Ioannis Mitliagkas

Compositional Risk Minimization ICML 2025 Solving hidden monotone variational inequalities with surrogate losses ICLR 2025 Empirical Analysis of Model Selection for Heterogeneous Causal Effect Estimation ICLR 2024 Expecting The Unexpected: Towards Broad Out-Of-Distribution Detection NeurIPS 2024 No Wrong Turns: The Simple Geometry Of Neural Networks Optimization Paths ICML 2024 Additive Decoders for Latent Variables Identification and Cartesian-Product Extrapolation NeurIPS 2023 A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Games ICLR 2023 CADet: Fully Self-Supervised Out-Of-Distribution Detection With Contrastive Learning NeurIPS 2023 Neural Networks Efficiently Learn Low-Dimensional Representations with SGD ICLR 2023 Performative Prediction with Neural Networks AISTATS 2023 Synergies between Disentanglement and Sparsity: Generalization and Identifiability in Multi-Task Learning ICML 2023 Gradient Descent Is Optimal Under Lower Restricted Secant Inequality And Upper Error Bound NeurIPS 2022 Towards efficient representation identification in supervised learning CLeaR 2022 Adversarial score matching and improved sampling for image generation ICLR 2021 A Study of Condition Numbers for First-Order Optimization AISTATS 2021 Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization NeurIPS 2021 Stochastic Gradient Descent-Ascent and Consensus Optimization for Smooth Games: Convergence Analysis under Expected Co-coercivity NeurIPS 2021 Accelerating Smooth Games by Manipulating Spectral Shapes AISTATS 2020 A Tight and Unified Analysis of Gradient-Based Methods for a Whole Spectrum of Differentiable Games AISTATS 2020 In search of robust measures of generalization NeurIPS 2020 Linear Lower Bounds and Conditioning of Differentiable Games ICML 2020 Stochastic Hamiltonian Gradient Methods for Smooth Games ICML 2020 h-detach: Modifying the LSTM Gradient Towards Better Optimization ICLR 2019 Manifold Mixup: Better Representations by Interpolating Hidden States ICML 2019 Multi-objective training of Generative Adversarial Networks with multiple discriminators ICML 2019 Negative Momentum for Improved Game Dynamics AISTATS 2019 Reducing the variance in online optimization by transporting past gradients NeurIPS 2019 State-Reification Networks: Improving Generalization by Modeling the Distribution of Hidden Representations ICML 2019 Accelerated Stochastic Power Iteration AISTATS 2018 Learning Representations and Generative Models for 3D Point Clouds ICML 2018 Improving Gibbs Sampler Scan Quality with DoGS ICML 2017 Scan Order in Gibbs Sampling: Models in Which it Matters and Bounds on How Much NeurIPS 2016 Finding Dense Subgraphs via Low-Rank Bilinear Optimization ICML 2014 Memory Limited, Streaming PCA NeurIPS 2013