One Size Fits All: Can We Train One Denoiser for All Noise Levels? (2016) mentioned, for reliable data analysis, the isometric embedding in low-dimensional space is necessary. 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Do not remove: This comment is monitored to verify that the site is working properly, Conditional Augmentation for Generative Modeling, On Leveraging Pretrained GANs for Generation with Limited Data, Implicit Euler Skip Connections: Enhancing Adversarial Robustness via Numerical Stability, Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models without Sampling, From Importance Sampling to Doubly Robust Policy Gradient, Understanding and Mitigating the Tradeoff between Robustness and Accuracy, Learning De-biased Representations with Biased Representations, Information Particle Filter Tree: An Online Algorithm for POMDPs with Belief-Based Rewards on Continuous Domains, Enhanced POET: Open-ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their Solutions, Stochastic Differential Equations with Variational Wishart Diffusions, Predicting Choice with Set-Dependent Aggregation, A Flexible Latent Space Model for Multilayer Networks, Estimating the Error of Randomized Newton Methods: A Bootstrap Approach, Loss Function Search for Face Recognition, Unsupervised Transfer Learning for Spatiotemporal Predictive Networks, A distributional view on multi objective policy optimization, On Efficient Low Distortion Ultrametric Embedding, Dispersed EM-VAEs for Interpretable Text Generation, Acceleration through spectral density estimation, Mix-n-Match : Ensemble and Compositional Methods for Uncertainty Calibration in Deep Learning, Linear Convergence of Randomized Primal-Dual Coordinate Method for Large-scale Linear Constrained Convex Programming, Accountable Off-Policy Evaluation via a Kernelized Bellman Statistics, Towards a General Theory of Infinite-Width Limits of Neural Classifiers, Self-supervised Label Augmentation via Input Transformations, Provable Representation Learning for Imitation Learning via Bi-level Optimization, Inter-domain Deep Gaussian Processes with RKHS Fourier Features, Superpolynomial Lower Bounds for Learning One-Layer Neural Networks using Gradient Descent, Online Convex Optimization in the Random Order Model, Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks, Enhancing Simple Models by Exploiting What They Already Know, Detecting Out-of-Distribution Examples with Gram Matrices, Adaptive Estimator Selection for Off-Policy Evaluation, Dissecting Non-Vacuous Generalization Bounds based on the Mean-Field Approximation, Non-convex Learning via Replica Exchange Stochastic Gradient MCMC, The Effect of Natural Distribution Shift on Question Answering Models, On Learning Language-Invariant Representations for Universal Machine Translation, Converging to Team-Maxmin Equilibria in Zero-Sum Multiplayer Games, Go Wide, Then Narrow: Efficient Training of Deep Thin Networks, Reducing Sampling Error in Batch Temporal Difference Learning, Channel Equilibrium Networks for Learning Deep Representation, Learning Representations that Support Extrapolation, Computational and Statistical Tradeoffs in Inferring Combinatorial Structures of Ising Model, Learning disconnected manifolds: a no GAN's land, Full Law Identification In Graphical Models Of Missing Data: Completeness Results, Online mirror descent and dual averaging: keeping pace in the dynamic case, Double Reinforcement Learning for Efficient and Robust Off-Policy Evaluation, A Free-Energy Principle for Representation Learning, Convolutional dictionary learning based auto-encoders for natural exponential-family distributions, Knowing The What But Not The Where in Bayesian Optimization, Being Bayesian about Categorical Probability, Optimizer Benchmarking Needs to Account for Hyperparameter Tuning, Unsupervised Speech Decomposition via Triple Information Bottleneck, Efficient proximal mapping of the path-norm regularizer of shallow networks, Neural Contextual Bandits with UCB-based Exploration, Model Fusion with Kullback--Leibler Divergence, Bayesian Differential Privacy for Machine Learning, Generating Programmatic Referring Expressions via Program Synthesis, Extreme Multi-label Classification from Aggregated Labels, Learning Deep Kernels for Non-Parametric Two-Sample Tests, MoNet3D: Towards Accurate Monocular 3D Object Localization in Real Time, Dual-Path Distillation: A Unified Framework to Improve Black-Box Attacks, Streaming Coresets for Symmetric Tensor Factorization, DROCC: Deep Robust One-Class Classification, Doubly robust off-policy evaluation with shrinkage, The Complexity of Finding Stationary Points with Stochastic Gradient Descent, Learning the Valuations of a $k$-demand Agent, Anderson Acceleration of Proximal Gradient Methods, Let's Agree to Agree: Neural Networks Share Classification Order on Real Datasets, Stochastic Regret Minimization in Extensive-Form Games, Low Bias Low Variance Gradient Estimates for Hierarchical Boolean Stochastic Networks, NetGAN without GAN: From Random Walks to Low-Rank Approximations, Random extrapolation for primal-dual coordinate descent, Finite-Time Last-Iterate Convergence for Multi-Agent Learning in Games, Composing Molecules with Multiple Property Constraints, Strength from Weakness: Fast Learning Using Weak Supervision, Margin-aware Adversarial Domain Adaptation with Optimal Transport, A quantile-based approach for hyperparameter transfer learning, High-dimensional Robust Mean Estimation via Gradient Descent, Composable Sketches for Functions of Frequencies: Beyond the Worst Case, StochasticRank: Global Optimization of Scale-Free Discrete Functions, All in the (Exponential) Family: Information Geometry and Thermodynamic Variational Inference, Efficient non-conjugate Gaussian process factor models for spike countdata using polynomial approximations, Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data, Scalable and Efficient Comparison-based Search without Features, Multi-Agent Routing Value Iteration Network, On Second-Order Group 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Embedding Layers, Interpretable, Multidimensional, Multimodal Anomaly Detection with Negative Sampling for Detection of Device Failure, Improved Bounds on Minimax Regret under Logarithmic Loss via Self-Concordance, Efficient Intervention Design for Causal Discovery with Latents, The role of regularization in classification of high-dimensional noisy Gaussian mixture, Recurrent Hierarchical Topic-Guided RNN for Language Generation, Breaking the Curse of Many Agents: Provable Mean Embedding $Q$-Iteration for Mean-Field Reinforcement Learning, Parameterized Rate-Distortion Stochastic Encoder, Leveraging Frequency Analysis for Deep Fake Image Recognition, Median Matrix Completion: from Embarrassment to Optimality, Acceleration for Compressed Gradient Descent in Distributed Optimization, Thompson Sampling Algorithms for Mean-Variance Bandits, Statistically Preconditioned Accelerated Gradient Method for Distributed Optimization, A Geometric Approach to Archetypal Analysis via Sparse Projections, Efficiently Solving MDPs with Stochastic Mirror Descent, Neural Networks are Convex Regularizers: Exact Polynomial-time Convex Optimization Formulations for Two-Layer Networks, Efficient Optimistic Exploration in Linear-Quadratic Regulators via Lagrangian Relaxation, Safe Reinforcement Learning in Constrained Markov Decision Processes, ControlVAE: Controllable Variational Autoencoder, Test-Time Training for Generalization under Distribution Shifts, Instance-hiding Schemes for Private Distributed Learning, Word-Level Speech Recognition With a Letter to Word Encoder, Structured Prediction with Partial Labelling through the Infimum Loss, Visual Grounding of Learned Physical Models, Infinite attention: NNGP and NTK for deep attention networks, Estimation of Bounds on Potential Outcomes For Decision Making, Understanding and Stabilizing GANs' Training Dynamics Using Control Theory, OPtions as REsponses: Grounding behavioural hierarchies in multi-agent reinforcement learning, Combining Differentiable PDE Solvers and Graph Neural Networks for Fluid Flow Prediction, Randomization matters How to defend against strong adversarial attacks, Kernelized Stein Discrepancy Tests of Goodness-of-fit for Time-to-Event Data, Adversarial Learning Guarantees for Linear Hypotheses and Neural Networks, Improving Robustness of Deep-Learning-Based Image Reconstruction, Emergence of Separable Manifolds in Deep Language Representations, SDE-Net: Equipping Deep Neural Networks with Uncertainty Estimates, Divide and Conquer: Leveraging Intermediate Feature Representations for Quantized Training of Neural Networks, Confidence-Calibrated Adversarial Training: Generalizing to Unseen Attacks, Efficient Domain Generalization via Common-Specific Low-Rank Decomposition, Optimal approximation for unconstrained non-submodular minimization. Ali Jadbabaie 2020 Poster: Learning Adversarial Markov Decision Processes with Bandit Feedback and Unknown Transition » Adaptive Sketching for Fast and Convergent Canonical Polyadic Decomposition, The Sample Complexity of Best-$k$ Items Selection from Pairwise Comparisons, Hybrid Stochastic-Deterministic Minibatch Proximal Gradient: Less-Than-Single-Pass Optimization with Nearly Optimal Generalization, Universal Equivariant Multilayer Perceptrons, Automatic Reparameterisation of Probabilistic Programs, Stronger and Faster Wasserstein Adversarial Attacks, Puzzle Mix: Exploiting Saliency and Local Statistics for Optimal Mixup, Robustness to Spurious Correlations via Human Annotations, Operation-Aware Soft Channel Pruning using Differentiable Masks, CURL: Contrastive Unsupervised Representation Learning for Reinforcement Learning, Dual Mirror Descent for Online Allocation Problems, Fully Parallel Hyperparameter Search: Reshaped Space-Filling, Striving for simplicity and performance in off-policy DRL: Output Normalization and Non-Uniform Sampling, DeBayes: a Bayesian method for debiasing network embeddings, Sub-linear Memory Sketches for Near Neighbor Search on Streaming Data with RACE, The Intrinsic Robustness of Stochastic Bandits to Strategic Manipulation, Learning with Feature and Distribution Evolvable Streams, Extra-gradient with player sampling for faster convergence in n-player games, Information-Theoretic Local Minima Characterization and Regularization, Small Data, Big Decisions: Model Selection in the Small-Data Regime, A Sample Complexity Separation between Non-Convex and Convex Meta-Learning, Online Dense Subgraph Discovery via Blurred-Graph Feedback, Real-Time Optimisation for Online Learning in Auctions, Learning to Simulate and Design for Structural Engineering, On the consistency of top-k surrogate losses, Adversarial Attacks on Probabilistic Autoregressive Forecasting Models, T-GD: Transferable GAN-generated Images Detection Framework, Goodness-of-Fit Tests for Inhomogeneous Random Graphs, Adversarial Mutual Information for Text Generation, Active World Model Learning in Agent-rich Environments with Progress Curiosity, Interpretations are Useful: Penalizing Explanations to Align Neural Networks with Prior Knowledge, FedBoost: A Communication-Efficient Algorithm for Federated Learning, Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach, Understanding the Impact of Model Incoherence on Convergence of Incremental SGD with Random Reshuffle, Representations for Stable Off-Policy Reinforcement Learning, Tensor denoising and completion based on ordinal observations, Improving Transformer Optimization Through Better Initialization, A Flexible Framework for Nonparametric Graphical Modeling that Accommodates Machine Learning, Sparsified Linear Programming for Zero-Sum Equilibrium Finding, Eliminating the Invariance on the Loss Landscape of Linear Autoencoders, On conditional versus marginal bias in multi-armed bandits, An Explicitly Relational Neural Network Architecture, A Game Theoretic Perspective on Model-Based Reinforcement Learning, Approximation Guarantees of Local Search Algorithms via Localizability of Set Functions, Consistent Estimators for Learning to Defer to an Expert, Nearly Linear Row Sampling Algorithm for Quantile Regression, Student Specialization in Deep Rectified Networks With Finite Width and Input Dimension, Provable guarantees for decision tree induction: the agnostic setting, Familywise Error Rate Control by Interactive Unmasking, Domain Aggregation Networks for Multi-Source Domain Adaptation, Generalized and Scalable Optimal Sparse Decision Trees, Data Amplification: Instance-Optimal Property Estimation, Sparse Convex Optimization via Adaptively Regularized Hard Thresholding, Online Pricing with Offline Data: Phase Transition and Inverse Square Law, Network Pruning by Greedy Subnetwork Selection, Forecasting sequential data using Consistent Koopman Autoencoders, Amortized Finite Element Analysis for Fast PDE-Constrained Optimization, Provable Self-Play Algorithms for Competitive Reinforcement Learning, Reinforcement Learning with Differential Privacy, Scalable Identification of Partially Observed Systems with Certainty-Equivalent EM, Sharp Composition Bounds for Gaussian Differential Privacy via Edgeworth Expansion, Towards Accurate Post-training Network Quantization via Bit-Split and Stitching, Discriminative Jackknife: Quantifying Uncertainty in Deep Learning via Higher-Order Influence Functions, A Distributional Framework For Data Valuation, Oracle Efficient Private Non-Convex Optimization, Explainable k-Means and k-Medians Clustering, On Validation and Planning of An Optimal Decision Rule with Application in Healthcare Studies, On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems, Feature-map-level Online Adversarial Knowledge Distillation, Robust One-Bit Recovery via ReLU Generative Networks: Near-Optimal Statistical Rate and Global Landscape Analysis, Rigging the Lottery: Making All Tickets Winners, Adaptive Sampling for Estimating Probability Distributions, Educating Text Autoencoders: Latent Representation Guidance via Denoising, Layered Sampling for Robust Optimization Problems, On Contrastive Learning for Likelihood-free Inference, Learning From Strategic Agents: Accuracy, Improvement, and Causality, Meta Variance Transfer: Learning to Augment from the Others, Fractional Underdamped Langevin Dynamics: Retargeting SGD with Momentum under Heavy-Tailed Gradient Noise, Progressive Identification of True Labels for Partial-Label Learning, Estimating Model Uncertainty of Neural Network in Sparse Information Form, Learning Task-Agnostic Embedding of Multiple Black-Box Experts for Multi-Task Model Fusion, Guided Learning of Nonconvex Models through Successive Functional Gradient Optimization, Inertial Block Proximal Methods for Non-Convex Non-Smooth Optimization, Retro*: Learning Retrosynthetic Planning with Neural Guided A* Search, Intrinsic Reward Driven Imitation Learning via Generative Model, Generalisation error in learning with random features and the hidden manifold model, Structured Policy Iteration for Linear Quadratic Regulator, Encoding Musical Style with Transformer Autoencoders, Minimally distorted Adversarial Examples with a Fast Adaptive Boundary Attack, Graph Random Neural Features for Distance-Preserving Graph Representations, The Implicit Regularization of Stochastic Gradient Flow for Least Squares, Recovery of sparse signals from a mixture of linear samples, Neural Topic Modeling with Continual Lifelong Learning, Online Learned Continual Compression with Adaptive Quantization Modules, T-Basis: a Compact Representation for Neural Networks, Upper bounds for Model-Free Row-Sparse Principal Component Analysis, History-Gradient Aided Batch Size Adaptation for Variance Reduced Algorithms, DeepCoDA: personalized interpretability for compositional health, Dynamics of Deep Neural Networks and Neural Tangent Hierarchy, Stochastic Frank-Wolfe for Constrained Finite-Sum Minimization, Constant Curvature Graph Convolutional Networks, How to Train Your Neural ODE: the World of Jacobian and Kinetic Regularization, How recurrent networks implement contextual processing in sentiment analysis, Duality in RKHSs with Infinite Dimensional Outputs: Application to Robust Losses, Few-shot Relation Extraction via Bayesian Meta-learning on Task Graphs, Simple and Deep Graph Convolutional Networks, Two Routes to Scalable Credit Assignment without Weight Symmetry, Radioactive data: tracing through training, Learning Autoencoders with Relational Regularization, On Thompson Sampling with Langevin Algorithms, Training Deep Energy-Based Models with f-Divergence Minimization, Scalable Differentiable Physics for Learning and Control, Implicit Generative Modeling for Efficient Exploration, Optimally Solving Two-Agent Decentralized POMDPs Under One-Sided Information Sharing, TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular Dynamics, Bayesian Optimisation over Multiple Continuous and Categorical Inputs, Entropy Minimization In Emergent Languages, Minimax Pareto Fairness: A Multi Objective Perspective, IPBoost – Non-Convex Boosting via Integer Programming, Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules, Goal-Aware Prediction: Learning to Model What Matters, DINO: Distributed Newton-Type Optimization Method, Multi-Task Learning with User Preferences: Gradient Descent with Controlled Ascent in Pareto Optimization, Piecewise Linear Regression via a Difference of Convex Functions, Inductive Bias-driven Reinforcement Learning For Efficient Schedules in Heterogeneous Clusters, Distance Metric Learning with Joint Representation Diversification, A simpler approach to accelerated optimization: iterative averaging meets optimism, Learning Adversarially Robust Representations via Worst-Case Mutual Information Maximization, On the Expressivity of Neural Networks for Deep Reinforcement Learning, PoWER-BERT: Accelerating BERT Inference via Progressive Word-vector Elimination, Restarted Bayesian Online Change-point Detector achieves Optimal Detection Delay, Linear Mode Connectivity and the Lottery Ticket Hypothesis, Gradient Temporal-Difference Learning with Regularized Corrections, Task-Oriented Active Perception and Planning in Environments with Partially Known Semantics, Semiparametric Nonlinear Bipartite Graph Representation Learning with Provable Guarantees, Improving the Sample and Communication Complexity for Decentralized Non-Convex Optimization: Joint Gradient Estimation and Tracking, On hyperparameter tuning in general clustering problemsm, Scalable Deep Generative Modeling for Sparse Graphs, Data Valuation using Reinforcement Learning, Representation Learning via Adversarially-Contrastive Optimal Transport, Class-Weighted Classification: Trade-offs and Robust Approaches, Cautious Adaptation For Reinforcement Learning in Safety-Critical Settings, NGBoost: Natural Gradient Boosting for Probabilistic Prediction, Minimax-Optimal Off-Policy Evaluation with Linear Function Approximation, CAUSE: Learning Granger Causality from Event Sequences using Attribution Methods, Do We Really Need to Access the Source Data?

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