Taming extreme heterogeneity via machine learning based design of autonomous manycore systems. - Paul Bogdan; Fan Chen 001; Aryan Deshwal; Janardhan Rao Doppa; Biresh Kumar Joardar; Hai (Helen) Li; Shahin Nazarian; Linghao Song; Yao Xiao (CODES+ISSS, 2019)

Randomized Greedy Search for Structured Prediction: Amortized Inference and Learning. - Chao Ma 001; F. A. Rezaur Rahman Chowdhury; Aryan Deshwal; Md. Rakibul Islam 001; Janardhan Rao Doppa; Dan Roth (IJCAI, 2019)

Learning and Inference for Structured Prediction: A Unifying Perspective. - Aryan Deshwal; Janardhan Rao Doppa; Dan Roth (IJCAI, 2019)

Max-value Entropy Search for Multi-Objective Bayesian Optimization. - Syrine Belakaria; Aryan Deshwal; Janardhan Rao Doppa (NeurIPS, 2019)

MOOS: A Multi-Objective Design Space Exploration and Optimization Framework for NoC Enabled Manycore Systems. - Aryan Deshwal; Nitthilan Kannappan Jayakodi; Biresh Kumar Joardar; Janardhan Rao Doppa; Partha Pratim Pande (ACM Trans. Embed. Comput. Syst., 2019)

Multi-Fidelity Multi-Objective Bayesian Optimization: An Output Space Entropy Search Approach. - Syrine Belakaria; Aryan Deshwal; Janardhan Rao Doppa (AAAI, 2020)

Uncertainty-Aware Search Framework for Multi-Objective Bayesian Optimization. - Syrine Belakaria; Aryan Deshwal; Nitthilan Kannappan Jayakodi; Janardhan Rao Doppa (AAAI, 2020)

Optimizing Discrete Spaces via Expensive Evaluations: A Learning to Search Framework. - Aryan Deshwal; Syrine Belakaria; Janardhan Rao Doppa; Alan Fern (AAAI, 2020)

Design of Multi-Output Switched-Capacitor Voltage Regulator via Machine Learning. - Zhiyuan Zhou; Syrine Belakaria; Aryan Deshwal; Wookpyo Hong; Janardhan Rao Doppa; Partha Pratim Pande; Deukhyoun Heo (DATE, 2020)

Uncertainty aware Search Framework for Multi-Objective Bayesian Optimization with Constraints. - Syrine Belakaria; Aryan Deshwal; Janardhan Rao Doppa (CoRR, 2020)

Scalable Combinatorial Bayesian Optimization with Tractable Statistical models. - Aryan Deshwal; Syrine Belakaria; Janardhan Rao Doppa (CoRR, 2020)

Max-value Entropy Search for Multi-Objective Bayesian Optimization with Constraints. - Syrine Belakaria; Aryan Deshwal; Janardhan Rao Doppa (CoRR, 2020)

Information-Theoretic Multi-Objective Bayesian Optimization with Continuous Approximations. - Syrine Belakaria; Aryan Deshwal; Janardhan Rao Doppa (CoRR, 2020)

Design and Optimization of Energy-Accuracy Tradeoff Networks for Mobile Platforms via Pretrained Deep Models. - Nitthilan Kanappan Jayakodi; Syrine Belakaria; Aryan Deshwal; Janardhan Rao Doppa (ACM Trans. Embed. Comput. Syst., 2020)

Mercer Features for Efficient Combinatorial Bayesian Optimization. - Aryan Deshwal; Syrine Belakaria; Janardhan Rao Doppa (AAAI, 2021)

Learning Pareto-Frontier Resource Management Policies for Heterogeneous SoCs: An Information-Theoretic Approach. - Aryan Deshwal; Syrine Belakaria; Ganapati Bhat; Janardhan Rao Doppa; Partha Pratim Pande (DAC, 2021)

Bayesian Optimization over Hybrid Spaces. - Aryan Deshwal; Syrine Belakaria; Janardhan Rao Doppa (ICML, 2021)

Combining Latent Space and Structured Kernels for Bayesian Optimization over Combinatorial Spaces. - Aryan Deshwal; Janardhan Rao Doppa (NeurIPS, 2021)

Output Space Entropy Search Framework for Multi-Objective Bayesian Optimization. - Syrine Belakaria; Aryan Deshwal; Janardhan Rao Doppa (J. Artif. Intell. Res., 2021)

Bayesian Optimization over Permutation Spaces. - Aryan Deshwal; Syrine Belakaria; Janardhan Rao Doppa; Dae Hyun Kim 004 (AAAI, 2022)

High-Throughput Training of Deep CNNs on ReRAM-Based Heterogeneous Architectures via Optimized Normalization Layers. - Biresh Kumar Joardar; Aryan Deshwal; Janardhan Rao Doppa; Partha Pratim Pande; Krishnendu Chakrabarty (IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 2022)

Bayesian Optimization over High-Dimensional Combinatorial Spaces via Dictionary-based Embeddings. - Aryan Deshwal; Sebastian Ament; Maximilian Balandat; Eytan Bakshy; Janardhan Rao Doppa; David Eriksson (AISTATS, 2023)

GAUCHE: A Library for Gaussian Processes in Chemistry. - Ryan-Rhys Griffiths; Leo Klarner; Henry B. Moss; Aditya Ravuri; Sang Truong; Yuanqi Du; Samuel Stanton; Gary Tom; Bojana Rankovic; Arian Rokkum Jamasb; Aryan Deshwal; Julius Schwartz; et al. (NeurIPS, 2023)

Dynamic Power Management in Large Manycore Systems: A Learning-to-Search Framework. - Gaurav Narang; Aryan Deshwal; Raid Ayoub; Michael Kishinevsky; Janardhan Rao Doppa; Partha Pratim Pande (ACM Trans. Design Autom. Electr. Syst., 2023)

Offline Model-Based Optimization via Policy-Guided Gradient Search. - Yassine Chemingui; Aryan Deshwal; Trong Nghia Hoang; Janardhan Rao Doppa (AAAI, 2024)

Learning Surrogates for Offline Black-Box Optimization via Gradient Matching. - Minh Hoang; Azza Fadhel; Aryan Deshwal; Jana Doppa; Trong Nghia Hoang (ICML, 2024)

Streamflow Prediction with Uncertainty Quantification for Water Management: A Constrained Reasoning and Learning Approach. - Mohammed Amine Gharsallaoui; Bhupinderjeet Singh; Supriya Savalkar; Aryan Deshwal; Ananth Kalyanaraman; Kirti Rajagopalan; Janardhan Rao Doppa (IJCAI, 2024)

Streamflow Prediction with Uncertainty Quantification for Water Management: A Constrained Reasoning and Learning Approach. - Mohammed Amine Gharsallaoui; Bhupinderjeet Singh; Supriya Savalkar; Aryan Deshwal; Yan Yan 006; Ananth Kalyanaraman; Kirti Rajagopalan; Janardhan Rao Doppa (CoRR, 2024)

Sample-Efficient Bayesian Optimization with Transfer Learning for Heterogeneous Search Spaces. - Aryan Deshwal; Sait Cakmak; Yuhou Xia; David Eriksson (CoRR, 2024)

Constraint-Adaptive Policy Switching for Offline Safe Reinforcement Learning. - Yassine Chemingui; Aryan Deshwal; Honghao Wei; Alan Fern; Jana Doppa (AAAI, 2025)

Adaptive Experimental Design to Accelerate Scientific Discovery and Engineering Design. - Aryan Deshwal (AAAI, 2025)