J. Zico Kolter

Affiliations:
  • Carnegie Mellon University, PA, USA
  • MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, USA (former)


According to our database1, J. Zico Kolter authored at least 228 papers between 2003 and 2024.

Collaborative distances:

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Bibliography

2024
Text Descriptions are Compressive and Invariant Representations for Visual Learning.
Trans. Mach. Learn. Res., 2024

Understanding Optimization in Deep Learning with Central Flows.
CoRR, 2024

One-Step Diffusion Distillation through Score Implicit Matching.
CoRR, 2024

Rethinking Distance Metrics for Counterfactual Explainability.
CoRR, 2024

Adaptive Data Optimization: Dynamic Sample Selection with Scaling Laws.
CoRR, 2024

Mimetic Initialization Helps State Space Models Learn to Recall.
CoRR, 2024

Context-Parametric Inversion: Why Instruction Finetuning May Not Actually Improve Context Reliance.
CoRR, 2024

AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents.
CoRR, 2024

Finetuning CLIP to Reason about Pairwise Differences.
CoRR, 2024

Transformers to SSMs: Distilling Quadratic Knowledge to Subquadratic Models.
CoRR, 2024

Prompt Recovery for Image Generation Models: A Comparative Study of Discrete Optimizers.
CoRR, 2024

FUSE-ing Language Models: Zero-Shot Adapter Discovery for Prompt Optimization Across Tokenizers.
CoRR, 2024

Consistency Models Made Easy.
CoRR, 2024

Understanding Hallucinations in Diffusion Models through Mode Interpolation.
CoRR, 2024

Improving Alignment and Robustness with Circuit Breakers.
CoRR, 2024

Neural Network Verification with Branch-and-Bound for General Nonlinearities.
CoRR, 2024

Computing Low-Entropy Couplings for Large-Support Distributions.
CoRR, 2024

Rethinking LLM Memorization through the Lens of Adversarial Compression.
CoRR, 2024

Forcing Diffuse Distributions out of Language Models.
CoRR, 2024

Predicting the Performance of Foundation Models via Agreement-on-the-Line.
CoRR, 2024

Automated Black-box Prompt Engineering for Personalized Text-to-Image Generation.
CoRR, 2024

AcceleratedLiNGAM: Learning Causal DAGs at the speed of GPUs.
CoRR, 2024

Massive Activations in Large Language Models.
CoRR, 2024

Bayesian Neural Networks with Domain Knowledge Priors.
CoRR, 2024

An Axiomatic Approach to Model-Agnostic Concept Explanations.
CoRR, 2024

TOFU: A Task of Fictitious Unlearning for LLMs.
CoRR, 2024

Understanding Augmentation-based Self-Supervised Representation Learning via RKHS Approximation and Regression.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

A Simple and Effective Pruning Approach for Large Language Models.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

The Update-Equivalence Framework for Decision-Time Planning.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

T-MARS: Improving Visual Representations by Circumventing Text Feature Learning.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

On the Joint Interaction of Models, Data, and Features.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

Manifold Preserving Guided Diffusion.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

Why is SAM Robust to Label Noise?
Proceedings of the Twelfth International Conference on Learning Representations, 2024

Understanding prompt engineering may not require rethinking generalization.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

DART: Implicit Doppler Tomography for Radar Novel View Synthesis.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

From Variance to Veracity: Unbundling and Mitigating Gradient Variance in Differentiable Bundle Adjustment Layers.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

Scaling Laws for Data Filtering - Data Curation Cannot be Compute Agnostic.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

2023
Patches Are All You Need?
Trans. Mach. Learn. Res., 2023

Monotone deep Boltzmann machines.
Trans. Mach. Learn. Res., 2023

Generative Posterior Networks for Approximately Bayesian Epistemic Uncertainty Estimation.
CoRR, 2023

Projected Off-Policy Q-Learning (POP-QL) for Stabilizing Offline Reinforcement Learning.
CoRR, 2023

TorchDEQ: A Library for Deep Equilibrium Models.
CoRR, 2023

On the Neural Tangent Kernel of Equilibrium Models.
CoRR, 2023

Reliable Test-Time Adaptation via Agreement-on-the-Line.
CoRR, 2023

Representation Engineering: A Top-Down Approach to AI Transparency.
CoRR, 2023

Universal and Transferable Adversarial Attacks on Aligned Language Models.
CoRR, 2023

Importance of equivariant and invariant symmetries for fluid flow modeling.
CoRR, 2023

Leveraging Multiple Descriptive Features for Robust Few-shot Image Learning.
CoRR, 2023

Localized Text-to-Image Generation for Free via Cross Attention Control.
CoRR, 2023

Understanding Augmentation-based Self-Supervised Representation Learning via RKHS Approximation.
CoRR, 2023

Low-Variance Gradient Estimation in Unrolled Computation Graphs with ES-Single.
CoRR, 2023

Sinkhorn-Flow: Predicting Probability Mass Flow in Dynamical Systems Using Optimal Transport.
CoRR, 2023

Permutation Equivariant Neural Functionals.
CoRR, 2023

Adversarial robustness in discontinuous spaces via alternating sampling & descent.
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2023

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

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

Learning with Explanation Constraints.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Deep Equilibrium Based Neural Operators for Steady-State PDEs.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Language Models are Weak Learners.
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

On the Importance of Exploration for Generalization in Reinforcement Learning.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

One-Step Diffusion Distillation via Deep Equilibrium Models.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Practical Critic Gradient based Actor Critic for On-Policy Reinforcement Learning.
Proceedings of the Learning for Dynamics and Control Conference, 2023

Deep Off-Policy Iterative Learning Control.
Proceedings of the Learning for Dynamics and Control Conference, 2023

Mimetic Initialization of Self-Attention Layers.
Proceedings of the International Conference on Machine Learning, 2023

Abstracting Imperfect Information Away from Two-Player Zero-Sum Games.
Proceedings of the International Conference on Machine Learning, 2023

Can Neural Network Memorization Be Localized?
Proceedings of the International Conference on Machine Learning, 2023

Understanding Why Generalized Reweighting Does Not Improve Over ERM.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Perfectly Secure Steganography Using Minimum Entropy Coupling.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Understanding the Covariance Structure of Convolutional Filters.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Games.
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

Simple initialization and parametrization of sinusoidal networks via their kernel bandwidth.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Model-tuning Via Prompts Makes NLP Models Adversarially Robust.
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023

Single Image Backdoor Inversion via Robust Smoothed Classifiers.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

Finetune like you pretrain: Improved finetuning of zero-shot vision models.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

Improving Adversarial Robustness via Joint Classification and Multiple Explicit Detection Classes.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

Losses over Labels: Weakly Supervised Learning via Direct Loss Construction.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

Function Approximation for Solving Stackelberg Equilibrium in Large Perfect Information Games.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

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

Smooth-Reduce: Leveraging Patches for Improved Certified Robustness.
CoRR, 2022

Dojo: A Differentiable Simulator for Robotics.
CoRR, 2022

General Cutting Planes for Bound-Propagation-Based Neural Network Verification.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Efficiently Computing Local Lipschitz Constants of Neural Networks via Bound Propagation.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Deep Equilibrium Approaches to Diffusion Models.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

The Pitfalls of Regularization in Off-Policy TD Learning.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Characterizing Datapoints via Second-Split Forgetting.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Learning Options via Compression.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Test Time Adaptation via Conjugate Pseudo-labels.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Agreement-on-the-line: Predicting the Performance of Neural Networks under Distribution Shift.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Path Independent Equilibrium Models Can Better Exploit Test-Time Computation.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

A Branch and Bound Framework for Stronger Adversarial Attacks of ReLU Networks.
Proceedings of the International Conference on Machine Learning, 2022

Communicating via Markov Decision Processes.
Proceedings of the International Conference on Machine Learning, 2022

Certified Robustness for Deep Equilibrium Models via Interval Bound Propagation.
Proceedings of the Tenth International Conference on Learning Representations, 2022

A Fine-Tuning Approach to Belief State Modeling.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Assessing Generalization of SGD via Disagreement.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Neural Deep Equilibrium Solvers.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Unified Fully and Timestamp Supervised Temporal Action Segmentation via Sequence to Sequence Translation.
Proceedings of the Computer Vision - ECCV 2022, 2022

Deep Equilibrium Optical Flow Estimation.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

2021
DeepSplit: Scalable Verification of Deep Neural Networks via Operator Splitting.
CoRR, 2021

Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Complete and Incomplete Neural Network Verification.
CoRR, 2021

You Only Query Once: Effective Black Box Adversarial Attacks with Minimal Repeated Queries.
CoRR, 2021

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

Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Neural Network Robustness Verification.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Monte Carlo Tree Search With Iteratively Refining State Abstractions.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Robustness between the worst and average case.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Training Certifiably Robust Neural Networks with Efficient Local Lipschitz Bounds.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

$(\textrm{Implicit})^2$: Implicit Layers for Implicit Representations.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Joint inference and input optimization in equilibrium networks.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Adversarially robust learning for security-constrained optimal power flow.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Simple and Efficient Hard Label Black-box Adversarial Attacks in Low Query Budget Regimes.
Proceedings of the KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021

DORO: Distributional and Outlier Robust Optimization.
Proceedings of the 38th International Conference on Machine Learning, 2021

On Proximal Policy Optimization's Heavy-tailed Gradients.
Proceedings of the 38th International Conference on Machine Learning, 2021

RATT: Leveraging Unlabeled Data to Guarantee Generalization.
Proceedings of the 38th International Conference on Machine Learning, 2021

Stabilizing Equilibrium Models by Jacobian Regularization.
Proceedings of the 38th International Conference on Machine Learning, 2021

Orthogonalizing Convolutional Layers with the Cayley Transform.
Proceedings of the 9th International Conference on Learning Representations, 2021

Provably robust classification of adversarial examples with detection.
Proceedings of the 9th International Conference on Learning Representations, 2021

Estimating Lipschitz constants of monotone deep equilibrium models.
Proceedings of the 9th International Conference on Learning Representations, 2021

Multiplicative Filter Networks.
Proceedings of the 9th International Conference on Learning Representations, 2021

DC3: A learning method for optimization with hard constraints.
Proceedings of the 9th International Conference on Learning Representations, 2021

Enforcing robust control guarantees within neural network policies.
Proceedings of the 9th International Conference on Learning Representations, 2021

Gradient Descent on Neural Networks Typically Occurs at the Edge of Stability.
Proceedings of the 9th International Conference on Learning Representations, 2021

Learning perturbation sets for robust machine learning.
Proceedings of the 9th International Conference on Learning Representations, 2021

A Bayesian Model of Cash Bail Decisions.
Proceedings of the FAccT '21: 2021 ACM Conference on Fairness, 2021

Enforcing Policy Feasibility Constraints through Differentiable Projection for Energy Optimization.
Proceedings of the e-Energy '21: The Twelfth ACM International Conference on Future Energy Systems, Virtual Event, Torino, Italy, 28 June, 2021

Exploring Classic and Neural Lexical Translation Models for Information Retrieval: Interpretability, Effectiveness, and Efficiency Benefits.
Proceedings of the Advances in Information Retrieval, 2021

Defending Multimodal Fusion Models Against Single-Source Adversaries.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

Provably Safe PAC-MDP Exploration Using Analogies.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020
Challenging common interpretability assumptions in feature attribution explanations.
CoRR, 2020

Poisoned classifiers are not only backdoored, they are fundamentally broken.
CoRR, 2020

Gaussian MRF Covariance Modeling for Efficient Black-Box Adversarial Attacks.
CoRR, 2020

A community-powered search of machine learning strategy space to find NMR property prediction models.
CoRR, 2020

Hard Label Black-box Adversarial Attacks in Low Query Budget Regimes.
CoRR, 2020

Provably robust deep generative models.
CoRR, 2020

Black-box Smoothing: A Provable Defense for Pretrained Classifiers.
CoRR, 2020

Monotone operator equilibrium networks.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Community detection using fast low-cardinality semidefinite programming
.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Denoised Smoothing: A Provable Defense for Pretrained Classifiers.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Efficient semidefinite-programming-based inference for binary and multi-class MRFs.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Deep Archimedean Copulas.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Multiscale Deep Equilibrium Models.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Neural Network Virtual Sensors for Fuel Injection Quantities with Provable Performance Specifications.
Proceedings of the IEEE Intelligent Vehicles Symposium, 2020

Certified Robustness to Label-Flipping Attacks via Randomized Smoothing.
Proceedings of the 37th International Conference on Machine Learning, 2020

Overfitting in adversarially robust deep learning.
Proceedings of the 37th International Conference on Machine Learning, 2020

Adversarial Robustness Against the Union of Multiple Perturbation Models.
Proceedings of the 37th International Conference on Machine Learning, 2020

Combining Differentiable PDE Solvers and Graph Neural Networks for Fluid Flow Prediction.
Proceedings of the 37th International Conference on Machine Learning, 2020

Fast is better than free: Revisiting adversarial training.
Proceedings of the 8th International Conference on Learning Representations, 2020

Differentiable learning of numerical rules in knowledge graphs.
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

AP-Perf: Incorporating Generic Performance Metrics in Differentiable Learning.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019
Dynamic Modeling and Equilibria in Fair Decision Making.
CoRR, 2019

Adversarial Music: Real World Audio Adversary Against Wake-word Detection System.
CoRR, 2019

Black-box Adversarial Attacks with Bayesian Optimization.
CoRR, 2019

On Physical Adversarial Patches for Object Detection.
CoRR, 2019

The Limited Multi-Label Projection Layer.
CoRR, 2019

Perceptual Based Adversarial Audio Attacks.
CoRR, 2019

Generalization in Deep Networks: The Role of Distance from Initialization.
CoRR, 2019

Computational sustainability: computing for a better world and a sustainable future.
Commun. ACM, 2019

Uniform convergence may be unable to explain generalization in deep learning.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Learning Stable Deep Dynamics Models.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

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

Differentiable Convex Optimization Layers.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Adversarial Music: Real world Audio Adversary against Wake-word Detection System.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Wasserstein Adversarial Examples via Projected Sinkhorn Iterations.
Proceedings of the 36th International Conference on Machine Learning, 2019

SATNet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver.
Proceedings of the 36th International Conference on Machine Learning, 2019

Adversarial camera stickers: A physical camera-based attack on deep learning systems.
Proceedings of the 36th International Conference on Machine Learning, 2019

Certified Adversarial Robustness via Randomized Smoothing.
Proceedings of the 36th International Conference on Machine Learning, 2019

Deterministic PAC-Bayesian generalization bounds for deep networks via generalizing noise-resilience.
Proceedings of the 7th International Conference on Learning Representations, 2019

Trellis Networks for Sequence Modeling.
Proceedings of the 7th International Conference on Learning Representations, 2019

Neural Variational Identification and Filtering for Stochastic Non-linear Dynamical Systems with Application to Non-intrusive Load Monitoring.
Proceedings of the IEEE International Conference on Acoustics, 2019

A Continuous-Time View of Early Stopping for Least Squares Regression.
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

Multimodal Transformer for Unaligned Multimodal Language Sequences.
Proceedings of the 57th Conference of the Association for Computational Linguistics, 2019

Low-Rank Semidefinite Programming for the MAX2SAT Problem.
Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

Large Scale Learning of Agent Rationality in Two-Player Zero-Sum Games.
Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling.
CoRR, 2018

Realtime Query Completion via Deep Language Models.
Proceedings of the SIGIR 2018 Workshop On eCommerce co-located with the 41st International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2018), 2018

Scaling provable adversarial defenses.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

End-to-End Differentiable Physics for Learning and Control.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Differentiable MPC for End-to-end Planning and Control.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

What Game Are We Playing? End-to-end Learning in Normal and Extensive Form Games.
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

Provable Defenses against Adversarial Examples via the Convex Outer Adversarial Polytope.
Proceedings of the 35th International Conference on Machine Learning, 2018

Convolutional Sequence Modeling Revisited.
Proceedings of the 6th International Conference on Learning Representations, 2018

2017
Intelligent Pothole Detection and Road Condition Assessment.
CoRR, 2017

The Mixing method: coordinate descent for low-rank semidefinite programming.
CoRR, 2017

Task-based End-to-end Model Learning.
CoRR, 2017

Gradient descent GAN optimization is locally stable.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Task-based End-to-end Model Learning in Stochastic Optimization.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Input Convex Neural Networks.
Proceedings of the 34th International Conference on Machine Learning, 2017

OptNet: Differentiable Optimization as a Layer in Neural Networks.
Proceedings of the 34th International Conference on Machine Learning, 2017

A Semismooth Newton Method for Fast, Generic Convex Programming.
Proceedings of the 34th International Conference on Machine Learning, 2017

Polynomial Optimization Methods for Matrix Factorization.
Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016
Hierarchical modeling of systems with similar components: A framework for adaptive monitoring and control.
Reliab. Eng. Syst. Saf., 2016

Computational approaches for efficient scheduling of steel plants as demand response resource.
Proceedings of the Power Systems Computation Conference, 2016

The Multiple Quantile Graphical Model.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Epigraph projections for fast general convex programming.
Proceedings of the 33nd International Conference on Machine Learning, 2016

2015
Optimal Planning and Learning in Uncertain Environments for the Management of Wind Farms.
J. Comput. Civ. Eng., 2015

Disciplined Convex Stochastic Programming: A New Framework for Stochastic Optimization.
Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, 2015

An SVD and Derivative Kernel Approach to Learning from Geometric Data.
Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015

An Additive Autoregressive Hidden Markov Model for Energy Disaggregation.
Proceedings of the Computational Sustainability, 2015

2014
Fast Newton methods for the group fused lasso.
Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence, 2014

Contextually Supervised Source Separation with Application to Energy Disaggregation.
Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014

2013
A Fast Algorithm for Sparse Controller Design.
CoRR, 2013

A moving horizon state estimator in the control of thermostatically controlled loads for demand response.
Proceedings of the IEEE Fourth International Conference on Smart Grid Communications, 2013

Sparse Gaussian Conditional Random Fields: Algorithms, Theory, and Application to Energy Forecasting.
Proceedings of the 30th International Conference on Machine Learning, 2013

Large-scale probabilistic forecasting in energy systems using sparse Gaussian conditional random fields.
Proceedings of the 52nd IEEE Conference on Decision and Control, 2013

2012
Approximate Inference in Additive Factorial HMMs with Application to Energy Disaggregation.
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

Design, analysis, and learning control of a fully actuated micro wind turbine.
Proceedings of the American Control Conference, 2012

2011
The Stanford LittleDog: A learning and rapid replanning approach to quadruped locomotion.
Int. J. Robotics Res., 2011

The Fixed Points of Off-Policy TD.
Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

Towards fully autonomous driving: Systems and algorithms.
Proceedings of the IEEE Intelligent Vehicles Symposium (IV), 2011

A Large-Scale Study on Predicting and Contextualizing Building Energy Usage.
Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence, 2011

2010
Learning and control with inaccurate models.
PhD thesis, 2010

Energy Disaggregation via Discriminative Sparse Coding.
Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

A probabilistic approach to mixed open-loop and closed-loop control, with application to extreme autonomous driving.
Proceedings of the IEEE International Conference on Robotics and Automation, 2010

2009
Policy search via the signed derivative.
Proceedings of the Robotics: Science and Systems V, University of Washington, Seattle, USA, June 28, 2009

Task-space trajectories via cubic spline optimization.
Proceedings of the 2009 IEEE International Conference on Robotics and Automation, 2009

Stereo vision and terrain modeling for quadruped robots.
Proceedings of the 2009 IEEE International Conference on Robotics and Automation, 2009

Regularization and feature selection in least-squares temporal difference learning.
Proceedings of the 26th Annual International Conference on Machine Learning, 2009

Near-Bayesian exploration in polynomial time.
Proceedings of the 26th Annual International Conference on Machine Learning, 2009

2008
A control architecture for quadruped locomotion over rough terrain.
Proceedings of the 2008 IEEE International Conference on Robotics and Automation, 2008

Space-indexed dynamic programming: learning to follow trajectories.
Proceedings of the Machine Learning, 2008

2007
Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts.
J. Mach. Learn. Res., 2007

Learning omnidirectional path following using dimensionality reduction.
Proceedings of the Robotics: Science and Systems III, 2007

Hierarchical Apprenticeship Learning with Application to Quadruped Locomotion.
Proceedings of the Advances in Neural Information Processing Systems 20, 2007

2006
Learning to Detect and Classify Malicious Executables in the Wild.
J. Mach. Learn. Res., 2006

2005
Using additive expert ensembles to cope with concept drift.
Proceedings of the Machine Learning, 2005

2004
Learning to detect malicious executables in the wild.
Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2004

2003
Dynamic Weighted Majority: A New Ensemble Method for Tracking Concept Drift.
Proceedings of the 3rd IEEE International Conference on Data Mining (ICDM 2003), 2003


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