Vahid Tarokh

37 papers · 2017–2025 · 7 conferences · across top CS/AI conferences

Achievements

+11 more ↓

🗺️ Taxonomy Completionist (12) 🧭 Keyword Pioneer 🌈 Renaissance Researcher (5) 🌉 Interdisciplinary Bridge 🌍 Conference Polyglot (7)

🏃 Academic Marathon (8) 🗺️ Taxonomy Completionist (12) 🧭 Keyword Pioneer 🧬 Topic Evolution 🤝 Dynamic Duo (12) 👑 Triple Crown 🗃️ Keyword Collector (103) ⚡ Prolific Year (6) 📈 Trend Setter 💎 Century Club (37) 🔥 Unstoppable (9)

Conferences

UAI (10) ICLR (8) NIPS (8) AISTATS (6) ICML (3) IJCAI (1) JMLR (1)

Top co-authors

Ali Hasan (12) Jie Ding (11) Juncheng Dong (9) Yuting Ng (7) Enmao Diao (6) Ahmed Aloui (4) Khalil Elkhalil (4) Haoming Yang (3) Yi Zhou (3) Yingbin Liang (3)

Research topics

Probability (1)

Keywords

fisher divergence (4) generative model (3) neural network architecture (2) nonconvex optimization (2) kullback-leibler divergence (2) federated learning (2) multivariate distribution (2) momentum method (2) diffusion process (2) semi-supervised learning (1) change detection (1) transfer learning (1) knowledge distillation (1) convergence analysis (1) representation learning (1) probabilistic modeling (1) empirical risk minimization (1) causal inference (1) distributed optimization (1) decentralized optimization (1)

Papers

Steinmetz Neural Networks for Complex-Valued Data AISTATS 2025 Variational Adversarial Training Towards Policies with Improved Robustness AISTATS 2025 Conditional Average Treatment Effect Estimation Under Hidden Confounders UAI 2025 Elliptic Loss Regularization ICLR 2025 In-Context Reinforcement Learning From Suboptimal Historical Data ICML 2025 Parabolic Continual Learning AISTATS 2025 Decoding Rewards in Competitive Games: Inverse Game Theory with Entropy Regularization ICML 2025 CATE Estimation With Potential Outcome Imputation From Local Regression UAI 2025 Distributionally Robust Optimization as a Scalable Framework to Characterize Extreme Value Distributions UAI 2024 Base Models for Parabolic Partial Differential Equations UAI 2024 Neural McKean-Vlasov Processes: Distributional Dependence in Diffusion Processes AISTATS 2024 Random Linear Projections Loss for Hyperplane-Based Optimization in Neural Networks UAI 2024 Pruning Deep Neural Networks from a Sparsity Perspective ICLR 2023 PASTA: Pessimistic Assortment Optimization ICML 2023 Off-Policy Evaluation for Human Feedback NIPS 2023 Score-based Quickest Change Detection for Unnormalized Models AISTATS 2023 Robust Quickest Change Detection for Unnormalized Models UAI 2023 Inference and sampling of point processes from diffusion excursions UAI 2023 Transfer learning for individual treatment effect estimation UAI 2023 Characteristic Neural Ordinary Differential Equation ICLR 2023 Task Affinity with Maximum Bipartite Matching in Few-Shot Learning ICLR 2022 GAL: Gradient Assisted Learning for Decentralized Multi-Organization Collaborations NIPS 2022 Inference and Sampling for Archimax Copulas NIPS 2022 SemiFL: Semi-Supervised Federated Learning for Unlabeled Clients with Alternate Training NIPS 2022 Blaschke Product Neural Networks (BPNN): A Physics-Infused Neural Network for Phase Retrieval of Meromorphic Functions ICLR 2022 Modeling extremes with $d$-max-decreasing neural networks UAI 2022 Model Linkage Selection for Cooperative Learning JMLR 2021 Generative Archimedean copulas UAI 2021 Projected Latent Markov Chain Monte Carlo: Conditional Sampling of Normalizing Flows ICLR 2021 HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients ICLR 2021 Fisher Auto-Encoders AISTATS 2021 Proximal Gradient Algorithm with Momentum and Flexible Parameter Restart for Nonconvex Optimization IJCAI 2020 SpiderBoost and Momentum: Faster Variance Reduction Algorithms NIPS 2019 SGD Converges to Global Minimum in Deep Learning via Star-convex Path ICLR 2019 Gradient Information for Representation and Modeling NIPS 2019 Learning Bounds for Greedy Approximation with Explicit Feature Maps from Multiple Kernels NIPS 2018 On Optimal Generalizability in Parametric Learning NIPS 2017