Akash Srivastava

89 papers A* 19A 2Journal 62Unranked 5

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Agent Factories for High Level Synthesis: How Far Can General-Purpose Coding Agents Go in Hardware Optimization? CoRR Abhishek Bhandwaldar, Mihir R. Choudhury, Ruchir Puri, Akash Srivastava
2026	J	`jnl`	GIFT: Bootstrapping Image-to-CAD Program Synthesis via Geometric Feedback. CoRR Giorgio Giannone, Anna C. Doris, Amin Heyrani Nobari, Kai Xu, Akash Srivastava, Faez Ahmed
2026	J	`jnl`	S2D2: Fast Decoding for Diffusion LLMs via Training-Free Self-Speculation. CoRR Ligong Han, Hao Wang, Han Gao, Kai Xu, Akash Srivastava
2026	J	`jnl`	T3D: Few-Step Diffusion Language Models via Trajectory Self-Distillation with Direct Discriminative Optimization. CoRR Tunyu Zhang, Xinxi Zhang, Ligong Han, Haizhou Shi, Xiaoxiao He, Zhuowei Li, Hao Wang, Kai Xu, Akash Srivastava, Hao Wang, Vladimir Pavlovic, Dimitris N. Metaxas
2025	J	`jnl`	A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods. CoRR Isha Puri, Shivchander Sudalairaj, Guangxuan Xu, Kai Xu, Akash Srivastava
2025	J	`jnl`	Activation-Informed Merging of Large Language Models. CoRR Amin Heyrani Nobari, Kaveh Alimohammadi, Ali ArjomandBigdeli, Akash Srivastava, Faez Ahmed, Navid Azizan
2025	—	`conf`	Hopscotch: Discovering and Skipping Redundancies in Language Models. EMNLP (Findings) Mustafa Eyceoz, Nikhil Shivakumar Nayak, Hao Wang, Ligong Han, Akash Srivastava
2025	J	`jnl`	Hopscotch: Discovering and Skipping Redundancies in Language Models. CoRR Mustafa Eyceoz, Nikhil Shivakumar Nayak, Hao Wang, Ligong Han, Akash Srivastava
2025	J	`jnl`	Inference-Time Scaling of Diffusion Language Models with Particle Gibbs Sampling. CoRR Meihua Dang, Jiaqi Han, Minkai Xu, Kai Xu, Akash Srivastava, Stefano Ermon
2025	J	`jnl`	Mitigating Premature Exploitation in Particle-based Monte Carlo for Inference-Time Scaling. CoRR Giorgio Giannone, Guangxuan Xu, Nikhil Shivakumar Nayak, Rohan Mahesh Awhad, Shivchander Sudalairaj, Kai Xu, Akash Srivastava
2025	A	`conf`	SODA: Spectral Orthogonal Decomposition Adaptation for Diffusion Models. WACV Xinxi Zhang, Song Wen, Ligong Han, Felix Juefei-Xu, Akash Srivastava, Junzhou Huang, Vladimir Pavlovic, Hao Wang, Molei Tao, Dimitris N. Metaxas
2025	J	`jnl`	SQuat: Subspace-orthogonal KV Cache Quantization. CoRR Hao Wang, Ligong Han, Kai Xu, Akash Srivastava
2025	J	`jnl`	Sculpting Subspaces: Constrained Full Fine-Tuning in LLMs for Continual Learning. CoRR Nikhil Shivakumar Nayak, Krishnateja Killamsetty, Ligong Han, Abhishek Bhandwaldar, Prateek Chanda, Kai Xu, Hao Wang, Aldo Pareja, Oleg Silkin, Mustafa Eyceoz, Akash Srivastava
2025	A*	`conf`	Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs. ICLR Aldo Pareja, Nikhil Shivakumar Nayak, Hao Wang, Krishnateja Killamsetty, Shivchander Sudalairaj, Wenlong Zhao, Seungwook Han, Abhishek Bhandwaldar, Guangxuan Xu, Kai Xu, Ligong Han, Luke Inglis, Akash Srivastava
2024	A*	`conf`	A Probabilistic Framework for Modular Continual Learning. ICLR Lazar Valkov, Akash Srivastava, Swarat Chaudhuri, Charles Sutton
2024	J	`jnl`	Adapting Differentially Private Synthetic Data to Relational Databases. CoRR Kaveh Alimohammadi, Hao Wang, Ojas Gulati, Akash Srivastava, Navid Azizan
2024	J	`jnl`	CDR: Customizable Density Ratios of Strong-over-weak LLMs for Preference Annotation. CoRR Guangxuan Xu, Kai Xu, Shivchander Sudalairaj, Hao Wang, Akash Srivastava
2024	J	`jnl`	Constraining Generative Models for Engineering Design with Negative Data. Trans. Mach. Learn. Res. Lyle Regenwetter, Giorgio Giannone, Akash Srivastava, Dan Gutfreund, Faez Ahmed
2024	A*	`conf`	Curiosity-driven Red-teaming for Large Language Models. ICLR Zhang-Wei Hong, Idan Shenfeld, Tsun-Hsuan Wang, Yung-Sung Chuang, Aldo Pareja, James R. Glass, Akash Srivastava, Pulkit Agrawal
2024	J	`jnl`	Curiosity-driven Red-teaming for Large Language Models. CoRR Zhang-Wei Hong, Idan Shenfeld, Tsun-Hsuan Wang, Yung-Sung Chuang, Aldo Pareja, James R. Glass, Akash Srivastava, Pulkit Agrawal
2024	J	`jnl`	DICE: Discrete Inversion Enabling Controllable Editing for Multinomial Diffusion and Masked Generative Models. CoRR 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
2024	J	`jnl`	Generative Optimization: A Perspective on AI-Enhanced Problem Solving in Engineering. CoRR Cyril Picard, Lyle Regenwetter, Amin Heyrani Nobari, Akash Srivastava, Faez Ahmed
2024	J	`jnl`	LAB: Large-Scale Alignment for ChatBots. CoRR Shivchander Sudalairaj, Abhishek Bhandwaldar, Aldo Pareja, Kai Xu, David D. Cox, Akash Srivastava
2024	J	`jnl`	LInK: Learning Joint Representations of Design and Performance Spaces through Contrastive Learning for Mechanism Synthesis. CoRR Amin Heyrani Nobari, Akash Srivastava, Dan Gutfreund, Kai Xu, Faez Ahmed
2024	J	`jnl`	LInK: Learning Joint Representations of Design and Performance Spaces through Contrastive Learning for Mechanism Synthesis. Trans. Mach. Learn. Res. Amin Heyrani Nobari, Akash Srivastava, Dan Gutfreund, Kai Xu, Faez Ahmed
2024	J	`jnl`	Learning to Deliver: a Foundation Model for the Montreal Capacitated Vehicle Routing Problem. CoRR Samuel J. K. Chin, Matthias Winkenbach, Akash Srivastava
2024	A	`conf`	ProxEdit: Improving Tuning-Free Real Image Editing with Proximal Guidance. WACV 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
2024	J	`jnl`	Spectrum-Aware Parameter Efficient Fine-Tuning for Diffusion Models. CoRR Xinxi Zhang, Song Wen, Ligong Han, Felix Juefei-Xu, Akash Srivastava, Junzhou Huang, Hao Wang, Molei Tao, Dimitris N. Metaxas
2024	J	`jnl`	Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs. CoRR Aldo Pareja, Nikhil Shivakumar Nayak, Hao Wang, Krishnateja Killamsetty, Shivchander Sudalairaj, Wenlong Zhao, Seungwook Han, Abhishek Bhandwaldar, Guangxuan Xu, Kai Xu, Ligong Han, Luke Inglis, Akash Srivastava
2024	J	`jnl`	Urban context and delivery performance: Modelling service time for cargo bikes and vans across diverse urban environments. CoRR Maxwell Schrader, Navish Kumar, Esben Sørig, Soonmyeong Yoon, Akash Srivastava, Kai Xu, Maria Sinziana Astefanoaei, Nicolas Collignon
2024	J	`jnl`	Value Augmented Sampling for Language Model Alignment and Personalization. CoRR Seungwook Han, Idan Shenfeld, Akash Srivastava, Yoon Kim, Pulkit Agrawal
2023	J	`jnl`	A Probabilistic Framework for Modular Continual Learning. CoRR Lazar Valkov, Akash Srivastava, Swarat Chaudhuri, Charles Sutton
2023	A*	`conf`	Aligning Optimization Trajectories with Diffusion Models for Constrained Design Generation. NeurIPS Giorgio Giannone, Akash Srivastava, Ole Winther, Faez Ahmed
2023	J	`jnl`	Aligning Optimization Trajectories with Diffusion Models for Constrained Design Generation. CoRR Giorgio Giannone, Akash Srivastava, Ole Winther, Faez Ahmed
2023	A*	`conf`	Analyzing Generalization of Neural Networks through Loss Path Kernels. NeurIPS Yilan Chen, Wei Huang, Hao Wang, Charlotte Loh, Akash Srivastava, Lam M. Nguyen, Lily Weng
2023	J	`jnl`	Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design. Comput. Aided Des. Lyle Regenwetter, Akash Srivastava, Dan Gutfreund, Faez Ahmed
2023	J	`jnl`	Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design. CoRR Lyle Regenwetter, Akash Srivastava, Dan Gutfreund, Faez Ahmed
2023	A*	`conf`	Beyond Uniform Sampling: Offline Reinforcement Learning with Imbalanced Datasets. NeurIPS Zhang-Wei Hong, Aviral Kumar, Sathwik Karnik, Abhishek Bhandwaldar, Akash Srivastava, Joni Pajarinen, Romain Laroche, Abhishek Gupta, Pulkit Agrawal
2023	J	`jnl`	Beyond Uniform Sampling: Offline Reinforcement Learning with Imbalanced Datasets. CoRR Zhang-Wei Hong, Aviral Kumar, Sathwik Karnik, Abhishek Bhandwaldar, Akash Srivastava, Joni Pajarinen, Romain Laroche, Abhishek Gupta, Pulkit Agrawal
2023	A*	`conf`	Compositional Foundation Models for Hierarchical Planning. NeurIPS Anurag Ajay, Seungwook Han, Yilun Du, Shuang Li, Abhi Gupta, Tommi S. Jaakkola, Joshua B. Tenenbaum, Leslie Pack Kaelbling, Akash Srivastava, Pulkit Agrawal
2023	J	`jnl`	Compositional Foundation Models for Hierarchical Planning. CoRR Anurag Ajay, Seungwook Han, Yilun Du, Shuang Li, Abhi Gupta, Tommi S. Jaakkola, Josh Tenenbaum, Leslie Pack Kaelbling, Akash Srivastava, Pulkit Agrawal
2023	J	`jnl`	Constructive Assimilation: Boosting Contrastive Learning Performance through View Generation Strategies. CoRR Ligong Han, Seungwook Han, Shivchander Sudalairaj, Charlotte Loh, Rumen Dangovski, Fei Deng, Pulkit Agrawal, Dimitris N. Metaxas, Leonid Karlinsky, Tsui-Wei Weng, Akash Srivastava
2023	J	`jnl`	Estimating the Density Ratio between Distributions with High Discrepancy using Multinomial Logistic Regression. CoRR Akash Srivastava, Seungwook Han, Kai Xu, Benjamin Rhodes, Michael U. Gutmann
2023	J	`jnl`	Estimating the Density Ratio between Distributions with High Discrepancy using Multinomial Logistic Regression. Trans. Mach. Learn. Res. Akash Srivastava, Seungwook Han, Kai Xu, Benjamin Rhodes, Michael U. Gutmann
2023	A*	`conf`	Identifiability Guarantees for Causal Disentanglement from Soft Interventions. NeurIPS Jiaqi Zhang, Kristjan H. Greenewald, Chandler Squires, Akash Srivastava, Karthikeyan Shanmugam, Caroline Uhler
2023	J	`jnl`	Identifiability Guarantees for Causal Disentanglement from Soft Interventions. CoRR Jiaqi Zhang, Chandler Squires, Kristjan H. Greenewald, Akash Srivastava, Karthikeyan Shanmugam, Caroline Uhler
2023	J	`jnl`	Improving Tuning-Free Real Image Editing with Proximal Guidance. CoRR 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
2023	J	`jnl`	Learning from Invalid Data: On Constraint Satisfaction in Generative Models. CoRR Giorgio Giannone, Lyle Regenwetter, Akash Srivastava, Dan Gutfreund, Faez Ahmed
2023	J	`jnl`	Mitigating Confirmation Bias in Semi-supervised Learning via Efficient Bayesian Model Averaging. Trans. Mach. Learn. Res. Charlotte Loh, Rumen Dangovski, Shivchander Sudalairaj, Seungwook Han, Ligong Han, Leonid Karlinsky, Marin Soljacic, Akash Srivastava
2023	J	`jnl`	Modelling the performance of delivery vehicles across urban micro-regions to accelerate the transition to cargo-bike logistics. CoRR Max Schrader, Navish Kumar, Nicolas Collignon, Esben Sørig, Soonmyeong Yoon, Akash Srivastava, Kai Xu, Maria Sinziana Astefanoaei
2023	A*	`conf`	Multi-Symmetry Ensembles: Improving Diversity and Generalization via Opposing Symmetries. ICML Charlotte Loh, Seungwook Han, Shivchander Sudalairaj, Rumen Dangovski, Kai Xu, Florian Wenzel, Marin Soljacic, Akash Srivastava
2023	J	`jnl`	Multi-Symmetry Ensembles: Improving Diversity and Generalization via Opposing Symmetries. CoRR Charlotte Loh, Seungwook Han, Shivchander Sudalairaj, Rumen Dangovski, Kai Xu, Florian Wenzel, Marin Soljacic, Akash Srivastava
2023	A*	`conf`	Post-processing Private Synthetic Data for Improving Utility on Selected Measures. NeurIPS Hao Wang, Shivchander Sudalairaj, John Henning, Kristjan H. Greenewald, Akash Srivastava
2023	J	`jnl`	Post-processing Private Synthetic Data for Improving Utility on Selected Measures. CoRR Hao Wang, Shivchander Sudalairaj, John Henning, Kristjan H. Greenewald, Akash Srivastava
2023	J	`jnl`	Private Synthetic Data Meets Ensemble Learning. CoRR Haoyuan Sun, Navid Azizan, Akash Srivastava, Hao Wang
2023	A*	`conf`	Towards robust and generalizable representations of extracellular data using contrastive learning. NeurIPS Ankit Vishnubhotla, Charlotte Loh, Akash Srivastava, Liam Paninski, Cole L. Hurwitz
2022	A*	`conf`	Equivariant Self-Supervised Learning: Encouraging Equivariance in Representations. ICLR Rumen Dangovski, Li Jing, Charlotte Loh, Seungwook Han, Akash Srivastava, Brian Cheung, Pulkit Agrawal, Marin Soljacic
2022	J	`jnl`	LINKS: A dataset of a hundred million planar linkage mechanisms for data-driven kinematic design. CoRR Amin Heyrani Nobari, Akash Srivastava, Dan Gutfreund, Faez Ahmed
2022	J	`jnl`	On the Importance of Calibration in Semi-supervised Learning. CoRR Charlotte Loh, Rumen Dangovski, Shivchander Sudalairaj, Seungwook Han, Ligong Han, Leonid Karlinsky, Marin Soljacic, Akash Srivastava
2021	A*	`conf`	A Bayesian-Symbolic Approach to Reasoning and Learning in Intuitive Physics. NeurIPS Kai Xu, Akash Srivastava, Dan Gutfreund, Felix Sosa, Tomer D. Ullman, Josh Tenenbaum, Charles Sutton
2021	J	`jnl`	Equivariant Contrastive Learning. CoRR Rumen Dangovski, Li Jing, Charlotte Loh, Seungwook Han, Akash Srivastava, Brian Cheung, Pulkit Agrawal, Marin Soljacic
2021	J	`jnl`	Security Assessment Rating Framework for Enterprises using MITRE ATT&CK Matrix. CoRR Hardik Manocha, Akash Srivastava, Chetan Verma, Ratan Gupta, Bhavya Bansal
2021	—	`conf`	Sequential Transfer Machine Learning in Networks: Measuring the Impact of Data and Neural Net Similarity on Transferability. HICSS Robin Hirt, Akash Srivastava, Carlos Berg, Niklas Kühl
2021	A*	`conf`	Targeted Neural Dynamical Modeling. NeurIPS Cole L. Hurwitz, Akash Srivastava, Kai Xu, Justin Jude, Matthew G. Perich, Lee E. Miller, Matthias H. Hennig
2020	A*	`conf`	Generative Ratio Matching Networks. ICLR Akash Srivastava, Kai Xu, Michael U. Gutmann, Charles Sutton
2020	J	`jnl`	Improving the Reconstruction of Disentangled Representation Learners via Multi-Stage Modelling. CoRR Akash Srivastava, Yamini Bansal, Yukun Ding, Cole L. Hurwitz, Kai Xu, Bernhard Egger, Prasanna Sattigeri, Josh Tenenbaum, David D. Cox, Dan Gutfreund
2020	J	`jnl`	Sequential Transfer Machine Learning in Networks: Measuring the Impact of Data and Neural Net Similarity on Transferability. CoRR Robin Hirt, Akash Srivastava, Carlos Berg, Niklas Kühl
2020	J	`jnl`	Tissue-specific Gene Expression Changes Are Associated with Aging in Mice. Genom. Proteom. Bioinform. Akash Srivastava, Emanuel Barth, Maria A. Ermolaeva, Madlen Guenther, Christiane Frahm, Manja Marz, Otto W. Witte
2020	J	`jnl`	not-so-BigGAN: Generating High-Fidelity Images on a Small Compute Budget. CoRR Seungwook Han, Akash Srivastava, Cole L. Hurwitz, Prasanna Sattigeri, David D. Cox
2019	J	`jnl`	A deadlock-free lock-based synchronization for GPUs. Concurr. Comput. Pract. Exp. Anshu S. Anand, Akash Srivastava, R. K. Shyamasundar
2019	J	`jnl`	BreGMN: scaled-Bregman Generative Modeling Networks. CoRR Akash Srivastava, Kristjan H. Greenewald, Farzaneh Mirzazadeh
2019	—	`—`	Deep generative modelling for amortised variational inference. Akash Srivastava
2019	A*	`conf`	Scalable Spike Source Localization in Extracellular Recordings using Amortized Variational Inference. NeurIPS Cole L. Hurwitz, Kai Xu, Akash Srivastava, Alessio Paolo Buccino, Matthias H. Hennig
2019	J	`jnl`	Scalable Spike Source Localization in Extracellular Recordings using Amortized Variational Inference. CoRR Cole L. Hurwitz, Kai Xu, Akash Srivastava, Alessio Paolo Buccino, Matthias H. Hennig
2019	J	`jnl`	SimVAE: Simulator-Assisted Training forInterpretable Generative Models. CoRR Akash Srivastava, Jessie C. Rosenberg, Dan Gutfreund, David D. Cox
2019	A*	`conf`	Variational Russian Roulette for Deep Bayesian Nonparametrics. ICML Kai Xu, Akash Srivastava, Charles Sutton
2018	A*	`conf`	Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam. ICML Mohammad Emtiyaz Khan, Didrik Nielsen, Voot Tangkaratt, Wu Lin, Yarin Gal, Akash Srivastava
2018	J	`jnl`	Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam. CoRR Mohammad Emtiyaz Khan, Didrik Nielsen, Voot Tangkaratt, Wu Lin, Yarin Gal, Akash Srivastava
2018	A*	`conf`	HOUDINI: Lifelong Learning as Program Synthesis. NeurIPS Lazar Valkov, Dipak Chaudhari, Akash Srivastava, Charles Sutton, Swarat Chaudhuri
2018	J	`jnl`	Ratio Matching MMD Nets: Low dimensional projections for effective deep generative models. CoRR Akash Srivastava, Kai Xu, Michael U. Gutmann, Charles Sutton
2018	J	`jnl`	Synthesis of Differentiable Functional Programs for Lifelong Learning. CoRR Lazar Valkov, Dipak Chaudhari, Akash Srivastava, Charles Sutton, Swarat Chaudhuri
2018	J	`jnl`	Variational Inference In Pachinko Allocation Machines. CoRR Akash Srivastava, Charles Sutton
2017	—	`conf`	Autoencoding Variational Inference For Topic Models. ICLR (Poster) Akash Srivastava, Charles Sutton
2017	J	`jnl`	Effect of Laser Modulation on Dispersion Induced Chirp Microwave Signal Generation by Using Temporal Pulse Shaping Technique. Wirel. Pers. Commun. Sanjeev Kumar Raghuwanshi, Nimish Kumar Srivastava, Akash Srivastava, Bidhanshel S. Athokpam
2017	—	`conf`	VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning. NIPS Akash Srivastava, Lazar Valkov, Chris Russell, Michael U. Gutmann, Charles Sutton
2016	J	`jnl`	Clustering with a Reject Option: Interactive Clustering as Bayesian Prior Elicitation. CoRR Akash Srivastava, James Y. Zou, Ryan P. Adams, Charles Sutton
2016	J	`jnl`	Clustering with a Reject Option: Interactive Clustering as Bayesian Prior Elicitation. CoRR Akash Srivastava, James Y. Zou, Ryan P. Adams, Charles Sutton
2015	—	`conf`	Performance Comparison of Various Particle Swarm Optimizers in DWT-SVD watermarking for RGB Images. ICCCT Akash Srivastava
2010	J	`jnl`	Ingenuity in pattern recognition: a novel bioinformatics approach towards lung cancer identification. Int. J. Bioinform. Res. Appl. Jayant Mishra, Amit Kumar, Amita Sinha, Silpa Das, Akash Srivastava