Krishnamurthy Dvijotham

Orcid: 0000-0002-1328-4677

Affiliations:
  • Google DeepMind, London, UK
  • University of Washington, Dept. of Computer Science and Engineering, Seattle, WA, USA (former)
  • California Institute of Technology, Department of Computing and Mathematical Sciences, Pasadena, CA, USA (former)


According to our database1, Krishnamurthy Dvijotham authored at least 103 papers between 2008 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of two.

Timeline

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Bibliography

2024
Robust feasibility of systems of quadratic equations using topological degree theory.
Optim. Lett., March, 2024

Achieving the Tightest Relaxation of Sigmoids for Formal Verification.
CoRR, 2024

Beyond Thumbs Up/Down: Untangling Challenges of Fine-Grained Feedback for Text-to-Image Generation.
CoRR, 2024

Verified Neural Compressed Sensing.
CoRR, 2024

Efficient and Near-Optimal Noise Generation for Streaming Differential Privacy.
CoRR, 2024

Understanding Subjectivity through the Lens of Motivational Context in Model-Generated Image Satisfaction.
CoRR, 2024

MINT: A wrapper to make multi-modal and multi-image AI models interactive.
CoRR, 2024

Monotone, Bi-Lipschitz, and Polyak-Łojasiewicz Networks.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Efficient Error Certification for Physics-Informed Neural Networks.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Stealing part of a production language model.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Private Gradient Descent for Linear Regression: Tighter Error Bounds and Instance-Specific Uncertainty Estimation.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Expressive Losses for Verified Robustness via Convex Combinations.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

Confidence-aware Reward Optimization for Fine-tuning Text-to-Image Models.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

Correlated Noise Provably Beats Independent Noise for Differentially Private Learning.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

Efficient and Near-Optimal Noise Generation for Streaming Differential Privacy.
Proceedings of the 65th IEEE Annual Symposium on Foundations of Computer Science, 2024

Rich Human Feedback for Text-to-Image Generation.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

2023
Helping or Herding? Reward Model Ensembles Mitigate but do not Eliminate Reward Hacking.
CoRR, 2023

Selective Concept Models: Permitting Stakeholder Customisation at Test-Time.
CoRR, 2023

Faithful Knowledge Distillation.
CoRR, 2023

Provably Correct Physics-Informed Neural Networks.
CoRR, 2023

Efficient Symbolic Reasoning for Neural-Network Verification.
CoRR, 2023

Pushing the Accuracy-Group Robustness Frontier with Introspective Self-play.
CoRR, 2023

Learning to Receive Help: Intervention-Aware Concept Embedding Models.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Training Private Models That Know What They Don't Know.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Provably Bounding Neural Network Preimages.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Pushing the Accuracy-Group Robustness Frontier with Introspective Self-play.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

(Certified!!) Adversarial Robustness for Free!
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Human Uncertainty in Concept-Based AI Systems.
Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society, 2023

Interactive Concept Bottleneck Models.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022
Fixed-Point Theorem-Based Voltage Stability Margin Estimation Techniques for Distribution Systems With Renewables.
IEEE Trans. Ind. Informatics, 2022

Convergence of incentive-driven dynamics in Fisher markets.
Games Econ. Behav., 2022

IBP Regularization for Verified Adversarial Robustness via Branch-and-Bound.
CoRR, 2022

(Certified!!) Adversarial Robustness for Free!
CoRR, 2022

A Fine-Grained Analysis on Distribution Shift.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Learning Optimal Conformal Classifiers.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Role of Human-AI Interaction in Selective Prediction.
Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021
Robust Optimization for Electricity Generation.
INFORMS J. Comput., 2021

Improved Branch and Bound for Neural Network Verification via Lagrangian Decomposition.
CoRR, 2021

Verifying Probabilistic Specifications with Functional Lagrangians.
CoRR, 2021

Make Sure You're Unsure: A Framework for Verifying Probabilistic Specifications.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Overcoming the Convex Barrier for Simplex Inputs.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020
Optimal Pricing in Markets with Nonconvex Costs.
Oper. Res., 2020

Towards transformation-resilient provenance detection of digital media.
CoRR, 2020

Lagrangian Decomposition for Neural Network Verification.
Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

The Autoencoding Variational Autoencoder.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

An efficient nonconvex reformulation of stagewise convex optimization problems.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

The NodeHopper: Enabling Low Latency Ranking with Constraints via a Fast Dual Solver.
Proceedings of the KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020

Toward Evaluating Robustness of Deep Reinforcement Learning with Continuous Control.
Proceedings of the 8th International Conference on Learning Representations, 2020

Towards Verified Robustness under Text Deletion Interventions.
Proceedings of the 8th International Conference on Learning Representations, 2020

A Framework for robustness Certification of Smoothed Classifiers using F-Divergences.
Proceedings of the 8th International Conference on Learning Representations, 2020

Adversarially Robust Representations with Smooth Encoders.
Proceedings of the 8th International Conference on Learning Representations, 2020

Achieving Robustness in the Wild via Adversarial Mixing With Disentangled Representations.
Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

2019
A Framework for Robust Long-Term Voltage Stability of Distribution Systems.
IEEE Trans. Smart Grid, 2019

Convex Restriction of Power Flow Feasibility Sets.
IEEE Trans. Control. Netw. Syst., 2019

Efficient Neural Network Verification with Exactness Characterization.
Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

Adversarial Robustness through Local Linearization.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

A Dual Approach to Verify and Train Deep Networks.
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

Rigorous Agent Evaluation: An Adversarial Approach to Uncover Catastrophic Failures.
Proceedings of the 7th International Conference on Learning Representations, 2019

Verification of Non-Linear Specifications for Neural Networks.
Proceedings of the 7th International Conference on Learning Representations, 2019

Scalable Verified Training for Provably Robust Image Classification.
Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, 2019

Achieving Verified Robustness to Symbol Substitutions via Interval Bound Propagation.
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, 2019

Optimal Pricing in Markets with Non-Convex Costs.
Proceedings of the 2019 ACM Conference on Economics and Computation, 2019

Knowing When to Stop: Evaluation and Verification of Conformity to Output-Size Specifications.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

2018
Solvability Regions of Affinely Parameterized Quadratic Equations.
IEEE Control. Syst. Lett., 2018

Verification of deep probabilistic models.
CoRR, 2018

On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models.
CoRR, 2018

Training verified learners with learned verifiers.
CoRR, 2018

Safe Exploration in Continuous Action Spaces.
CoRR, 2018

Learning Fast Optimizers for Contextual Stochastic Integer Programs.
Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, 2018

A Dual Approach to Scalable Verification of Deep Networks.
Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, 2018

2017
Real-Time Optimal Power Flow.
IEEE Trans. Smart Grid, 2017

High-Voltage Solution in Radial Power Networks: Existence, Properties, and Equivalent Algorithms.
IEEE Control. Syst. Lett., 2017

Graphical models for optimal power flow.
Constraints An Int. J., 2017

Real-time OPF based on quasi-Newton methods.
Proceedings of the 51st Annual Conference on Information Sciences and Systems, 2017

2016
Opportunities for Price Manipulation by Aggregators in Electricity Markets.
SIGMETRICS Perform. Evaluation Rev., 2016

Market Dynamics of Best-Response with Lookahead.
CoRR, 2016

Error bounds on the DC power flow approximation: A convex relaxation approach.
Proceedings of the 55th IEEE Conference on Decision and Control, 2016

Monotone operator approach to power flow solutions.
Proceedings of the 2016 American Control Conference, 2016

2015
Convex Structured Controller Design in Finite Horizon.
IEEE Trans. Control. Netw. Syst., 2015

Natural Gas Flow Solutions with Guarantees: A Monotone Operator Theory Approach.
CoRR, 2015

Construction of power flow feasibility sets.
CoRR, 2015

Solving the Power Flow Equations: A Monotone Operator Approach.
CoRR, 2015

Solving the Power Flow Equations: A Monotone Operator Theory Approach.
CoRR, 2015

A differential analysis of the power flow equations.
Proceedings of the 54th IEEE Conference on Decision and Control, 2015

Convexity of structure preserving energy functions in power transmission: Novel results and applications.
Proceedings of the American Control Conference, 2015

Systems of quadratic equations: Efficient solution algorithms and conditions for solvability.
Proceedings of the 53rd Annual Allerton Conference on Communication, 2015

2014
Automating Stochastic Optimal Control.
PhD thesis, 2014

Efficient Synchronization Stability Metrics for Fault Clearing.
CoRR, 2014

Universal Convexification via Risk-Aversion.
Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence, 2014

Storage Sizing and Placement through Operational and Uncertainty-Aware Simulations.
Proceedings of the 47th Hawaii International Conference on System Sciences, 2014

Convex risk averse control design.
Proceedings of the 53rd IEEE Conference on Decision and Control, 2014

2013
Convex Structured Controller Design.
CoRR, 2013

Time varying nonlinear Policy Gradients.
Proceedings of the 52nd IEEE Conference on Decision and Control, 2013

Convexity of optimal linear controller design.
Proceedings of the 52nd IEEE Conference on Decision and Control, 2013

Convex control design via covariance minimization.
Proceedings of the 51st Annual Allerton Conference on Communication, 2013

2012
Distributed control of generation in a transmission grid with a high penetration of renewables.
Proceedings of the IEEE Third International Conference on Smart Grid Communications, 2012

Linearly solvable Markov games.
Proceedings of the American Control Conference, 2012

2011
Operations-Based Planning for Placement and Sizing of Energy Storage in a Grid With a High Penetration of Renewables
CoRR, 2011

A Unifying Framework for Linearly Solvable Control.
Proceedings of the UAI 2011, 2011

2010
Inverse Optimal Control with Linearly-Solvable MDPs.
Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010

A nullspace analysis of the nuclear norm heuristic for rank minimization.
Proceedings of the IEEE International Conference on Acoustics, 2010

2008
New closed-form bounds on the partition function.
Mach. Learn., 2008


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