Evaluation
Compressed Image Generation with Denoising Diffusion Codebook Models
QuIP#: Even Better LLM Quantization with Hadamard Incoherence and Lattice Codebooks
Learning Collaborative Information Filters
Contrastive Multi-View Representation Learning on Graphs
Recycling Randomness with Structure for Sublinear time Kernel Expansions
Robust Counterfactual Explanations for Tree-Based Ensembles
KGMark: A Diffusion Watermark for Knowledge Graphs
GroupCover: A Secure, Efficient and Scalable Inference Framework for On-device Model Protection based on TEEs
Functional Gradient Boosting based on Residual Network Perception
GeoMFormer: A General Architecture for Geometric Molecular Representation Learning
Differentially Private Chi-Squared Hypothesis Testing: Goodness of Fit and Independence Testing
Lookahead-based algorithms for anytime induction of decision trees
Advancing Constrained Monotonic Neural Networks: Achieving Universal Approximation Beyond Bounded Activations
Fairwashing explanations with off-manifold detergent
FARE: Provably Fair Representation Learning with Practical Certificates
WorkArena: How Capable are Web Agents at Solving Common Knowledge Work Tasks?
Debiased Sinkhorn barycenters
Measuring the Effect of Training Data on Deep Learning Predictions via Randomized Experiments
Quantum 3D Graph Learning with Applications to Molecule Embedding
When Samples Are Strategically Selected
Deep Speech 2 : End-to-End Speech Recognition in English and Mandarin
Safe Exploration for Efficient Policy Evaluation and Comparison
Liger: Linearizing Large Language Models to Gated Recurrent Structures
No-Regret Learning in Time-Varying Zero-Sum Games
Supervised Hierarchical Clustering with Exponential Linkage
On Predicting Traveling Times in Scheduled Transportation (Extended Abstract)
The Cross-Entropy Method Optimizes for Quantiles
End-to-End Multi-Object Detection with a Regularized Mixture Model
Data Determines Distributional Robustness in Contrastive Language Image Pre-training (CLIP)
Convex Calibrated Surrogates for Hierarchical Classification
Monotone Individual Fairness
Transferring Knowledge From Large Foundation Models to Small Downstream Models
Bayesian Knowledge Distillation: A Bayesian Perspective of Distillation with Uncertainty Quantification
Collaborative Topic Regression with Social Matrix Factorization for Recommendation Systems
One Policy to Control Them All: Shared Modular Policies for Agent-Agnostic Control
Being Robust (in High Dimensions) Can Be Practical
Linear convergence of Sinkhorn's algorithm for generalized static Schrödinger bridge
Noisy Dual Principal Component Pursuit
AMPA: Adaptive Mixed Precision Allocation for Low-Bit Integer Training
Non-Negative Bregman Divergence Minimization for Deep Direct Density Ratio Estimation
Brauer's Group Equivariant Neural Networks
C2IQL: Constraint-Conditioned Implicit Q-learning for Safe Offline Reinforcement Learning
Constrained Decision Transformer for Offline Safe Reinforcement Learning
Good learners for evil teachers
Differentially Private Matrix Completion Revisited
Robust Multiple Manifold Structure Learning
Cross-regularization: Adaptive Model Complexity through Validation Gradients
How Good is the Bayes Posterior in Deep Neural Networks Really?
Exponentiated gradient algorithms for log-linear structured prediction
Faster Graph Embeddings via Coarsening
FactTest: Factuality Testing in Large Language Models with Finite-Sample and Distribution-Free Guarantees
Transformation Autoregressive Networks
Variance-reduced Zeroth-Order Methods for Fine-Tuning Language Models
Contextual Combinatorial Bandits with Probabilistically Triggered Arms
Fast Lossless Neural Compression with Integer-Only Discrete Flows
Volume-Aware Distance for Robust Similarity Learning
What Limits Bidirectional Model's Generative Capabilities? A Uni-Bi-Directional Mixture-of-Expert Method For Bidirectional Fine-tuning
Attentional Meta-learners for Few-shot Polythetic Classification
Reward-Guided Prompt Evolving in Reinforcement Learning for LLMs
Larger or Smaller Reward Margins to Select Preferences for LLM Alignment?
Provable Lipschitz Certification for Generative Models
Robust bounds for classification via selective sampling
Single-Model Attribution of Generative Models Through Final-Layer Inversion
What makes an Ensemble (Un) Interpretable?
Reinforcement Learning with Deep Energy-Based Policies
Incremental, Instance-Based Learning of Independent and Graded Concept Descriptions
Bounded Rationality for LLMs: Satisficing Alignment at Inference-Time
Optimal kernel selection in Kernel Fisher discriminant analysis
Transferable Facial Privacy Protection against Blind Face Restoration via Domain-Consistent Adversarial Obfuscation
Fast and Three-rious: Speeding Up Weak Supervision with Triplet Methods
Byzantine Resilient and Fast Federated Few-Shot Learning
Latent Space Factorisation and Manipulation via Matrix Subspace Projection
The Generate, Test, and Explain Discovery System Architecture
SafeArena: Evaluating the Safety of Autonomous Web Agents
Reflection-Window Decoding: Text Generation with Selective Refinement
LazyGNN: Large-Scale Graph Neural Networks via Lazy Propagation
Retrieval-Augmented Reinforcement Learning
Exphormer: Sparse Transformers for Graphs
Attribute Dependencies, Understandability and Split Selection in Tree Based Models
Fundamental limits of learning in sequence multi-index models and deep attention networks: high-dimensional asymptotics and sharp thresholds
Test-time Adapted Reinforcement Learning with Action Entropy Regularization
A Regret Minimization Approach to Multi-Agent Control
Generative Particle Variational Inference via Estimation of Functional Gradients
Massively Parallel Algorithms and Hardness for Single-Linkage Clustering under 𝓁 p Distances
Importance Corrected Neural JKO Sampling
TabLog: Test-Time Adaptation for Tabular Data Using Logic Rules
Fast Neighborhood Subgraph Pairwise Distance Kernel
Optimizing Dynamic Structures with Bayesian Generative Search
The Statistical Recurrent Unit
PAC-Bayesian Offline Contextual Bandits With Guarantees
Beyond Adaptive Submodularity: Approximation Guarantees of Greedy Policy with Adaptive Submodularity Ratio
Efficient Bound of Lipschitz Constant for Convolutional Layers by Gram Iteration
Lossless or Quantized Boosting with Integer Arithmetic
On the Consistency of Feature Selection With Lasso for Non-linear Targets
Reducing Variance of Stochastic Optimization for Approximating Nash Equilibria in Normal-Form Games
A Combinatorial Algebraic Approach for the Identifiability of Low-Rank Matrix Completion
Parameter-free, Dynamic, and Strongly-Adaptive Online Learning
Weakly-Supervised Temporal Localization via Occurrence Count Learning
Stream-level Flow Matching with Gaussian Processes
Bayesian Posterior Sampling via Stochastic Gradient Fisher Scoring
Estimating and computing density based distance metrics
Multi-Agent Reinforcement Learning with Hierarchical Coordination for Emergency Responder Stationing
Regret Bounds for Stochastic Shortest Path Problems with Linear Function Approximation
Approximate Newton Methods and Their Local Convergence
Distributed Learning over Unreliable Networks
Learning to Drive a Bicycle Using Reinforcement Learning and Shaping
Spread Divergence
Scalable Discrete Sampling as a Multi-Armed Bandit Problem
Expert Race: A Flexible Routing Strategy for Scaling Diffusion Transformer with Mixture of Experts
Topology-Aware Network Pruning using Multi-stage Graph Embedding and Reinforcement Learning
An Accelerated Chow and Liu Algorithm: Fitting Tree Distributions to High-Dimensional Sparse Data
RLEG: Vision-Language Representation Learning with Diffusion-based Embedding Generation
On Collective Robustness of Bagging Against Data Poisoning
Linking losses for density ratio and class-probability estimation
Ditto: Fair and Robust Federated Learning Through Personalization
SageAttention2: Efficient Attention with Thorough Outlier Smoothing and Per-thread INT4 Quantization
Position: A Roadmap to Pluralistic Alignment
UNIREX: A Unified Learning Framework for Language Model Rationale Extraction
Simultaneous identification of models and parameters of scientific simulators
Biological Sequence Design with GFlowNets
Categorical Distributional Reinforcement Learning with Kullback-Leibler Divergence: Convergence and Asymptotics
Neural Tangent Kernels for Axis-Aligned Tree Ensembles
Reprogramming Pretrained Language Models for Antibody Sequence Infilling
LDA Topic Model with Soft Assignment of Descriptors to Words
Learning from Streaming Data when Users Choose
CMoS: Rethinking Time Series Prediction Through the Lens of Chunk-wise Spatial Correlations
Prioritized Level Replay
A Statistical Convergence Perspective of Algorithms for Rank Aggregation from Pairwise Data
Robust Classification with Adiabatic Quantum Optimization
prDeep: Robust Phase Retrieval with a Flexible Deep Network
Algorithms and Hardness for Active Learning on Graphs
Stochastic Video Generation with a Learned Prior