Aryan Deshwal

Latest

• Learning Surrogates for Offline Black-Box Optimization via Gradient Matching
• Streamflow Prediction with Uncertainty Quantification for Water Management
• Offline Model-based Black-Box Optimization via Policy-guided Gradient Search
• Multi-fidelity Bayesian Optimization of Covalent Organic Frameworks for Xenon/Krypton Separations
• Bayesian Optimization over High-Dimensional Combinatorial Spaces via Dictionary-based Embeddings
• Dynamic Power Management in Large Manycore Systems - A Learning-to-Search Framework
• Bayesian Optimization over Permutation Spaces
• Combining Latent Space and Structured Kernels for Bayesian Optimization over Combinatorial Spaces
• Bayesian Optimization of Nanoporous Materials
• Output Space Entropy Search Framework for Multi-Objective Bayesian Optimization
• Bayesian Optimization over Hybrid Spaces
• High-Throughput Training of Deep CNNs on ReRAM-based Heterogeneous Architectures via Optimized Normalization Layers
• Learning Pareto-Frontier Resource Management Policies for Heterogeneous SoCs - An Information-Theoretic Approach
• Mercer Features for Efficient Combinatorial Bayesian Optimization
• Information-Theoretic Multi-Objective Bayesian Optimization with Continuous Approximations
• Max-value Entropy Search for Multi-Objective Bayesian Optimization with Constraints
• Scalable Combinatorial Bayesian Optimization with Tractable Statistical models
• Optimizing Discrete Spaces via Expensive Evaluations: A Learning to Search Framework
• Uncertainty-Aware Search Framework for Multi-Objective Bayesian Optimization
• Design and Optimization of Energy-Accuracy Tradeoff Networks for Mobile Platforms via Pretrained Deep Models
• Design of Multi-Output Switched-Capacitor Voltage Regulator via Machine Learning
• Multi-Fidelity Multi-Objective Bayesian Optimization, An Output Space Entropy Search Approach
• Max-value Entropy Search for Multi-Objective Bayesian Optimization
• MOOS: A multi-objective design space exploration and optimization framework for NoC enabled manycore systems
• Learning and inference for structured prediction: a unifying perspective
• Randomized greedy search for structured prediction: amortized inference and learning
• Taming extreme heterogeneity via machine learning based design of autonomous manycore systems