Akash Srivastava

According to our database, Akash Srivastava authored at least 56 papers between 2010 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

DICE: Discrete Inversion Enabling Controllable Editing for Multinomial Diffusion and Masked Generative Models.

Xiaoxiao He, Ligong Han, Quan Dao, Song Wen, Minhao Bai, Di Liu, Han Zhang, Martin Renqiang Min, Felix Juefei-Xu, Chaowei Tan, Bo Liu, Kang Li, Hongdong Li, Junzhou Huang, Faez Ahmed, Akash Srivastava, Dimitris N. Metaxas

CoRR, 2024

Urban context and delivery performance: Modelling service time for cargo bikes and vans across diverse urban environments.

Maxwell Schrader, Navish Kumar, Esben Sørig, Soonmyeong Yoon, Akash Srivastava, Kai Xu, Maria Sinziana Astefanoaei, Nicolas Collignon

CoRR, 2024

Spectrum-Aware Parameter Efficient Fine-Tuning for Diffusion Models.

Xinxi Zhang, Song Wen, Ligong Han, Felix Juefei-Xu, Akash Srivastava, Junzhou Huang, Hao Wang, Molei Tao, Dimitris N. Metaxas

CoRR, 2024

LInK: Learning Joint Representations of Design and Performance Spaces through Contrastive Learning for Mechanism Synthesis.

Amin Heyrani Nobari, Akash Srivastava, Dan Gutfreund, Kai Xu, Faez Ahmed

CoRR, 2024

Adapting Differentially Private Synthetic Data to Relational Databases.

Kaveh Alimohammadi, Hao Wang, Ojas Gulati, Akash Srivastava, Navid Azizan

CoRR, 2024

Value Augmented Sampling for Language Model Alignment and Personalization.

Seungwook Han, Idan Shenfeld, Akash Srivastava, Yoon Kim, Pulkit Agrawal

CoRR, 2024

LAB: Large-Scale Alignment for ChatBots.

Shivchander Sudalairaj, Abhishek Bhandwaldar, Aldo Pareja, Kai Xu, David D. Cox, Akash Srivastava

CoRR, 2024

Learning to Deliver: a Foundation Model for the Montreal Capacitated Vehicle Routing Problem.

Samuel J. K. Chin, Matthias Winkenbach, Akash Srivastava

CoRR, 2024

ProxEdit: Improving Tuning-Free Real Image Editing with Proximal Guidance.

Ligong Han, Song Wen, Qi Chen, Zhixing Zhang, Kunpeng Song, Mengwei Ren, Ruijiang Gao, Anastasis Stathopoulos, Xiaoxiao He, Yuxiao Chen, Di Liu, Qilong Zhangli, Jindong Jiang, Zhaoyang Xia, Akash Srivastava, Dimitris N. Metaxas

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2024

A Probabilistic Framework for Modular Continual Learning.

Lazar Valkov, Akash Srivastava, Swarat Chaudhuri, Charles Sutton

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Curiosity-driven Red-teaming for Large Language Models.

Zhang-Wei Hong, Idan Shenfeld, Tsun-Hsuan Wang, Yung-Sung Chuang, Aldo Pareja, James R. Glass, Akash Srivastava, Pulkit Agrawal

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design.

Lyle Regenwetter, Akash Srivastava, Dan Gutfreund, Faez Ahmed

Comput. Aided Des., December, 2023

Estimating the Density Ratio between Distributions with High Discrepancy using Multinomial Logistic Regression.

Akash Srivastava, Seungwook Han, Kai Xu, Benjamin Rhodes, Michael U. Gutmann

Trans. Mach. Learn. Res., 2023

Mitigating Confirmation Bias in Semi-supervised Learning via Efficient Bayesian Model Averaging.

Charlotte Loh, Rumen Dangovski, Shivchander Sudalairaj, Seungwook Han, Ligong Han, Leonid Karlinsky, Marin Soljacic, Akash Srivastava

Trans. Mach. Learn. Res., 2023

Private Synthetic Data Meets Ensemble Learning.

Haoyuan Sun, Navid Azizan, Akash Srivastava, Hao Wang

CoRR, 2023

Learning from Invalid Data: On Constraint Satisfaction in Generative Models.

Giorgio Giannone, Lyle Regenwetter, Akash Srivastava, Dan Gutfreund, Faez Ahmed

CoRR, 2023

Improving Tuning-Free Real Image Editing with Proximal Guidance.

Ligong Han, Song Wen, Qi Chen, Zhixing Zhang, Kunpeng Song, Mengwei Ren, Ruijiang Gao, Yuxiao Chen, Di Liu, Qilong Zhangli, Anastasis Stathopoulos, Jindong Jiang, Zhaoyang Xia, Akash Srivastava, Dimitris N. Metaxas

CoRR, 2023

Constructive Assimilation: Boosting Contrastive Learning Performance through View Generation Strategies.

Ligong Han, Seungwook Han, Shivchander Sudalairaj, Charlotte Loh, Rumen Dangovski, Fei Deng, Pulkit Agrawal, Dimitris N. Metaxas, Leonid Karlinsky, Tsui-Wei Weng, Akash Srivastava

CoRR, 2023

Modelling the performance of delivery vehicles across urban micro-regions to accelerate the transition to cargo-bike logistics.

Max Schrader, Navish Kumar, Nicolas Collignon, Esben Sørig, Soonmyeong Yoon, Akash Srivastava, Kai Xu, Maria Sinziana Astefanoaei

CoRR, 2023

Identifiability Guarantees for Causal Disentanglement from Soft Interventions.

Jiaqi Zhang, Kristjan H. Greenewald, Chandler Squires, Akash Srivastava, Karthikeyan Shanmugam, Caroline Uhler

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Post-processing Private Synthetic Data for Improving Utility on Selected Measures.

Hao Wang, Shivchander Sudalairaj, John Henning, Kristjan H. Greenewald, Akash Srivastava

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Towards robust and generalizable representations of extracellular data using contrastive learning.

Ankit Vishnubhotla, Charlotte Loh, Akash Srivastava, Liam Paninski, Cole L. Hurwitz

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Beyond Uniform Sampling: Offline Reinforcement Learning with Imbalanced Datasets.

Zhang-Wei Hong, Aviral Kumar, Sathwik Karnik, Abhishek Bhandwaldar, Akash Srivastava, Joni Pajarinen, Romain Laroche, Abhishek Gupta, Pulkit Agrawal

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Aligning Optimization Trajectories with Diffusion Models for Constrained Design Generation.

Giorgio Giannone, Akash Srivastava, Ole Winther, Faez Ahmed

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Analyzing Generalization of Neural Networks through Loss Path Kernels.

Yilan Chen, Wei Huang, Hao Wang, Charlotte Loh, Akash Srivastava, Lam M. Nguyen, Lily Weng

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Compositional Foundation Models for Hierarchical Planning.

Anurag Ajay, Seungwook Han, Yilun Du, Shuang Li, Abhi Gupta, Tommi S. Jaakkola, Joshua B. Tenenbaum, Leslie Pack Kaelbling, Akash Srivastava, Pulkit Agrawal

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Multi-Symmetry Ensembles: Improving Diversity and Generalization via Opposing Symmetries.

Charlotte Loh, Seungwook Han, Shivchander Sudalairaj, Rumen Dangovski, Kai Xu, Florian Wenzel, Marin Soljacic, Akash Srivastava

Proceedings of the International Conference on Machine Learning, 2023

2022

On the Importance of Calibration in Semi-supervised Learning.

Charlotte Loh, Rumen Dangovski, Shivchander Sudalairaj, Seungwook Han, Ligong Han, Leonid Karlinsky, Marin Soljacic, Akash Srivastava

CoRR, 2022

LINKS: A dataset of a hundred million planar linkage mechanisms for data-driven kinematic design.

Amin Heyrani Nobari, Akash Srivastava, Dan Gutfreund, Faez Ahmed

CoRR, 2022

Equivariant Self-Supervised Learning: Encouraging Equivariance in Representations.

Rumen Dangovski, Li Jing, Charlotte Loh, Seungwook Han, Akash Srivastava, Brian Cheung, Pulkit Agrawal, Marin Soljacic

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Equivariant Contrastive Learning.

Rumen Dangovski, Li Jing, Charlotte Loh, Seungwook Han, Akash Srivastava, Brian Cheung, Pulkit Agrawal, Marin Soljacic

CoRR, 2021

Security Assessment Rating Framework for Enterprises using MITRE ATT&CK Matrix.

Hardik Manocha, Akash Srivastava, Chetan Verma, Ratan Gupta, Bhavya Bansal

CoRR, 2021

A Bayesian-Symbolic Approach to Reasoning and Learning in Intuitive Physics.

Kai Xu, Akash Srivastava, Dan Gutfreund, Felix Sosa, Tomer D. Ullman, Josh Tenenbaum, Charles Sutton

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Targeted Neural Dynamical Modeling.

Cole L. Hurwitz, Akash Srivastava, Kai Xu, Justin Jude, Matthew G. Perich, Lee E. Miller, Matthias H. Hennig

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Sequential Transfer Machine Learning in Networks: Measuring the Impact of Data and Neural Net Similarity on Transferability.

Robin Hirt, Akash Srivastava, Carlos Berg, Niklas Kühl

Proceedings of the 54th Hawaii International Conference on System Sciences, 2021

2020

Tissue-specific Gene Expression Changes Are Associated with Aging in Mice.

Akash Srivastava, Emanuel Barth, Maria A. Ermolaeva, Madlen Guenther, Christiane Frahm, Manja Marz, Otto W. Witte

Genom. Proteom. Bioinform., 2020

Improving the Reconstruction of Disentangled Representation Learners via Multi-Stage Modelling.

Akash Srivastava, Yamini Bansal, Yukun Ding, Cole L. Hurwitz, Kai Xu, Bernhard Egger, Prasanna Sattigeri, Josh Tenenbaum, David D. Cox, Dan Gutfreund

CoRR, 2020

not-so-BigGAN: Generating High-Fidelity Images on a Small Compute Budget.

Seungwook Han, Akash Srivastava, Cole L. Hurwitz, Prasanna Sattigeri, David D. Cox

CoRR, 2020

Generative Ratio Matching Networks.

Akash Srivastava, Kai Xu, Michael U. Gutmann, Charles Sutton

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Deep generative modelling for amortised variational inference.

Akash Srivastava

PhD thesis, 2019

SimVAE: Simulator-Assisted Training forInterpretable Generative Models.

Akash Srivastava, Jessie C. Rosenberg, Dan Gutfreund, David D. Cox

CoRR, 2019

BreGMN: scaled-Bregman Generative Modeling Networks.

Akash Srivastava, Kristjan H. Greenewald, Farzaneh Mirzazadeh

CoRR, 2019

A deadlock-free lock-based synchronization for GPUs.

Anshu S. Anand, Akash Srivastava, R. K. Shyamasundar

Concurr. Comput. Pract. Exp., 2019

Scalable Spike Source Localization in Extracellular Recordings using Amortized Variational Inference.

Cole L. Hurwitz, Kai Xu, Akash Srivastava, Alessio Paolo Buccino, Matthias H. Hennig

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Variational Russian Roulette for Deep Bayesian Nonparametrics.

Kai Xu, Akash Srivastava, Charles Sutton

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Ratio Matching MMD Nets: Low dimensional projections for effective deep generative models.

Akash Srivastava, Kai Xu, Michael U. Gutmann, Charles Sutton

CoRR, 2018

Variational Inference In Pachinko Allocation Machines.

Akash Srivastava, Charles Sutton

CoRR, 2018

Synthesis of Differentiable Functional Programs for Lifelong Learning.

Lazar Valkov, Dipak Chaudhari, Akash Srivastava, Charles Sutton, Swarat Chaudhuri

CoRR, 2018

HOUDINI: Lifelong Learning as Program Synthesis.

Lazar Valkov, Dipak Chaudhari, Akash Srivastava, Charles Sutton, Swarat Chaudhuri

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam.

Mohammad Emtiyaz Khan, Didrik Nielsen, Voot Tangkaratt, Wu Lin, Yarin Gal, Akash Srivastava

Proceedings of the 35th International Conference on Machine Learning, 2018

2017

Effect of Laser Modulation on Dispersion Induced Chirp Microwave Signal Generation by Using Temporal Pulse Shaping Technique.

Sanjeev Kumar Raghuwanshi, Nimish Kumar Srivastava, Akash Srivastava, Bidhanshel S. Athokpam

Wirel. Pers. Commun., 2017

VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning.

Akash Srivastava, Lazar Valkov, Chris Russell, Michael U. Gutmann, Charles Sutton

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Autoencoding Variational Inference For Topic Models.

Akash Srivastava, Charles Sutton

Proceedings of the 5th International Conference on Learning Representations, 2017

2016

Clustering with a Reject Option: Interactive Clustering as Bayesian Prior Elicitation.

Akash Srivastava, James Y. Zou, Ryan P. Adams, Charles Sutton

CoRR, 2016

2015

Performance Comparison of Various Particle Swarm Optimizers in DWT-SVD watermarking for RGB Images.

Akash Srivastava

Proceedings of the Sixth International Conference on Computer and Communication Technology 2015, 2015

2010

Ingenuity in pattern recognition: a novel bioinformatics approach towards lung cancer identification.

Jayant Mishra, Amit Kumar, Amita Sinha, Silpa Das, Akash Srivastava

Int. J. Bioinform. Res. Appl., 2010