yusuke1997/dblp-id-title huggingface dataset:
Learning to Schedule Straight-Line Code
A Surrogate Objective Framework for Prediction+Programming with Soft Constraints
Odor Processing in the Bee: A Preliminary Study of the Role of Central Input to the Antennal Lobe
Beyond accuracy: generalization properties of bio-plausible temporal credit assignment rules
Distributional regression: CRPS-error bounds for model fitting, model selection and convex aggregation
Towards Free Data Selection with General-Purpose Models
Optimistic Planning in Markov Decision Processes Using a Generative Model
An Auditory Localization and Coordinate Transform Chip
Reinforcement Learning with Simple Sequence Priors
PopArt: Efficient Sparse Regression and Experimental Design for Optimal Sparse Linear Bandits
An Empirical Bayes Approach to Optimizing Machine Learning Algorithms
Trajectory Data Suffices for Statistically Efficient Learning in Offline RL with Linear q π -Realizability and Concentrability
A mechanistic model of early sensory processing based on subtracting sparse representations
Constant Nullspace Strong Convexity and Fast Convergence of Proximal Methods under High-Dimensional Settings
SkiLD: Unsupervised Skill Discovery Guided by Factor Interactions
Bayesian Robust Optimization for Imitation Learning
Towards Consistency in Adversarial Classification
Enhancing In-Context Learning Performance with just SVD-Based Weight Pruning: A Theoretical Perspective
Theory-Inspired Path-Regularized Differential Network Architecture Search
TTT++: When Does Self-Supervised Test-Time Training Fail or Thrive?
Multi-label Co-regularization for Semi-supervised Facial Action Unit Recognition
Debiased Visual Question Answering from Feature and Sample Perspectives
Embracing Consistency: A One-Stage Approach for Spatio-Temporal Video Grounding
On the Complexity of Learning Sparse Functions with Statistical and Gradient Queries
Contact-aware Human Motion Forecasting
Measuring Mutual Policy Divergence for Multi-Agent Sequential Exploration
On Robustness of Kernel Clustering
Quantifying and Improving Transferability in Domain Generalization
Robust Learning of Chaotic Attractors
Improving Textual Network Learning with Variational Homophilic Embeddings
TAB-VCR: Tags and Attributes based VCR Baselines
Meta-learning to Improve Pre-training
Robust Binary Models by Pruning Randomly-initialized Networks
Representer Point Selection for Explaining Deep Neural Networks
Understanding Gradient Clipping in Private SGD: A Geometric Perspective
Symmetries in Overparametrized Neural Networks: A Mean Field View
Towards Anytime Classification in Early-Exit Architectures by Enforcing Conditional Monotonicity
Mission Impossible: A Statistical Perspective on Jailbreaking LLMs
Amortized Proximal Optimization
Pseudo-Private Data Guided Model Inversion Attacks
Controlled Sparsity via Constrained Optimization or: How I Learned to Stop Tuning Penalties and Love Constraints
Random Normalization Aggregation for Adversarial Defense
Least Squares Regression with Markovian Data: Fundamental Limits and Algorithms
Scaling Laws in Linear Regression: Compute, Parameters, and Data
Singular Value Fine-tuning: Few-shot Segmentation requires Few-parameters Fine-tuning
A causal view of compositional zero-shot recognition
Generate What You Prefer: Reshaping Sequential Recommendation via Guided Diffusion
Fine-Grained Neural Network Explanation by Identifying Input Features with Predictive Information
Adaptive Probing Policies for Shortest Path Routing
Exact Recovery of Mangled Clusters with Same-Cluster Queries
Global Identifiability of 𝓁 1 -based Dictionary Learning via Matrix Volume Optimization
UViM: A Unified Modeling Approach for Vision with Learned Guiding Codes
Rethinking Generative Mode Coverage: A Pointwise Guaranteed Approach
Evaluating Efficient Performance Estimators of Neural Architectures
Kernel-Based Tests for Likelihood-Free Hypothesis Testing
Position-based Scaled Gradient for Model Quantization and Pruning
Finding Local Minima Efficiently in Decentralized Optimization
Residual Distillation: Towards Portable Deep Neural Networks without Shortcuts
Byzantine Resilient Distributed Multi-Task Learning
Interpretability at Scale: Identifying Causal Mechanisms in Alpaca
Volume Regularization for Binary Classification
Where2Explore: Few-shot Affordance Learning for Unseen Novel Categories of Articulated Objects
Symmetry-Informed Geometric Representation for Molecules, Proteins, and Crystalline Materials
Personalized Federated Learning via Feature Distribution Adaptation
Incomplete Multimodality-Diffused Emotion Recognition
Hidden Poison: Machine Unlearning Enables Camouflaged Poisoning Attacks
Secretary and Online Matching Problems with Machine Learned Advice
Modeling Midazolam's Effect on the Hippocampus and Recognition Memory
Point Cloud Matters: Rethinking the Impact of Different Observation Spaces on Robot Learning
The Relaxed Online Maximum Margin Algorithm
Deliberated Domain Bridging for Domain Adaptive Semantic Segmentation
NAP: Neural 3D Articulated Object Prior
UniAudio 1.5: Large Language Model-Driven Audio Codec is A Few-Shot Audio Task Learner
Neural Network Application to Diagnostics
Understanding the Role of Adaptivity in Machine Teaching: The Case of Version Space Learners
Near-Optimal No-Regret Learning Dynamics for General Convex Games
Coupling-based Invertible Neural Networks Are Universal Diffeomorphism Approximators
Density Estimation via Discrepancy Based Adaptive Sequential Partition
Graphical Time Warping for Joint Alignment of Multiple Curves
Active Learning for Derivative-Based Global Sensitivity Analysis with Gaussian Processes
GPT4Tools: Teaching Large Language Model to Use Tools via Self-instruction
Predictive Coding beyond Gaussian Distributions
Analysis of Representations for Domain Adaptation
The SenSE Toolkit: A System for Visualization and Explanation of Semantic Shift
MosAIc: Finding Artistic Connections across Culture with Conditional Image Retrieval
HEPrune: Fast Private Training of Deep Neural Networks With Encrypted Data Pruning
Quality Not Quantity: On the Interaction between Dataset Design and Robustness of CLIP
IMDL-BenCo: A Comprehensive Benchmark and Codebase for Image Manipulation Detection & Localization
A Closer Look at AUROC and AUPRC under Class Imbalance
Contrastive Sampling Chains in Diffusion Models
Controllable Invariance through Adversarial Feature Learning
Bezier Gaussian Processes for Tall and Wide Data
Explain My Surprise: Learning Efficient Long-Term Memory by predicting uncertain outcomes
Universal Off-Policy Evaluation
LASSO with Non-linear Measurements is Equivalent to One With Linear Measurements
A Variational Approach to Learning Curves
Convex Multi-view Subspace Learning
Receptive field structure of flow detectors for heading perception
Fast Routing under Uncertainty: Adaptive Learning in Congestion Games via Exponential Weights
Convolutional-Recursive Deep Learning for 3D Object Classification
A Contrastive Framework for Neural Text Generation
In Differential Privacy, There is Truth: on Vote-Histogram Leakage in Ensemble Private Learning
Periodic agent-state based Q-learning for POMDPs
Aggregating Classification Accuracy across Time: Application to Single Trial EEG
UniDSeg: Unified Cross-Domain 3D Semantic Segmentation via Visual Foundation Models Prior
Debugging and Explaining Metric Learning Approaches: An Influence Function Based Perspective
A Global Structural EM Algorithm for a Model of Cancer Progression
An Application of Tree-Structured Expectation Propagation for Channel Decoding
Intrinsic Robustness of Prophet Inequality to Strategic Reward Signaling
Unleashing the Power of Randomization in Auditing Differentially Private ML
Soft Contrastive Learning for Visual Localization
AMAG: Additive, Multiplicative and Adaptive Graph Neural Network For Forecasting Neuron Activity
Reinforcement Learning of Theorem Proving
RCC Cannot Compute Certain FSA, Even with Arbitrary Transfer Functions
A Primal-Dual-Assisted Penalty Approach to Bilevel Optimization with Coupled Constraints
Pranking with Ranking
Learning Kernels Using Local Rademacher Complexity
Unlocking Fairness: a Trade-off Revisited
Reflective Multi-Agent Collaboration based on Large Language Models
SKFlow: Learning Optical Flow with Super Kernels
Distributional Policy Evaluation: a Maximum Entropy approach to Representation Learning
Concrete Score Matching: Generalized Score Matching for Discrete Data
Deep invariant networks with differentiable augmentation layers
Generalized Delayed Feedback Model with Post-Click Information in Recommender Systems
Unpaired Multi-Domain Causal Representation Learning
SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability
A Variant of Anderson Mixing with Minimal Memory Size
Thinker: Learning to Plan and Act
Fair Hierarchical Clustering
MKGL: Mastery of a Three-Word Language
Robust and Actively Secure Serverless Collaborative Learning
Act As You Wish: Fine-Grained Control of Motion Diffusion Model with Hierarchical Semantic Graphs
Wasserstein Gradient Flows for Optimizing Gaussian Mixture Policies
CNN 2 : Viewpoint Generalization via a Binocular Vision
Time-Sensitive Recommendation From Recurrent User Activities
Scalable Semi-Supervised Aggregation of Classifiers
Latent World Models For Intrinsically Motivated Exploration
Context-guided Embedding Adaptation for Effective Topic Modeling in Low-Resource Regimes
Ultra-Low Precision 4-bit Training of Deep Neural Networks
DualCoOp: Fast Adaptation to Multi-Label Recognition with Limited Annotations
A Large-Scale Human-Centric Benchmark for Referring Expression Comprehension in the LMM Era
Marginalised Gaussian Processes with Nested Sampling
Gaussian-Based Pooling for Convolutional Neural Networks
Double Bubble, Toil and Trouble: Enhancing Certified Robustness through Transitivity
Distributed Flexible Nonlinear Tensor Factorization
Exploring the Limits of Domain-Adaptive Training for Detoxifying Large-Scale Language Models
No-Regret Learning and Mixed Nash Equilibria: They Do Not Mix
Spider2-V: How Far Are Multimodal Agents From Automating Data Science and Engineering Workflows?
The Maximal Causes of Natural Scenes are Edge Filters
PAC-Bayes & Margins
Meta-AdaM: An Meta-Learned Adaptive Optimizer with Momentum for Few-Shot Learning
Anomaly Detection with Score functions based on Nearest Neighbor Graphs
Planning to the Information Horizon of BAMDPs via Epistemic State Abstraction
OPERA: Automatic Offline Policy Evaluation with Re-weighted Aggregates of Multiple Estimators
Learning Decision Theoretic Utilities through Reinforcement Learning
BLIP-Diffusion: Pre-trained Subject Representation for Controllable Text-to-Image Generation and Editing
Sample Constrained Treatment Effect Estimation
Applications of Error Back-Propagation to Phonetic Classification
Globally optimal score-based learning of directed acyclic graphs in high-dimensions
DESSERT: An Efficient Algorithm for Vector Set Search with Vector Set Queries
Learning Equivariant Energy Based Models with Equivariant Stein Variational Gradient Descent
Robust group and simultaneous inferences for high-dimensional single index model
Approximate Gaussian process inference for the drift function in stochastic differential equations
PANORAMIA: Privacy Auditing of Machine Learning Models without Retraining
Learning long-term dependencies is not as difficult with NARX networks
Locally Adaptive Bayesian Multivariate Time Series
Policy Poisoning in Batch Reinforcement Learning and Control
Debugging Tests for Model Explanations
Learning on Large Graphs using Intersecting Communities
Deep Temporal Sigmoid Belief Networks for Sequence Modeling
Taming "data-hungry" reinforcement learning? Stability in continuous state-action spaces
GTSinger: A Global Multi-Technique Singing Corpus with Realistic Music Scores for All Singing Tasks
AIMEE: Interactive model maintenance with rule-based surrogates
Grammar Learning by a Self-Organizing Network
Conic Descent and its Application to Memory-efficient Optimization over Positive Semidefinite Matrices
Convex-Concave Zero-Sum Stochastic Stackelberg Games
Compatible Reward Inverse Reinforcement Learning
Statistical bounds for entropic optimal transport: sample complexity and the central limit theorem
Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012, Lake Tahoe, Nevada, United States
AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation
Using Embeddings for Causal Estimation of Peer Influence in Social Networks
Transformers to SSMs: Distilling Quadratic Knowledge to Subquadratic Models
Implicit Zoo: A Large-Scale Dataset of Neural Implicit Functions for 2D Images and 3D Scenes
Hamming Ball Auxiliary Sampling for Factorial Hidden Markov Models
RectifID: Personalizing Rectified Flow with Anchored Classifier Guidance
An Improved Policy Iteration Algorithm for Partially Observable MDPs
Metric Learning for Temporal Sequence Alignment
SSDiff: Spatial-spectral Integrated Diffusion Model for Remote Sensing Pansharpening
LOT: Layer-wise Orthogonal Training on Improving l2 Certified Robustness
Paxion: Patching Action Knowledge in Video-Language Foundation Models
Who Leads and Who Follows in Strategic Classification?
Boosting Semi-Supervised Scene Text Recognition via Viewing and Summarizing
A Causal View on Robustness of Neural Networks
Minimax Optimal Quantile and Semi-Adversarial Regret via Root-Logarithmic Regularizers
Low-Rank Bandit Methods for High-Dimensional Dynamic Pricing
Enhancing Large Language Models through Adaptive Tokenizers
Disentangled Unsupervised Skill Discovery for Efficient Hierarchical Reinforcement Learning
SARAMIS: Simulation Assets for Robotic Assisted and Minimally Invasive Surgery
Forward dynamic models in human motor control: Psychophysical evidence
Neutralizing Self-Selection Bias in Sampling for Sortition
Neural Flow Diffusion Models: Learnable Forward Process for Improved Diffusion Modelling
MetaAligner: Towards Generalizable Multi-Objective Alignment of Language Models
Multilinear Dynamical Systems for Tensor Time Series
Differentially Private Testing of Identity and Closeness of Discrete Distributions
Contextual Dynamic Pricing with Unknown Noise: Explore-then-UCB Strategy and Improved Regrets
Revisiting Adversarial Training for ImageNet: Architectures, Training and Generalization across Threat Models
A Mixed-Signal VLSI for Real-Time Generation of Edge-Based Image Vectors
Proceedings of the Crowd Science Workshop: Remoteness, Fairness, and Mechanisms as Challenges of Data Supply by Humans for Automation co-located with 34th Conference on Neural Information Processing Systems, CSW@NeurIPS 2020, Vancouver, BC, Canada / Online Event, December 11, 2020
Beyond Tikhonov: faster learning with self-concordant losses, via iterative regularization
An Optimal and Scalable Matrix Mechanism for Noisy Marginals under Convex Loss Functions
FastCorrect: Fast Error Correction with Edit Alignment for Automatic Speech Recognition
Minimax Value Interval for Off-Policy Evaluation and Policy Optimization
Learning 3D Dense Correspondence via Canonical Point Autoencoder
Firing rate predictions in optimal balanced networks
Structured in Space, Randomized in Time: Leveraging Dropout in RNNs for Efficient Training
No-regret Algorithms for Online Convex Programs
Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks
Lightweight Generative Adversarial Networks for Text-Guided Image Manipulation
Training Generative Adversarial Networks by Solving Ordinary Differential Equations
Incremental Clustering: The Case for Extra Clusters
DataPerf: Benchmarks for Data-Centric AI Development
Analysis of Linsker's Simulations of Hebbian Rules
Mastering Atari Games with Limited Data
Collaborative PAC Learning
STimage-1K4M: A histopathology image-gene expression dataset for spatial transcriptomics
Non-stationary dynamic Bayesian networks
NOMAD: Nonlinear Manifold Decoders for Operator Learning
Color Visual Illusions: A Statistics-based Computational Model
Markov Chain Monte Carlo with People
NEO: Non Equilibrium Sampling on the Orbits of a Deterministic Transform
Alleviating Distortion in Image Generation via Multi-Resolution Diffusion Models and Time-Dependent Layer Normalization
DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection
Combinatorial Pure Exploration of Multi-Armed Bandits
The Infinite Gaussian Mixture Model
Reconstructing Patterns of Information Diffusion from Incomplete Observations
Machine learning structure preserving brackets for forecasting irreversible processes
Neurophysiological Evidence of Cooperative Mechanisms for Stereo Computation
Dynamic Normalization and Relay for Video Action Recognition
Normalization Helps Training of Quantized LSTM
Bias, Variance and the Combination of Least Squares Estimators
Schema Learning: Experience-Based Construction of Predictive Action Models
Metric Space Magnitude for Evaluating the Diversity of Latent Representations
FairMedFM: Fairness Benchmarking for Medical Imaging Foundation Models
Would I Lie To You? Inference Time Alignment of Language Models using Direct Preference Heads
Grid4D: 4D Decomposed Hash Encoding for High-Fidelity Dynamic Gaussian Splatting
Don't Generate Me: Training Differentially Private Generative Models with Sinkhorn Divergence
Optimal Rates for Nonparametric Density Estimation under Communication Constraints
Polyhistor: Parameter-Efficient Multi-Task Adaptation for Dense Vision Tasks
Syndicated Bandits: A Framework for Auto Tuning Hyper-parameters in Contextual Bandit Algorithms
SILG: The Multi-domain Symbolic Interactive Language Grounding Benchmark
Approximate inference in latent Gaussian-Markov models from continuous time observations
EPIC Fields: Marrying 3D Geometry and Video Understanding
An Information-Theoretic Framework for Deep Learning
Advances in Neural Information Processing Systems 4, [NIPS Conference, Denver, Colorado, USA, December 2-5, 1991]
An Alternative to Low-level-Sychrony-Based Methods for Speech Detection
VanillaNet: the Power of Minimalism in Deep Learning
Connected Letter Recognition with a Multi-State Time Delay Neural Network
Spectral Bias Outside the Training Set for Deep Networks in the Kernel Regime
Are Large-scale Soft Labels Necessary for Large-scale Dataset Distillation?
I2Q: A Fully Decentralized Q-Learning Algorithm
Designing Counterfactual Generators using Deep Model Inversion
Implicit Sparse Regularization: The Impact of Depth and Early Stopping
Direct Preference Optimization: Your Language Model is Secretly a Reward Model
Consistency Purification: Effective and Efficient Diffusion Purification towards Certified Robustness
A Theory-Driven Self-Labeling Refinement Method for Contrastive Representation Learning
Understanding Information Storage and Transfer in Multi-Modal Large Language Models
Ranking Measures and Loss Functions in Learning to Rank
Rare Gems: Finding Lottery Tickets at Initialization
Tame a Wild Camera: In-the-Wild Monocular Camera Calibration
Towards Scalable and Stable Parallelization of Nonlinear RNNs
Distributed Optimization in Adaptive Networks
Learning Diverse and Discriminative Representations via the Principle of Maximal Coding Rate Reduction
Almost Optimal Model-Free Reinforcement Learningvia Reference-Advantage Decomposition
Modelling Uncertainty in the Game of Go
State Chrono Representation for Enhancing Generalization in Reinforcement Learning
Information Competing Process for Learning Diversified Representations
Correlated-PCA: Principal Components' Analysis when Data and Noise are Correlated
Active Set Ordering
Unsupervised object-centric video generation and decomposition in 3D
Efficient Estimation of OOMs
A New Theoretical Framework for Fast and Accurate Online Decision-Making
Defending Pre-trained Language Models as Few-shot Learners against Backdoor Attacks
ALPS: Improved Optimization for Highly Sparse One-Shot Pruning for Large Language Models
Improving Generalization in Reinforcement Learning with Mixture Regularization
Connectivity-Driven Pseudo-Labeling Makes Stronger Cross-Domain Segmenters
DrivAerNet++: A Large-Scale Multimodal Car Dataset with Computational Fluid Dynamics Simulations and Deep Learning Benchmarks
A state-space model for inferring effective connectivity of latent neural dynamics from simultaneous EEG/fMRI
No RL, No Simulation: Learning to Navigate without Navigating
Diff-eRank: A Novel Rank-Based Metric for Evaluating Large Language Models
Optimal Classification under Performative Distribution Shift
Decentralized Learning in Online Queuing Systems
Low Degree Hardness for Broadcasting on Trees
Facial Expression Transfer with Input-Output Temporal Restricted Boltzmann Machines
Learning Kernels with Radiuses of Minimum Enclosing Balls
Exploring Adversarial Robustness of Deep State Space Models
FedLLM-Bench: Realistic Benchmarks for Federated Learning of Large Language Models
Theoretical Comparisons of Positive-Unlabeled Learning against Positive-Negative Learning
Global Convergence Analysis of Local SGD for Two-layer Neural Network without Overparameterization
SWAD: Domain Generalization by Seeking Flat Minima
Learning Feature Selection Dependencies in Multi-task Learning
Learning Generalizable Device Placement Algorithms for Distributed Machine Learning
Does Momentum Change the Implicit Regularization on Separable Data?
AdvAD: Exploring Non-Parametric Diffusion for Imperceptible Adversarial Attacks
Learning Partially Observable Models Using Temporally Abstract Decision Trees
Combinatorial Multi-Armed Bandit with General Reward Functions
Robust Distributed Learning: Tight Error Bounds and Breakdown Point under Data Heterogeneity
Large-Scale Distributed Learning via Private On-Device LSH
An Additive Latent Feature Model for Transparent Object Recognition
Empathic AI Painter: A Computational Creativity System with Embodied Conversational Interaction
LAM3D: Large Image-Point Clouds Alignment Model for 3D Reconstruction from Single Image
Generalized equivalences between subsampling and ridge regularization
A Near-Optimal Algorithm for Stochastic Bilevel Optimization via Double-Momentum
Sifting through the noise: Universal first-order methods for stochastic variational inequalities
Efficient and Near-Optimal Smoothed Online Learning for Generalized Linear Functions
Understanding the Effect of Stochasticity in Policy Optimization
A Non-Parametric Multi-Scale Statistical Model for Natural Images
Fully Explicit Dynamic Gaussian Splatting
Meta-Consolidation for Continual Learning
DatasetDM: Synthesizing Data with Perception Annotations Using Diffusion Models
Universal Option Models
Improving Neural Network Training in Low Dimensional Random Bases
Minimizing Regret on Reflexive Banach Spaces and Nash Equilibria in Continuous Zero-Sum Games
The Mamba in the Llama: Distilling and Accelerating Hybrid Models
Optimal Time Complexities of Parallel Stochastic Optimization Methods Under a Fixed Computation Model
Offline RL Without Off-Policy Evaluation
A Continuous Time Framework for Discrete Denoising Models
Dopamine Bonuses
Why Is My Classifier Discriminatory?
Multivariate Time Series Imputation with Generative Adversarial Networks
GUST: Combinatorial Generalization by Unsupervised Grouping with Neuronal Coherence
Time Series Prediction using Mixtures of Experts
Beyond BatchNorm: Towards a Unified Understanding of Normalization in Deep Learning
Learner-aware Teaching: Inverse Reinforcement Learning with Preferences and Constraints
Shepard Convolutional Neural Networks
A PAC-Bayesian Analysis of Randomized Learning with Application to Stochastic Gradient Descent
Mean-field Langevin dynamics: Time-space discretization, stochastic gradient, and variance reduction
Vitron: A Unified Pixel-level Vision LLM for Understanding, Generating, Segmenting, Editing
Approximate Supermodularity Bounds for Experimental Design
PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networks
Deep Subspace Clustering with Data Augmentation
Neuromorphic Bistable VLSI Synapses with Spike-Timing-Dependent Plasticity
Multi-Instance Partial-Label Learning with Margin Adjustment
Model Selection in Contextual Stochastic Bandit Problems
Simple and Controllable Music Generation