yusuke1997/dblp-id-title huggingface dataset:
A Context-Integrated Transformer-Based Neural Network for Auction Design
APS: Active Pretraining with Successor Features
Robust Sparsification via Sensitivity
Constant-Time Predictive Distributions for Gaussian Processes
An Improved Finite-time Analysis of Temporal Difference Learning with Deep Neural Networks
The Wasserstein Transform
Breaking the Barrier: Enhanced Utility and Robustness in Smoothed DRL Agents
Neural Optimizer Search with Reinforcement Learning
Streaming Algorithm for Monotone k-Submodular Maximization with Cardinality Constraints
Infinite Mixture Prototypes for Few-shot Learning
FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models
Sample Average Approximation for Conditional Stochastic Optimization with Dependent Data
Restricted Boltzmann Machines are Hard to Approximately Evaluate or Simulate
Efficient Distributionally Robust Bayesian Optimization with Worst-case Sensitivity
Stabilizing Gradients for Deep Neural Networks via Efficient SVD Parameterization
Layer-wise Alignment: Examining Safety Alignment Across Image Encoder Layers in Vision Language Models
Fast Inference with Kronecker-Sparse Matrices
Learning Subjective Functions with Large Margins
Improving Generalization with Flat Hilbert Bayesian Inference
Open-Det: An Efficient Learning Framework for Open-Ended Detection
One-Shot Federated Conformal Prediction
Using Decision Trees to Improve Case-Based Learning
Deep One-Class Classification
Axiomatic Attribution for Deep Networks
Neural Architecture Search without Training
FlexTok: Resampling Images into 1D Token Sequences of Flexible Length
Model-based Reinforcement Learning for Confounded POMDPs
Nonlinear Distributional Gradient Temporal-Difference Learning
Learning Multiple Behaviors from Unlabeled Demonstrations in a Latent Controller Space
RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer
Machine Vision Therapy: Multimodal Large Language Models Can Enhance Visual Robustness via Denoising In-Context Learning
Model Tailor: Mitigating Catastrophic Forgetting in Multi-modal Large Language Models
On the Convergence of Inexact Predictor-Corrector Methods for Linear Programming
Fully distributed EM for very large datasets
Universal Approximation Theorem of Deep Q-Networks
Minimum reference set based feature selection for small sample classifications
Federated Learning with Only Positive Labels
Towards Understanding the Dynamics of the First-Order Adversaries
Learning Graphs with a Few Hubs
Convex Relaxations for Learning Bounded-Treewidth Decomposable Graphs
GOAT: A Global Transformer on Large-scale Graphs
Learning the Structure of Generative Models without Labeled Data
Latent Collaborative Retrieval
Correcting discount-factor mismatch in on-policy policy gradient methods
MA-LoT: Model-Collaboration Lean-based Long Chain-of-Thought Reasoning enhances Formal Theorem Proving
Estimating Latent-Variable Graphical Models using Moments and Likelihoods
Token-Specific Watermarking with Enhanced Detectability and Semantic Coherence for Large Language Models
Feature hashing for large scale multitask learning
Harmonic Self-Conditioned Flow Matching for joint Multi-Ligand Docking and Binding Site Design
Soft-DTW: a Differentiable Loss Function for Time-Series
Adversarial Robustness for Code
Reaction Graph: Towards Reaction-Level Modeling for Chemical Reactions with 3D Structures
Machine Learning, Proceedings of the Twelfth International Conference on Machine Learning, Tahoe City, California, USA, July 9-12, 1995
Using Multiple Levels of Learning and Diverse Evidence to Uncover Coordinately Controlled Genes
Revisiting Online Submodular Minimization: Gap-Dependent Regret Bounds, Best of Both Worlds and Adversarial Robustness
Memory-Based Meta-Learning on Non-Stationary Distributions
EpiCoder: Encompassing Diversity and Complexity in Code Generation
Attributing Image Generative Models using Latent Fingerprints
Fixed-Point Model For Structured Labeling
Budgeted Online Influence Maximization
Utility Theory for Sequential Decision Making
Discriminatively Activated Sparselets
Average-Reward Reinforcement Learning for Variance Penalized Markov Decision Problems
Evolving Subnetwork Training for Large Language Models
Active Reward Modeling: Adaptive Preference Labeling for Large Language Model Alignment
SDNA: Stochastic Dual Newton Ascent for Empirical Risk Minimization
OmniBal: Towards Fast Instruction-Tuning for Vision-Language Models via Omniverse Computation Balance
Approximate Steepest Coordinate Descent
UniRank: Unimodal Bandit Algorithms for Online Ranking
Knowledge Base Question Answering by Case-based Reasoning over Subgraphs
Continuously Updating Digital Twins using Large Language Models
FormulaZero: Distributionally Robust Online Adaptation via Offline Population Synthesis
Towards Theoretical Understanding of Sequential Decision Making with Preference Feedback
Hierarchical Tensor Decomposition of Latent Tree Graphical Models
Integrated Learning with Incorrect and Incomplete Theories
Think Before You Act: Decision Transformers with Working Memory
A Certified Unlearning Approach without Access to Source Data
Markov Modulated Gaussian Cox Processes for Semi-Stationary Intensity Modeling of Events Data
Understanding The Robustness in Vision Transformers
DL2: Training and Querying Neural Networks with Logic
Quantile Bandits for Best Arms Identification
The Earth Mover's Pinball Loss: Quantiles for Histogram-Valued Regression
Accurate and Efficient World Modeling with Masked Latent Transformers
Overcoming Non-monotonicity in Transducer-based Streaming Generation
Toward Optimal Stratification for Stratified Monte-Carlo Integration
Robust PCA in High-dimension: A Deterministic Approach
Do Embodied Agents Dream of Pixelated Sheep: Embodied Decision Making using Language Guided World Modelling
Active Learning for Decision-Making from Imbalanced Observational Data
Composable Sketches for Functions of Frequencies: Beyond the Worst Case
Online Local Differential Private Quantile Inference via Self-normalization
UCB Momentum Q-learning: Correcting the bias without forgetting
On the Role of Edge Dependency in Graph Generative Models
Parallel and Streaming Algorithms for K-Core Decomposition
Revisiting End-to-End Speech-to-Text Translation From Scratch
Duality in RKHSs with Infinite Dimensional Outputs: Application to Robust Losses
Adaptive and Safe Bayesian Optimization in High Dimensions via One-Dimensional Subspaces
Prometheus: Taming Sample and Communication Complexities in Constrained Decentralized Stochastic Bilevel Learning
The Hessian perspective into the Nature of Convolutional Neural Networks
Fair and Optimal Classification via Post-Processing
STAIR: Improving Safety Alignment with Introspective Reasoning
Double-Win Quant: Aggressively Winning Robustness of Quantized Deep Neural Networks via Random Precision Training and Inference
Towards Binary-Valued Gates for Robust LSTM Training
Implicit Graph Neural Networks: A Monotone Operator Viewpoint
Nonconvex Optimization for Regression with Fairness Constraints
Flexible Tails for Normalizing Flows
Diffusion Approximation for Bayesian Markov Chains
Reinforcement Learning Can Be More Efficient with Multiple Rewards
Boosting Virtual Agent Learning and Reasoning: A Step-Wise, Multi-Dimensional, and Generalist Reward Model with Benchmark
BASGD: Buffered Asynchronous SGD for Byzantine Learning
Efficient Contrastive Learning for Fast and Accurate Inference on Graphs
Representation Shattering in Transformers: A Synthetic Study with Knowledge Editing
On the Power and Limits of Distance-Based Learning
Enabling Optimal Decisions in Rehearsal Learning under CARE Condition
Understanding Generalization and Optimization Performance of Deep CNNs
Recommendations with Sparse Comparison Data: Provably Fast Convergence for Nonconvex Matrix Factorization
Policy Gradient Method For Robust Reinforcement Learning
Batch Active Learning via Coordinated Matching
Robust and private stochastic linear bandits
Deep Lambertian Networks
Nested Bandits
Sequential Strategic Screening
Learning Scoring Functions with Order-Preserving Losses and Standardized Supervision
DYNAMIC: A New Role for Training Problems in EBL
Discovering Interpretable Representations for Both Deep Generative and Discriminative Models
Double-Weighting for Covariate Shift Adaptation
GiLOT: Interpreting Generative Language Models via Optimal Transport
Some Chunks Are Expensive
On the Robustness of Text Vectorizers
LTF: A Label Transformation Framework for Correcting Label Shift
Fitting a graph to vector data
Learning Latent Structures in Network Games via Data-Dependent Gated-Prior Graph Variational Autoencoders
The Sample-Complexity of General Reinforcement Learning
End-to-End Full-Atom Antibody Design
Projection Penalties: Dimension Reduction without Loss
Sharp Variance-Dependent Bounds in Reinforcement Learning: Best of Both Worlds in Stochastic and Deterministic Environments
Rethinking Explaining Graph Neural Networks via Non-parametric Subgraph Matching
Katyusha X: Practical Momentum Method for Stochastic Sum-of-Nonconvex Optimization
The Shattered Gradients Problem: If resnets are the answer, then what is the question?
EVOLvE: Evaluating and Optimizing LLMs For In-Context Exploration
Fast k-Nearest Neighbour Search via Prioritized DCI
Combinatorial Optimization in Inductive Concept Learning
Combining Model-Based and Model-Free Updates for Trajectory-Centric Reinforcement Learning
The Price of Freedom: Exploring Expressivity and Runtime Tradeoffs in Equivariant Tensor Products
Streaming k-Submodular Maximization under Noise subject to Size Constraint
Proceedings of the 27th International Conference on Machine Learning (ICML-10), June 21-24, 2010, Haifa, Israel
Iterative Amortized Inference
Striving for Simplicity and Performance in Off-Policy DRL: Output Normalization and Non-Uniform Sampling
Fake News Mitigation via Point Process Based Intervention
Self-Organizing Visual Prototypes for Non-Parametric Representation Learning
On Computing Optimal Tree Ensembles
Automatically Extracting Features for Concept Learning from the Web
Subequivariant Reinforcement Learning in 3D Multi-Entity Physical Environments
Dimensionality Reduction by Local Discriminative Gaussians
Off-Policy Deep Reinforcement Learning without Exploration
Error Estimation for Randomized Least-Squares Algorithms via the Bootstrap
Fast Tensor Completion via Approximate Richardson Iteration
Permutation Equivariant Neural Networks for Symmetric Tensors
Inverse Problem Sampling in Latent Space Using Sequential Monte Carlo
Boosting Graph Structure Learning with Dummy Nodes
Semi-Dual Unbalanced Quadratic Optimal Transport: fast statistical rates and convergent algorithm
Stability of transductive regression algorithms
Rate-Optimal Policy Optimization for Linear Markov Decision Processes
Learning Word Representations with Hierarchical Sparse Coding
DeBayes: a Bayesian Method for Debiasing Network Embeddings
Deep Stochastic Mechanics
Split-Ensemble: Efficient OOD-aware Ensemble via Task and Model Splitting
Pre-computed memory or on-the-fly encoding? A hybrid approach to retrieval augmentation makes the most of your compute
A Model of Place Field Reorganization During Reward Maximization
The Dormant Neuron Phenomenon in Deep Reinforcement Learning
DiffAug: Enhance Unsupervised Contrastive Learning with Domain-Knowledge-Free Diffusion-based Data Augmentation
Towards Accurate Post-training Network Quantization via Bit-Split and Stitching
Learning String Edit Distance
Modeling Multi-Task Model Merging as Adaptive Projective Gradient Descent
MF-LAL: Drug Compound Generation Using Multi-Fidelity Latent Space Active Learning
An Online Learning Algorithm for Bilinear Models
InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective
Provable In-Context Vector Arithmetic via Retrieving Task Concepts
Enhancing Parallelism in Decentralized Stochastic Convex Optimization
Improving Your Model Ranking on Chatbot Arena by Vote Rigging
Joint Transfer and Batch-mode Active Learning
Learning 1-Bit Tiny Object Detector with Discriminative Feature Refinement
TrueLabel + Confusions: A Spectrum of Probabilistic Models in Analyzing Multiple Ratings
An intrinsic reward mechanism for efficient exploration
Spectral Frank-Wolfe Algorithm: Strict Complementarity and Linear Convergence
Horizonal Generalization
Reward Identification in Inverse Reinforcement Learning
Unifying Image Processing as Visual Prompting Question Answering
Regularization and feature selection in least-squares temporal difference learning
ClimaX: A foundation model for weather and climate
Revisiting Convergence: Shuffling Complexity Beyond Lipschitz Smoothness
Learning Label Shift Correction for Test-Agnostic Long-Tailed Recognition
Self-Discriminative Modeling for Anomalous Graph Detection
A Discriminative Technique for Multiple-Source Adaptation
Deep Molecular Programming: A Natural Implementation of Binary-Weight ReLU Neural Networks
Using a Generalization Hierarchy to Learn from Examples
Compressed Maximum Likelihood
CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models
Online Linear Quadratic Control
k-DPPs: Fixed-Size Determinantal Point Processes
Rethinking Chain-of-Thought from the Perspective of Self-Training
Solving Noisy Linear Operator Equations by Gaussian Processes: Application to Ordinary and Partial Differential Equations
Optimal No-Regret Learning for One-Sided Lipschitz Functions
Improving Adversarial Robustness Through the Contrastive-Guided Diffusion Process
Infinite SVM: a Dirichlet Process Mixture of Large-margin Kernel Machines
Risk-Sensitive Reward-Free Reinforcement Learning with CVaR
Stabilizing Equilibrium Models by Jacobian Regularization
The Role of Experimentation in Scientific Theory Revision
ATA: Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning
Transfer-Based Semantic Anomaly Detection
Learning the Target Network in Function Space
Ensemble Learned Bloom Filters: Two Oracles are Better than One
Structure Learning of Partitioned Markov Networks
Boosting Frank-Wolfe by Chasing Gradients
Online conformal prediction with decaying step sizes
Improved Stability and Generalization Guarantees of the Decentralized SGD Algorithm
Extracting and composing robust features with denoising autoencoders
Bounds on the Approximation Power of Feedforward Neural Networks
Subproblem-Tree Calibration: A Unified Approach to Max-Product Message Passing
EMC2: Efficient MCMC Negative Sampling for Contrastive Learning with Global Convergence
Epsilon-VAE: Denoising as Visual Decoding
A New Statistical Approach to Personal Name Extraction
Rotation and Translation Invariant Representation Learning with Implicit Neural Representations
Characterizing Truthfulness in Large Language Model Generations with Local Intrinsic Dimension
Evaluating Self-Supervised Learning via Risk Decomposition
Zero-Shot ECG Classification with Multimodal Learning and Test-time Clinical Knowledge Enhancement
Stronger Generalization Bounds for Deep Nets via a Compression Approach
Hierarchical Planning for Complex Tasks with Knowledge Graph-RAG and Symbolic Verification
Automated Search for Conjectures on Mathematical Constants using Analysis of Integer Sequences
TFix: Learning to Fix Coding Errors with a Text-to-Text Transformer
Gibbs Max-Margin Topic Models with Fast Sampling Algorithms
Certified Adversarial Robustness Under the Bounded Support Set
Fast Adaptation to New Environments via Policy-Dynamics Value Functions
Conditionally-Conjugate Gaussian Process Factor Analysis for Spike Count Data via Data Augmentation
Optimistic initialization and greediness lead to polynomial time learning in factored MDPs
Deferred Commitment in UNIMEM: Waiting to Learn
Multi-Bias Non-linear Activation in Deep Neural Networks
Compressed Factorization: Fast and Accurate Low-Rank Factorization of Compressively-Sensed Data
Learning Deep Temporal Representations for fMRI Brain Decoding
Provable Representation Learning for Imitation Learning via Bi-level Optimization
Revisiting k-means: New Algorithms via Bayesian Nonparametrics
Model-Free Imitation Learning with Policy Optimization
DeepNose: Using artificial neural networks to represent the space of odorants
Random extrapolation for primal-dual coordinate descent
Combating Label Noise in Deep Learning using Abstention
Is margin preserved after random projection?
Learning an Agent's Utility Function by Observing Behavior
Catoni Contextual Bandits are Robust to Heavy-tailed Rewards
Improving fine-grained understanding in image-text pre-training
Unifying 2D and 3D Vision-Language Understanding
Interplay of ROC and Precision-Recall AUCs: Theoretical Limits and Practical Implications in Binary Classification
How Framelets Enhance Graph Neural Networks
Revisiting Sampling for Combinatorial Optimization
Gravity-Bench-v1: A Benchmark on Gravitational Physics Discovery for Agents
Efficient Test-Time Model Adaptation without Forgetting
Empirical Privacy Variance
Feature Selection with Selective Sampling
Guiding Example Acquisition by Generating Scenarios
Implicit surface modelling as an eigenvalue problem
Proximal and Federated Random Reshuffling
Lightweight Rule Induction
Towards a Deep and Unified Understanding of Deep Neural Models in NLP
Affinity Weighted Embedding
Stay on Topic with Classifier-Free Guidance
Learning a Set of Primitive Actions with an Uninterpreted Sensorimotor Apparatus
Scaling Reinforcement Learning Techniques via Modularity
Stochastic Modified Equations and Adaptive Stochastic Gradient Algorithms
Sparse Spectral Training and Inference on Euclidean and Hyperbolic Neural Networks
One Example Shown, Many Concepts Known! Counterexample-Driven Conceptual Reasoning in Mathematical LLMs
Improved Large-Scale Graph Learning through Ridge Spectral Sparsification
Knowledge Swapping via Learning and Unlearning
Continual Learning via Sequential Function-Space Variational Inference
The Blessing of Heterogeneity in Federated Q-Learning: Linear Speedup and Beyond
Representation Surgery: Theory and Practice of Affine Steering
Improving Minority Class Prediction Using Case-Specific Feature Weights
A Formal Framework for Learning in Embedded Systems
Sparse cooperative Q-learning
Trading Off Simplicity and Coverage in Incremental concept Learning
Branches: Efficiently Seeking Optimal Sparse Decision Trees via AO
Split-and-Denoise: Protect large language model inference with local differential privacy
Navigation Turing Test (NTT): Learning to Evaluate Human-Like Navigation
Discovering Latent Network Structure in Point Process Data
Zonotope Hit-and-run for Efficient Sampling from Projection DPPs
Active Learning for Networked Data
Scaling up Hybrid Probabilistic Inference with Logical and Arithmetic Constraints via Message Passing
A Simple Algorithm for Nuclear Norm Regularized Problems
Slot Abstractors: Toward Scalable Abstract Visual Reasoning
Global Convergence of Block Coordinate Descent in Deep Learning
CITRIS: Causal Identifiability from Temporal Intervened Sequences
PWSHAP: A Path-Wise Explanation Model for Targeted Variables
Hyperbolic Diffusion Embedding and Distance for Hierarchical Representation Learning
Understanding the Origins of Bias in Word Embeddings
Learning for Automotive Collision Avoidance and Autonomous Control
Active World Model Learning with Progress Curiosity
Exploiting the Cost (In)sensitivity of Decision Tree Splitting Criteria
Discovering Physics Laws of Dynamical Systems via Invariant Function Learning
Diffusive Gibbs Sampling
Composable Core-sets for Determinant Maximization: A Simple Near-Optimal Algorithm
Temporal Phenotyping using Deep Predictive Clustering of Disease Progression
Lower Complexity Bounds for Finite-Sum Convex-Concave Minimax Optimization Problems
Inertial Block Proximal Methods for Non-Convex Non-Smooth Optimization
Let LLM Tell What to Prune and How Much to Prune
Exploring the LLM Journey from Cognition to Expression with Linear Representations
Analysis of Stochastic Processes through Replay Buffers
Staged Training for Transformer Language Models
Model-Based Active Exploration
Score as Action: Fine Tuning Diffusion Generative Models by Continuous-time Reinforcement Learning
AGENT: A Benchmark for Core Psychological Reasoning
Implicit-PDF: Non-Parametric Representation of Probability Distributions on the Rotation Manifold
Model Comparison for Semantic Grouping
Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack
Selective Reformulation of Examples in Concept Learning
RAUCA: A Novel Physical Adversarial Attack on Vehicle Detectors via Robust and Accurate Camouflage Generation
Understanding and Controlling Memory in Recurrent Neural Networks
Adaptive cognitive orthotics: combining reinforcement learning and constraint-based temporal reasoning
Efficiently Vectorized MCMC on Modern Accelerators
Polynomial Networks and Factorization Machines: New Insights and Efficient Training Algorithms
Approximation Algorithms for Fair Range Clustering
Augment with Care: Contrastive Learning for Combinatorial Problems
Generalizing to New Physical Systems via Context-Informed Dynamics Model
On Leveraging Pretrained GANs for Generation with Limited Data
Domain-Adapted Diffusion Model for PROTAC Linker Design Through the Lens of Density Ratio in Chemical Space
CUPS: Improving Human Pose-Shape Estimators with Conformalized Deep Uncertainty
Learning Quadratic Games on Networks
Positive-unlabeled AUC Maximization under Covariate Shift
All-Purpose Mean Estimation over R: Optimal Sub-Gaussianity with Outlier Robustness and Low Moments Performance
Learning Ordered Representations with Nested Dropout
Justification-based Multiagent Learning
Matrix Norms in Data Streams: Faster, Multi-Pass and Row-Order
Experimental Results on Q-Learning for General-Sum Stochastic Games
Neural Contextual Bandits with UCB-based Exploration
Geometric Autoencoders - What You See is What You Decode
Learning Hierarchical Features from Deep Generative Models
BilBOWA: Fast Bilingual Distributed Representations without Word Alignments
Geometric Contact Flows: Contactomorphisms for Dynamics and Control
Scaling Laws for Generative Mixed-Modal Language Models
Newboole: A Fast GBML System
An Empirical Comparison of Genetic and Decision-Tree Classifiers
Feedback Efficient Online Fine-Tuning of Diffusion Models
Retrieval-Augmented Perception: High-resolution Image Perception Meets Visual RAG
Efficient and Scalable Density Functional Theory Hamiltonian Prediction through Adaptive Sparsity
Linear Bandit Algorithms with Sublinear Time Complexity
Knowledge-Based Equation Discovery in Engineering Domains
Adaptive Canonical Correlation Analysis Based On Matrix Manifolds
Beyond Sensor Data: Foundation Models of Behavioral Data from Wearables Improve Health Predictions
Tighter Bounds on the Expressivity of Transformer Encoders
Fast Bayesian Intensity Estimation for the Permanental Process
The Pairwise Piecewise-Linear Embedding for Efficient Non-Linear Classification
Accelerating Spectral Clustering under Fairness Constraints
LightGTS: A Lightweight General Time Series Forecasting Model
Lorentz Group Equivariant Neural Network for Particle Physics
Machine Learning, Proceedings of the Twenty-first International Conference (ICML 2004), Banff, Alberta, Canada, July 4-8, 2004
Testing Group Fairness via Optimal Transport Projections
Boosting Classifiers with Tightened L0-Relaxation Penalties
Benefits and Pitfalls of the Exponential Mechanism with Applications to Hilbert Spaces and Functional PCA
Using Concept Hierarchies to Organize Plan Knowledge
Approximately Equivariant Networks for Imperfectly Symmetric Dynamics
Interpreting and Improving Large Language Models in Arithmetic Calculation
Nonparametric Extensions of Randomized Response for Private Confidence Sets
Stochastically Transitive Models for Pairwise Comparisons: Statistical and Computational Issues
ESC: Exploration with Soft Commonsense Constraints for Zero-shot Object Navigation
An Accelerated DFO Algorithm for Finite-sum Convex Functions
Learning hierarchical task networks by observation
Layer by Layer: Uncovering Hidden Representations in Language Models
Truly No-Regret Learning in Constrained MDPs