Timezone: »
Sustainability encompasses the balance of environmental, economic and societal demands. There is strong evidence suggesting that more actions need to be taken in order to achieve this balance. For example, Edward O. Wilson said in his 2002 Book The Future of Life that "at the current rates of human destruction of natural ecosystems, 50% of all species of life on earth will be extinct in 100 years". More recently, a 2012 review in Nature has stated that, similarly to localized ecological systems, "the global ecosystem as a whole can react in the same way and is approaching a planetary-scale critical transition as a result of human influence".
While the significance of the problem is apparent, more involvement from the machine learning community in sustainability problems is required. Not surprisingly, sustainability problems bring along interesting challenges and opportunities for machine learning in terms of complexity, scalability and impact in areas such as prediction, modeling and control. This workshop aims at bringing together scientists in machine learning, operations research, applied mathematics and statistics with a strong interest in sustainability to discuss how to use existing techniques and how to develop novel methods in order to address such challenges.
There are many application areas in sustainability where machine learning can have a significant impact. For example:
- Climate change
- Conservation and biodiversity
- Socio-economic systems
- Understanding energy consumption
- Renewable energy
- Impact of mining
- Sustainability in the developing world
- Managing the power grid
- Biofuels
Similarly, machine learning approaches to sustainability problems can be drawn from several fields such as:
- Constraint optimization
- Dynamical systems
- Spatio-temporal modeling
- Probabilistic inference
- Sensing and monitoring
- Decision making under uncertainty
- Stochastic optimization
The talks and posters are expected to span (but not be limited to) the above areas. More importantly, there will be a specific focus on how cutting-edge machine learning research is developed (i.e. not only using off-the-shelf ML techniques) in order to address challenges in terms of complexity, scalability and impact that sustainability problems may pose.
The main expected outcomes of this workshop are: (1) attracting more people to work on computational sustainability; (2) transfer of knowledge across different application domains; and (3) emerging collaboration between participants. More long-term avenues such as datasets and competitions will be explored.
There will be an award (~ $$250 book voucher) for the best contribution, which will be given an oral presentation.
Author Information
Edwin Bonilla (Data61)
Thomas Dietterich (Oregon State University)
Tom Dietterich (AB Oberlin College 1977; MS University of Illinois 1979; PhD Stanford University 1984) is Professor and Director of Intelligent Systems Research at Oregon State University. Among his contributions to machine learning research are (a) the formalization of the multiple-instance problem, (b) the development of the error-correcting output coding method for multi-class prediction, (c) methods for ensemble learning, (d) the development of the MAXQ framework for hierarchical reinforcement learning, and (e) the application of gradient tree boosting to problems of structured prediction and latent variable models. Dietterich has pursued application-driven fundamental research in many areas including drug discovery, computer vision, computational sustainability, and intelligent user interfaces. Dietterich has served the machine learning community in a variety of roles including Executive Editor of the Machine Learning journal, co-founder of the Journal of Machine Learning Research, editor of the MIT Press Book Series on Adaptive Computation and Machine Learning, and editor of the Morgan-Claypool Synthesis series on Artificial Intelligence and Machine Learning. He was Program Co-Chair of AAAI-1990, Program Chair of NIPS-2000, and General Chair of NIPS-2001. He was first President of the International Machine Learning Society (the parent organization of ICML) and served a term on the NIPS Board of Trustees and the Council of AAAI.
Theodoros Damoulas (New York University)
Andreas Krause (ETHZ)
Daniel Sheldon (University of Massachusetts Amherst)
Iadine Chades (CSIRO)
J. Zico Kolter (Carnegie Mellon University / Bosch Center for AI)
Zico Kolter is an Assistant Professor in the School of Computer Science at Carnegie Mellon University, and also serves as Chief Scientist of AI Research for the Bosch Center for Artificial Intelligence. His work focuses on the intersection of machine learning and optimization, with a large focus on developing more robust, explainable, and rigorous methods in deep learning. In addition, he has worked on a number of application areas, highlighted by work on sustainability and smart energy systems. He is the recipient of the DARPA Young Faculty Award, and best paper awards at KDD, IJCAI, and PESGM.
Bistra Dilkina (Cornell University)
Carla Gomes (Cornell University)
Hugo P Simao (Princeton University)
More from the Same Authors
-
2020 : An adversarially robust approach to security-constrained optimal power flow »
Neeraj Vijay Bedmutha · Priya Donti · J. Zico Kolter -
2021 Spotlight: DiBS: Differentiable Bayesian Structure Learning »
Lars Lorch · Jonas Rothfuss · Bernhard Schölkopf · Andreas Krause -
2021 : Gaussian Mixture Variational Autoencoder with Contrastive Learning for Multi-Label Classification »
Junwen Bai · Shufeng Kong · Carla Gomes -
2021 : Towards Safe Global Optimality in Robot Learning with GoSafe »
Bhavya Sukhija · Matteo Turchetta · Andreas Krause · Sebastian Trimpe · Dominik Baumann -
2021 : DiBS: Differentiable Bayesian Structure Learning »
Lars Lorch · Jonas Rothfuss · Bernhard Schölkopf · Andreas Krause -
2021 : Learning Single-Cell Perturbation Responses using Neural Optimal Transport »
Charlotte Bunne · Stefan Stark · Gabriele Gut · Andreas Krause · Gunnar Rätsch · Lucas Pelkmans · Kjong Lehmann -
2021 : Gaussian Mixture Variational Autoencoder with Contrastive Learning for Multi-Label Classification »
Junwen Bai · Shufeng Kong · Carla Gomes -
2021 : Resolving Super Fine-Resolution SIF via Coarsely-Supervised U-Net Regression »
Joshua Fan · Di Chen · Jiaming Wen · Ying Sun · Carla Gomes -
2021 : A GNN-RNN Approach for Harnessing Geospatial and Temporal Information: Application to Crop Yield Prediction »
Joshua Fan · Junwen Bai · Zhiyun Li · Ariel Ortiz-Bobea · Carla Gomes -
2022 : Active Bayesian Causal Inference »
Christian Toth · Lars Lorch · Christian Knoll · Andreas Krause · Franz Pernkopf · Robert Peharz · Julius von Kügelgen -
2022 : Xtal2DoS: Attention-based Crystal to Sequence Learning for Density of States Prediction »
Junwen Bai · Yuanqi Du · Yingheng Wang · Shufeng Kong · John Gregoire · Carla Gomes -
2022 : Structure-based Drug Design with Equivariant Diffusion Models »
Arne Schneuing · Yuanqi Du · Charles Harris · Arian Jamasb · Ilia Igashov · weitao Du · Tom Blundell · Pietro Lió · Carla Gomes · Max Welling · Michael Bronstein · Bruno Correia -
2022 : Generative Posterior Networks for Approximately Bayesian Epistemic Uncertainty Estimation »
Melrose Roderick · Felix Berkenkamp · Fatemeh Sheikholeslami · J. Zico Kolter -
2022 : Denoised Smoothing with Sample Rejection for Robustifying Pretrained Classifiers »
Fatemeh Sheikholeslami · Wan-Yi Lin · Jan Hendrik Metzen · Huan Zhang · J. Zico Kolter -
2022 : Active Bayesian Causal inference »
Christian Toth · Lars Lorch · Christian Knoll · Andreas Krause · Franz Pernkopf · Robert Peharz · Julius von Kügelgen -
2022 : Amortized Inference for Causal Structure Learning »
Lars Lorch · Scott Sussex · Jonas Rothfuss · Andreas Krause · Bernhard Schölkopf -
2022 : MARS: Meta-learning as score matching in the function space »
Kruno Lehman · Jonas Rothfuss · Andreas Krause -
2022 : Neural All-Pairs Shortest Path for Reinforcement Learning »
Cristina Pinneri · Georg Martius · Andreas Krause -
2022 : A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Games »
Samuel Sokota · Ryan D'Orazio · J. Zico Kolter · Nicolas Loizou · Marc Lanctot · Ioannis Mitliagkas · Noam Brown · Christian Kroer -
2022 : Uncertainty-Driven Exploration for Generalization in Reinforcement Learning »
Yiding Jiang · J. Zico Kolter · Roberta Raileanu -
2022 : Improving Adversarial Robustness via Joint Classification and Multiple Explicit Detection Classes »
Sina Baharlouei · Fatemeh Sheikholeslami · Meisam Razaviyayn · J. Zico Kolter -
2023 Poster: On the Importance of Exploration for Generalization in Reinforcement Learning »
Yiding Jiang · J. Zico Kolter · Roberta Raileanu -
2023 Poster: Deep Equilibrium Based Neural Operators for Steady-State PDEs »
Tanya Marwah · Ashwini Pokle · J. Zico Kolter · Zachary Lipton · Jianfeng Lu · Andrej Risteski -
2023 Poster: Optimistic Active Exploration of Dynamical Systems »
Bhavya · Lenart Treven · Cansu Sancaktar · Sebastian Blaes · Stelian Coros · Andreas Krause -
2023 Poster: Riemannian stochastic optimization methods avoid strict saddle points »
Ya-Ping Hsieh · Mohammad Reza Karimi Jaghargh · Andreas Krause · Panayotis Mertikopoulos -
2023 Poster: Learning with Explanation Constraints »
Rattana Pukdee · Dylan Sam · J. Zico Kolter · Maria-Florina Balcan · Pradeep Ravikumar -
2023 Poster: Unsupervised Learning for Solving the Travelling Salesman Problem »
Yimeng Min · Yiwei Bai · Carla Gomes -
2023 Poster: Learning To Dive In Branch And Bound »
Max Paulus · Andreas Krause -
2023 Poster: A new perspective on building efficient and expressive 3D equivariant graph neural networks »
weitao Du · Yuanqi Du · Limei Wang · Dieqiao Feng · Guifeng Wang · Shuiwang Ji · Carla Gomes · Zhi-Ming Ma -
2023 Poster: Permutation Equivariant Neural Functionals »
Allan Zhou · Kaien Yang · Kaylee Burns · Adriano Cardace · Yiding Jiang · Samuel Sokota · J. Zico Kolter · Chelsea Finn -
2023 Poster: Implicit Manifold Gaussian Process Regression »
Bernardo Fichera · Viacheslav Borovitskiy · Andreas Krause · Aude G Billard -
2023 Poster: One-Step Diffusion Distillation via Deep Equilibrium Models »
Zhengyang Geng · Ashwini Pokle · J. Zico Kolter -
2023 Poster: A Dynamical System View of Langevin-Based Non-Convex Sampling »
Mohammad Reza Karimi Jaghargh · Ya-Ping Hsieh · Andreas Krause -
2023 Poster: Anytime Model Selection in Linear Bandits »
Parnian Kassraie · Aldo Pacchiano · Nicolas Emmenegger · Andreas Krause -
2023 Poster: Stochastic Approximation Algorithms for Systems of Interacting Particles »
Mohammad Reza Karimi Jaghargh · Ya-Ping Hsieh · Andreas Krause -
2023 Poster: Efficient Exploration in Continuous-time Model-based Reinforcement Learning »
Lenart Treven · Jonas Hübotter · Bhavya · Florian Dorfler · Andreas Krause -
2023 Poster: Contextual Stochastic Bilevel Optimization »
Yifan Hu · Jie Wang · Yao Xie · Andreas Krause · Daniel Kuhn -
2023 Poster: Neural Functional Transformers »
Allan Zhou · Kaien Yang · Yiding Jiang · Kaylee Burns · Winnie Xu · Samuel Sokota · J. Zico Kolter · Chelsea Finn -
2023 Poster: Provably Bounding Neural Network Preimages »
Christopher Brix · Suhas Kotha · Huan Zhang · J. Zico Kolter · Krishnamurthy Dvijotham -
2023 Poster: Multitask Learning with No Regret: from Improved Confidence Bounds to Active Learning »
Pier Giuseppe Sessa · Pierre Laforgue · Nicolò Cesa-Bianchi · Andreas Krause -
2023 Poster: Language Models are Weak Learners »
Hariharan Manikandan · Yiding Jiang · J. Zico Kolter -
2023 Poster: Likelihood Ratio Confidence Sets for Sequential Decision Making »
Nicolas Emmenegger · Mojmir Mutny · Andreas Krause -
2023 Poster: M$^2$Hub: Unlocking the Potential of Machine Learning for Materials Discovery »
Yuanqi Du · Yingheng Wang · Yining Huang · Jianan Canal Li · Yanqiao Zhu · Tian Xie · Chenru Duan · John Gregoire · Carla Gomes -
2023 Workshop: Adaptive Experimental Design and Active Learning in the Real World »
Willie Neiswanger · Mojmir Mutny · Ilija Bogunovic · Ava Soleimany · Zi Wang · Stefano Ermon · Andreas Krause -
2023 Workshop: XAI in Action: Past, Present, and Future Applications »
Chhavi Yadav · Michal Moshkovitz · Nave Frost · Suraj Srinivas · Bingqing Chen · Valentyn Boreiko · Himabindu Lakkaraju · J. Zico Kolter · Dotan Di Castro · Kamalika Chaudhuri -
2023 Workshop: AI for Science: from Theory to Practice »
Yuanqi Du · Max Welling · Yoshua Bengio · Marinka Zitnik · Carla Gomes · Jure Leskovec · Maria Brbic · Wenhao Gao · Kexin Huang · Ziming Liu · Rocío Mercado · Miles Cranmer · Shengchao Liu · Lijing Wang -
2022 Workshop: Trustworthy and Socially Responsible Machine Learning »
Huan Zhang · Linyi Li · Chaowei Xiao · J. Zico Kolter · Anima Anandkumar · Bo Li -
2022 Spotlight: Kernel Interpolation with Sparse Grids »
Mohit Yadav · Daniel Sheldon · Cameron Musco -
2022 Spotlight: Lightning Talks 1A-3 »
Kimia Noorbakhsh · Ronan Perry · Qi Lyu · Jiawei Jiang · Christian Toth · Olivier Jeunen · Xin Liu · Yuan Cheng · Lei Li · Manuel Rodriguez · Julius von Kügelgen · Lars Lorch · Nicolas Donati · Lukas Burkhalter · Xiao Fu · Zhongdao Wang · Songtao Feng · Ciarán Gilligan-Lee · Rishabh Mehrotra · Fangcheng Fu · Jing Yang · Bernhard Schölkopf · Ya-Li Li · Christian Knoll · Maks Ovsjanikov · Andreas Krause · Shengjin Wang · Hong Zhang · Mounia Lalmas · Bolin Ding · Bo Du · Yingbin Liang · Franz Pernkopf · Robert Peharz · Anwar Hithnawi · Julius von Kügelgen · Bo Li · Ce Zhang -
2022 Spotlight: Active Bayesian Causal Inference »
Christian Toth · Lars Lorch · Christian Knoll · Andreas Krause · Franz Pernkopf · Robert Peharz · Julius von Kügelgen -
2022 : Zico Kolter, Adapt like you train: How optimization at training time affects model finetuning and adaptation »
J. Zico Kolter -
2022 Workshop: AI for Science: Progress and Promises »
Yi Ding · Yuanqi Du · Tianfan Fu · Hanchen Wang · Anima Anandkumar · Yoshua Bengio · Anthony Gitter · Carla Gomes · Aviv Regev · Max Welling · Marinka Zitnik -
2022 Poster: Characterizing Datapoints via Second-Split Forgetting »
Pratyush Maini · Saurabh Garg · Zachary Lipton · J. Zico Kolter -
2022 Poster: Supervised Training of Conditional Monge Maps »
Charlotte Bunne · Andreas Krause · Marco Cuturi -
2022 Poster: Learning Options via Compression »
Yiding Jiang · Evan Liu · Benjamin Eysenbach · J. Zico Kolter · Chelsea Finn -
2022 Poster: Kernel Interpolation with Sparse Grids »
Mohit Yadav · Daniel Sheldon · Cameron Musco -
2022 Poster: Near-Optimal Multi-Agent Learning for Safe Coverage Control »
Manish Prajapat · Matteo Turchetta · Melanie Zeilinger · Andreas Krause -
2022 Poster: Amortized Inference for Causal Structure Learning »
Lars Lorch · Scott Sussex · Jonas Rothfuss · Andreas Krause · Bernhard Schölkopf -
2022 Poster: Movement Penalized Bayesian Optimization with Application to Wind Energy Systems »
Shyam Sundhar Ramesh · Pier Giuseppe Sessa · Andreas Krause · Ilija Bogunovic -
2022 Poster: Experimental Design for Linear Functionals in Reproducing Kernel Hilbert Spaces »
Mojmir Mutny · Andreas Krause -
2022 Poster: Graph Neural Network Bandits »
Parnian Kassraie · Andreas Krause · Ilija Bogunovic -
2022 Poster: Left Heavy Tails and the Effectiveness of the Policy and Value Networks in DNN-based best-first search for Sokoban Planning »
Dieqiao Feng · Carla Gomes · Bart Selman -
2022 Poster: Efficiently Computing Local Lipschitz Constants of Neural Networks via Bound Propagation »
Zhouxing Shi · Yihan Wang · Huan Zhang · J. Zico Kolter · Cho-Jui Hsieh -
2022 Poster: Test Time Adaptation via Conjugate Pseudo-labels »
Sachin Goyal · Mingjie Sun · Aditi Raghunathan · J. Zico Kolter -
2022 Poster: Active Bayesian Causal Inference »
Christian Toth · Lars Lorch · Christian Knoll · Andreas Krause · Franz Pernkopf · Robert Peharz · Julius von Kügelgen -
2022 Poster: Deep Equilibrium Approaches to Diffusion Models »
Ashwini Pokle · Zhengyang Geng · J. Zico Kolter -
2022 Poster: Agreement-on-the-line: Predicting the Performance of Neural Networks under Distribution Shift »
Christina Baek · Yiding Jiang · Aditi Raghunathan · J. Zico Kolter -
2022 Poster: General Cutting Planes for Bound-Propagation-Based Neural Network Verification »
Huan Zhang · Shiqi Wang · Kaidi Xu · Linyi Li · Bo Li · Suman Jana · Cho-Jui Hsieh · J. Zico Kolter -
2022 Poster: A Robust Phased Elimination Algorithm for Corruption-Tolerant Gaussian Process Bandits »
Ilija Bogunovic · Zihan Li · Andreas Krause · Jonathan Scarlett -
2022 Poster: Active Exploration for Inverse Reinforcement Learning »
David Lindner · Andreas Krause · Giorgia Ramponi -
2022 Poster: Path Independent Equilibrium Models Can Better Exploit Test-Time Computation »
Cem Anil · Ashwini Pokle · Kaiqu Liang · Johannes Treutlein · Yuhuai Wu · Shaojie Bai · J. Zico Kolter · Roger Grosse -
2022 Poster: The Pitfalls of Regularization in Off-Policy TD Learning »
Gaurav Manek · J. Zico Kolter -
2022 Poster: Learning Long-Term Crop Management Strategies with CyclesGym »
Matteo Turchetta · Luca Corinzia · Scott Sussex · Amanda Burton · Juan Herrera · Ioannis Athanasiadis · Joachim M Buhmann · Andreas Krause -
2021 : Panel B: Safe Learning and Decision Making in Uncertain and Unstructured Environments »
Yisong Yue · J. Zico Kolter · Ivan Dario D Jimenez Rodriguez · Dragos Margineantu · Animesh Garg · Melissa Greeff -
2021 : Enforcing Robustness for Neural Network Policies »
J. Zico Kolter -
2021 : A GNN-RNN Approach for Harnessing Geospatial and Temporal Information: Application to Crop Yield Prediction »
Joshua Fan · Junwen Bai · Zhiyun Li · Ariel Ortiz-Bobea · Carla Gomes -
2021 : Resolving Super Fine-Resolution SIF via Coarsely-Supervised U-Net Regression »
Joshua Fan · Di Chen · Jiaming Wen · Ying Sun · Carla Gomes -
2021 : Meta-Learning Reliable Priors in the Function Space »
Jonas Rothfuss · Dominique Heyn · jinfan Chen · Andreas Krause -
2021 Poster: Learning Graph Models for Retrosynthesis Prediction »
Vignesh Ram Somnath · Charlotte Bunne · Connor Coley · Andreas Krause · Regina Barzilay -
2021 Poster: Risk-averse Heteroscedastic Bayesian Optimization »
Anastasia Makarova · Ilnura Usmanova · Ilija Bogunovic · Andreas Krause -
2021 Poster: Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Neural Network Robustness Verification »
Shiqi Wang · Huan Zhang · Kaidi Xu · Xue Lin · Suman Jana · Cho-Jui Hsieh · J. Zico Kolter -
2021 Poster: Hierarchical Skills for Efficient Exploration »
Jonas Gehring · Gabriel Synnaeve · Andreas Krause · Nicolas Usunier -
2021 Poster: Towards Deeper Deep Reinforcement Learning with Spectral Normalization »
Nils Bjorck · Carla Gomes · Kilian Weinberger -
2021 Poster: Contrastively Disentangled Sequential Variational Autoencoder »
Junwen Bai · Weiran Wang · Carla Gomes -
2021 Poster: Joint inference and input optimization in equilibrium networks »
Swaminathan Gurumurthy · Shaojie Bai · Zachary Manchester · J. Zico Kolter -
2021 Poster: $(\textrm{Implicit})^2$: Implicit Layers for Implicit Representations »
Zhichun Huang · Shaojie Bai · J. Zico Kolter -
2021 Poster: Relaxed Marginal Consistency for Differentially Private Query Answering »
Ryan McKenna · Siddhant Pradhan · Daniel Sheldon · Gerome Miklau -
2021 Poster: Multi-Scale Representation Learning on Proteins »
Vignesh Ram Somnath · Charlotte Bunne · Andreas Krause -
2021 Poster: Boosted CVaR Classification »
Runtian Zhai · Chen Dan · Arun Suggala · J. Zico Kolter · Pradeep Ravikumar -
2021 Poster: Learning Stable Deep Dynamics Models for Partially Observed or Delayed Dynamical Systems »
Andreas Schlaginhaufen · Philippe Wenk · Andreas Krause · Florian Dorfler -
2021 Poster: Information Directed Reward Learning for Reinforcement Learning »
David Lindner · Matteo Turchetta · Sebastian Tschiatschek · Kamil Ciosek · Andreas Krause -
2021 Poster: Robust Generalization despite Distribution Shift via Minimum Discriminating Information »
Tobias Sutter · Andreas Krause · Daniel Kuhn -
2021 Poster: Near-Optimal Multi-Perturbation Experimental Design for Causal Structure Learning »
Scott Sussex · Caroline Uhler · Andreas Krause -
2021 Poster: Training Certifiably Robust Neural Networks with Efficient Local Lipschitz Bounds »
Yujia Huang · Huan Zhang · Yuanyuan Shi · J. Zico Kolter · Anima Anandkumar -
2021 Poster: Distributional Gradient Matching for Learning Uncertain Neural Dynamics Models »
Lenart Treven · Philippe Wenk · Florian Dorfler · Andreas Krause -
2021 Poster: Meta-Learning Reliable Priors in the Function Space »
Jonas Rothfuss · Dominique Heyn · jinfan Chen · Andreas Krause -
2021 Poster: Adversarially robust learning for security-constrained optimal power flow »
Priya Donti · Aayushya Agarwal · Neeraj Vijay Bedmutha · Larry Pileggi · J. Zico Kolter -
2021 Poster: Robustness between the worst and average case »
Leslie Rice · Anna Bair · Huan Zhang · J. Zico Kolter -
2021 Poster: Monte Carlo Tree Search With Iteratively Refining State Abstractions »
Samuel Sokota · Caleb Y Ho · Zaheen Ahmad · J. Zico Kolter -
2021 Poster: Misspecified Gaussian Process Bandit Optimization »
Ilija Bogunovic · Andreas Krause -
2021 Poster: DiBS: Differentiable Bayesian Structure Learning »
Lars Lorch · Jonas Rothfuss · Bernhard Schölkopf · Andreas Krause -
2021 Poster: Regret Bounds for Gaussian-Process Optimization in Large Domains »
Manuel Wuethrich · Bernhard Schölkopf · Andreas Krause -
2020 : Mini-panel discussion 3 - Prioritizing Real World RL Challenges »
Chelsea Finn · Thomas Dietterich · Angela Schoellig · Anca Dragan · Anusha Nagabandi · Doina Precup -
2020 : Keynote: Tom Diettrich »
Thomas Dietterich -
2020 : Invited Talk (Zico Kolter) »
J. Zico Kolter -
2020 Workshop: Machine Learning for Engineering Modeling, Simulation and Design »
Alex Beatson · Priya Donti · Amira Abdel-Rahman · Stephan Hoyer · Rose Yu · J. Zico Kolter · Ryan Adams -
2020 : Keynote by Zico Kolter »
J. Zico Kolter -
2020 : Invited speaker: Adaptive Sampling for Stochastic Risk-Averse Learning, Andreas Krause »
Andreas Krause -
2020 Poster: Community detection using fast low-cardinality semidefinite programming
»
Po-Wei Wang · J. Zico Kolter -
2020 Poster: Deep Archimedean Copulas »
Chun Kai Ling · Fei Fang · J. Zico Kolter -
2020 Poster: Advances in Black-Box VI: Normalizing Flows, Importance Weighting, and Optimization »
Abhinav Agrawal · Daniel Sheldon · Justin Domke -
2020 Poster: Adaptive Sampling for Stochastic Risk-Averse Learning »
Sebastian Curi · Kfir Y. Levy · Stefanie Jegelka · Andreas Krause -
2020 Poster: Contextual Games: Multi-Agent Learning with Side Information »
Pier Giuseppe Sessa · Ilija Bogunovic · Andreas Krause · Maryam Kamgarpour -
2020 Tutorial: (Track3) Deep Implicit Layers: Neural ODEs, Equilibrium Models, and Differentiable Optimization Q&A »
David Duvenaud · J. Zico Kolter · Matthew Johnson -
2020 Poster: Coresets via Bilevel Optimization for Continual Learning and Streaming »
Zalan Borsos · Mojmir Mutny · Andreas Krause -
2020 Poster: Gradient Estimation with Stochastic Softmax Tricks »
Max Paulus · Dami Choi · Danny Tarlow · Andreas Krause · Chris Maddison -
2020 Poster: Efficient semidefinite-programming-based inference for binary and multi-class MRFs »
Chirag Pabbaraju · Po-Wei Wang · J. Zico Kolter -
2020 Spotlight: Efficient semidefinite-programming-based inference for binary and multi-class MRFs »
Chirag Pabbaraju · Po-Wei Wang · J. Zico Kolter -
2020 Oral: Gradient Estimation with Stochastic Softmax Tricks »
Max Paulus · Dami Choi · Danny Tarlow · Andreas Krause · Chris Maddison -
2020 Poster: A Novel Automated Curriculum Strategy to Solve Hard Sokoban Planning Instances »
Dieqiao Feng · Carla Gomes · Bart Selman -
2020 Poster: Permute-and-Flip: A new mechanism for differentially private selection »
Ryan McKenna · Daniel Sheldon -
2020 Poster: Multiscale Deep Equilibrium Models »
Shaojie Bai · Vladlen Koltun · J. Zico Kolter -
2020 Poster: Denoised Smoothing: A Provable Defense for Pretrained Classifiers »
Hadi Salman · Mingjie Sun · Greg Yang · Ashish Kapoor · J. Zico Kolter -
2020 Poster: Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and Planning »
Sebastian Curi · Felix Berkenkamp · Andreas Krause -
2020 Poster: Learning to Play Sequential Games versus Unknown Opponents »
Pier Giuseppe Sessa · Ilija Bogunovic · Maryam Kamgarpour · Andreas Krause -
2020 Poster: Monotone operator equilibrium networks »
Ezra Winston · J. Zico Kolter -
2020 Spotlight: Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and Planning »
Sebastian Curi · Felix Berkenkamp · Andreas Krause -
2020 Spotlight: Permute-and-Flip: A new mechanism for differentially private selection »
Ryan McKenna · Daniel Sheldon -
2020 Spotlight: Monotone operator equilibrium networks »
Ezra Winston · J. Zico Kolter -
2020 Oral: Multiscale Deep Equilibrium Models »
Shaojie Bai · Vladlen Koltun · J. Zico Kolter -
2020 Poster: Safe Reinforcement Learning via Curriculum Induction »
Matteo Turchetta · Andrey Kolobov · Shital Shah · Andreas Krause · Alekh Agarwal -
2020 Spotlight: Safe Reinforcement Learning via Curriculum Induction »
Matteo Turchetta · Andrey Kolobov · Shital Shah · Andreas Krause · Alekh Agarwal -
2020 Tutorial: (Track3) Deep Implicit Layers: Neural ODEs, Equilibrium Models, and Differentiable Optimization »
David Duvenaud · J. Zico Kolter · Matthew Johnson -
2019 : AI and Sustainable Development »
Fei Fang · Carla Gomes · Miguel Luengo-Oroz · Thomas Dietterich · Julien Cornebise -
2019 : Automated Quality Control for a Weather Sensor Network »
Thomas Dietterich -
2019 : Carla Gomes (Cornell) »
Carla Gomes -
2019 : Climate Change: A Grand Challenge for ML »
Yoshua Bengio · Carla Gomes · Andrew Ng · Jeff Dean · Lester Mackey -
2019 : Computational Sustainability: Computing for a Better World and a Sustainable Future »
Carla Gomes -
2019 : Outstanding Contribution Talk: Variational Graph Convolutional Networks »
Edwin Bonilla -
2019 Poster: Efficiently Learning Fourier Sparse Set Functions »
Andisheh Amrollahi · Amir Zandieh · Michael Kapralov · Andreas Krause -
2019 Spotlight: Efficiently Learning Fourier Sparse Set Functions »
Andisheh Amrollahi · Amir Zandieh · Michael Kapralov · Andreas Krause -
2019 Poster: Learning Stable Deep Dynamics Models »
J. Zico Kolter · Gaurav Manek -
2019 Poster: Stochastic Bandits with Context Distributions »
Johannes Kirschner · Andreas Krause -
2019 Poster: Adversarial Music: Real world Audio Adversary against Wake-word Detection System »
Juncheng Li · Shuhui Qu · Xinjian Li · Joseph Szurley · J. Zico Kolter · Florian Metze -
2019 Spotlight: Adversarial Music: Real world Audio Adversary against Wake-word Detection System »
Juncheng Li · Shuhui Qu · Xinjian Li · Joseph Szurley · J. Zico Kolter · Florian Metze -
2019 Poster: Differentiable Convex Optimization Layers »
Akshay Agrawal · Brandon Amos · Shane Barratt · Stephen Boyd · Steven Diamond · J. Zico Kolter -
2019 Poster: A Domain Agnostic Measure for Monitoring and Evaluating GANs »
Paulina Grnarova · Kfir Y. Levy · Aurelien Lucchi · Nathanael Perraudin · Ian Goodfellow · Thomas Hofmann · Andreas Krause -
2019 Poster: Structured Variational Inference in Continuous Cox Process Models »
Virginia Aglietti · Edwin Bonilla · Theodoros Damoulas · Sally Cripps -
2019 Poster: No-Regret Learning in Unknown Games with Correlated Payoffs »
Pier Giuseppe Sessa · Ilija Bogunovic · Maryam Kamgarpour · Andreas Krause -
2019 Poster: Teaching Multiple Concepts to a Forgetful Learner »
Anette Hunziker · Yuxin Chen · Oisin Mac Aodha · Manuel Gomez Rodriguez · Andreas Krause · Pietro Perona · Yisong Yue · Adish Singla -
2019 Poster: Adaptive Sequence Submodularity »
Marko Mitrovic · Ehsan Kazemi · Moran Feldman · Andreas Krause · Amin Karbasi -
2019 Poster: Divide and Couple: Using Monte Carlo Variational Objectives for Posterior Approximation »
Justin Domke · Daniel Sheldon -
2019 Spotlight: Divide and Couple: Using Monte Carlo Variational Objectives for Posterior Approximation »
Justin Domke · Daniel Sheldon -
2019 Poster: Uniform convergence may be unable to explain generalization in deep learning »
Vaishnavh Nagarajan · J. Zico Kolter -
2019 Poster: Differentially Private Bayesian Linear Regression »
Garrett Bernstein · Daniel Sheldon -
2019 Poster: Safe Exploration for Interactive Machine Learning »
Matteo Turchetta · Felix Berkenkamp · Andreas Krause -
2019 Poster: Deep Equilibrium Models »
Shaojie Bai · J. Zico Kolter · Vladlen Koltun -
2019 Spotlight: Deep Equilibrium Models »
Shaojie Bai · J. Zico Kolter · Vladlen Koltun -
2019 Oral: Uniform convergence may be unable to explain generalization in deep learning »
Vaishnavh Nagarajan · J. Zico Kolter -
2018 : Talk 1: Zico Kolter - Differentiable Physics and Control »
J. Zico Kolter -
2018 Poster: Differentiable MPC for End-to-end Planning and Control »
Brandon Amos · Ivan Jimenez · Jacob I Sacks · Byron Boots · J. Zico Kolter -
2018 Poster: Provable Variational Inference for Constrained Log-Submodular Models »
Josip Djolonga · Stefanie Jegelka · Andreas Krause -
2018 Poster: Differentially Private Bayesian Inference for Exponential Families »
Garrett Bernstein · Daniel Sheldon -
2018 Poster: Understanding Batch Normalization »
Johan Bjorck · Carla Gomes · Bart Selman · Kilian Weinberger -
2018 Poster: Efficient High Dimensional Bayesian Optimization with Additivity and Quadrature Fourier Features »
Mojmir Mutny · Andreas Krause -
2018 Spotlight: Efficient High Dimensional Bayesian Optimization with Additivity and Quadrature Fourier Features »
Mojmir Mutny · Andreas Krause -
2018 Poster: Fairness Behind a Veil of Ignorance: A Welfare Analysis for Automated Decision Making »
Hoda Heidari · Claudio Ferrari · Krishna Gummadi · Andreas Krause -
2018 Poster: Importance Weighting and Variational Inference »
Justin Domke · Daniel Sheldon -
2018 Poster: End-to-End Differentiable Physics for Learning and Control »
Filipe de Avila Belbute Peres · Kevin Smith · Kelsey Allen · Josh Tenenbaum · J. Zico Kolter -
2018 Spotlight: End-to-End Differentiable Physics for Learning and Control »
Filipe de Avila Belbute Peres · Kevin Smith · Kelsey Allen · Josh Tenenbaum · J. Zico Kolter -
2018 Poster: Inferring Latent Velocities from Weather Radar Data using Gaussian Processes »
Rico Angell · Daniel Sheldon -
2018 Poster: Scaling provable adversarial defenses »
Eric Wong · Frank Schmidt · Jan Hendrik Metzen · J. Zico Kolter -
2018 Tutorial: Adversarial Robustness: Theory and Practice »
J. Zico Kolter · Aleksander Madry -
2017 : Invited talk: Towards Safe Bayesian Optimization »
Andreas Krause -
2017 : Provable defenses against adversarial examples via the convex outer adversarial polytope »
J. Zico Kolter -
2017 Workshop: Discrete Structures in Machine Learning »
Yaron Singer · Jeff A Bilmes · Andreas Krause · Stefanie Jegelka · Amin Karbasi -
2017 Poster: Interactive Submodular Bandit »
Lin Chen · Andreas Krause · Amin Karbasi -
2017 Poster: Gradient descent GAN optimization is locally stable »
Vaishnavh Nagarajan · J. Zico Kolter -
2017 Oral: Gradient descent GAN optimization is locally stable »
Vaishnavh Nagarajan · J. Zico Kolter -
2017 Poster: Safe Model-based Reinforcement Learning with Stability Guarantees »
Felix Berkenkamp · Matteo Turchetta · Angela Schoellig · Andreas Krause -
2017 Poster: Differentiable Learning of Submodular Functions »
Josip Djolonga · Andreas Krause -
2017 Spotlight: Differentiable Learning of Submodular Functions »
Josip Djolonga · Andreas Krause -
2017 Poster: Non-monotone Continuous DR-submodular Maximization: Structure and Algorithms »
Yatao Bian · Kfir Levy · Andreas Krause · Joachim M Buhmann -
2017 Poster: Task-based End-to-end Model Learning in Stochastic Optimization »
Priya Donti · J. Zico Kolter · Brandon Amos -
2017 Poster: Stochastic Submodular Maximization: The Case of Coverage Functions »
Mohammad Karimi · Mario Lucic · Hamed Hassani · Andreas Krause -
2016 : Automated Data Cleaning via Multi-View Anomaly Detection »
Thomas Dietterich -
2016 Poster: The Multiple Quantile Graphical Model »
Alnur Ali · J. Zico Kolter · Ryan Tibshirani -
2016 Poster: Variational Inference in Mixed Probabilistic Submodular Models »
Josip Djolonga · Sebastian Tschiatschek · Andreas Krause -
2016 Poster: Truncated Variance Reduction: A Unified Approach to Bayesian Optimization and Level-Set Estimation »
Ilija Bogunovic · Jonathan Scarlett · Andreas Krause · Volkan Cevher -
2016 Poster: Solving Marginal MAP Problems with NP Oracles and Parity Constraints »
Yexiang Xue · zhiyuan li · Stefano Ermon · Carla Gomes · Bart Selman -
2016 Poster: Probabilistic Inference with Generating Functions for Poisson Latent Variable Models »
Kevin Winner · Daniel Sheldon -
2016 Poster: Cooperative Graphical Models »
Josip Djolonga · Stefanie Jegelka · Sebastian Tschiatschek · Andreas Krause -
2016 Poster: Fast and Provably Good Seedings for k-Means »
Olivier Bachem · Mario Lucic · Hamed Hassani · Andreas Krause -
2016 Oral: Fast and Provably Good Seedings for k-Means »
Olivier Bachem · Mario Lucic · Hamed Hassani · Andreas Krause -
2016 Poster: Safe Exploration in Finite Markov Decision Processes with Gaussian Processes »
Matteo Turchetta · Felix Berkenkamp · Andreas Krause -
2015 : Safe Exploration for Bayesian Optimization »
Andreas Krause -
2015 Poster: Distributed Submodular Cover: Succinctly Summarizing Massive Data »
Baharan Mirzasoleiman · Amin Karbasi · Ashwinkumar Badanidiyuru · Andreas Krause -
2015 Poster: Sampling from Probabilistic Submodular Models »
Alkis Gotovos · Hamed Hassani · Andreas Krause -
2015 Spotlight: Distributed Submodular Cover: Succinctly Summarizing Massive Data »
Baharan Mirzasoleiman · Amin Karbasi · Ashwinkumar Badanidiyuru · Andreas Krause -
2015 Oral: Sampling from Probabilistic Submodular Models »
Alkis Gotovos · Hamed Hassani · Andreas Krause -
2015 Poster: Scalable Inference for Gaussian Process Models with Black-Box Likelihoods »
Amir Dezfouli · Edwin Bonilla -
2014 Workshop: NIPS’14 Workshop on Crowdsourcing and Machine Learning »
David Parkes · Denny Zhou · Chien-Ju Ho · Nihar Bhadresh Shah · Adish Singla · Jared Heyman · Edwin Simpson · Andreas Krause · Rafael Frongillo · Jennifer Wortman Vaughan · Panagiotis Papadimitriou · Damien Peters -
2014 Workshop: Discrete Optimization in Machine Learning »
Jeffrey A Bilmes · Andreas Krause · Stefanie Jegelka · S Thomas McCormick · Sebastian Nowozin · Yaron Singer · Dhruv Batra · Volkan Cevher -
2014 Workshop: 3rd NIPS Workshop on Probabilistic Programming »
Daniel Roy · Josh Tenenbaum · Thomas Dietterich · Stuart J Russell · YI WU · Ulrik R Beierholm · Alp Kucukelbir · Zenna Tavares · Yura Perov · Daniel Lee · Brian Ruttenberg · Sameer Singh · Michael Hughes · Marco Gaboardi · Alexey Radul · Vikash Mansinghka · Frank Wood · Sebastian Riedel · Prakash Panangaden -
2014 Workshop: From Bad Models to Good Policies (Sequential Decision Making under Uncertainty) »
Odalric-Ambrym Maillard · Timothy A Mann · Shie Mannor · Jeremie Mary · Laurent Orseau · Thomas Dietterich · Ronald Ortner · Peter Grünwald · Joelle Pineau · Raphael Fonteneau · Georgios Theocharous · Esteban D Arcaute · Christos Dimitrakakis · Nan Jiang · Doina Precup · Pierre-Luc Bacon · Marek Petrik · Aviv Tamar -
2014 Poster: Extended and Unscented Gaussian Processes »
Daniel M Steinberg · Edwin Bonilla -
2014 Poster: Efficient Sampling for Learning Sparse Additive Models in High Dimensions »
Hemant Tyagi · Bernd Gärtner · Andreas Krause -
2014 Poster: From MAP to Marginals: Variational Inference in Bayesian Submodular Models »
Josip Djolonga · Andreas Krause -
2014 Poster: Stochastic Network Design in Bidirected Trees »
Xiaojian Wu · Daniel Sheldon · Shlomo Zilberstein -
2014 Spotlight: Extended and Unscented Gaussian Processes »
Daniel M Steinberg · Edwin Bonilla -
2014 Poster: Automated Variational Inference for Gaussian Process Models »
Trung V Nguyen · Edwin Bonilla -
2014 Poster: Efficient Partial Monitoring with Prior Information »
Hastagiri P Vanchinathan · Gábor Bartók · Andreas Krause -
2013 Workshop: Bayesian Optimization in Theory and Practice »
Matthew Hoffman · Jasper Snoek · Nando de Freitas · Michael A Osborne · Ryan Adams · Sebastien Bubeck · Philipp Hennig · Remi Munos · Andreas Krause -
2013 Workshop: Discrete Optimization in Machine Learning: Connecting Theory and Practice »
Stefanie Jegelka · Andreas Krause · Pradeep Ravikumar · Kazuo Murota · Jeffrey A Bilmes · Yisong Yue · Michael Jordan -
2013 Poster: High-Dimensional Gaussian Process Bandits »
Josip Djolonga · Andreas Krause · Volkan Cevher -
2013 Poster: Distributed Submodular Maximization: Identifying Representative Elements in Massive Data »
Baharan Mirzasoleiman · Amin Karbasi · Rik Sarkar · Andreas Krause -
2013 Poster: Embed and Project: Discrete Sampling with Universal Hashing »
Stefano Ermon · Carla Gomes · Ashish Sabharwal · Bart Selman -
2012 Workshop: Human Computation for Science and Computational Sustainability »
Theodoros Damoulas · Thomas Dietterich · Edith Law · Serge Belongie -
2012 Workshop: Discrete Optimization in Machine Learning (DISCML): Structure and Scalability »
Stefanie Jegelka · Andreas Krause · Jeffrey A Bilmes · Pradeep Ravikumar -
2012 Poster: Probabilistic Topic Coding for Superset Label Learning »
Liping Liu · Thomas Dietterich -
2012 Invited Talk: Challenges for Machine Learning in Computational Sustainability »
Thomas Dietterich -
2012 Poster: Density Propagation and Improved Bounds on the Partition Function »
Stefano Ermon · Carla Gomes · Ashish Sabharwal · Bart Selman -
2011 Workshop: Machine Learning for Sustainability »
Thomas Dietterich · J. Zico Kolter · Matthew A Brown -
2011 Workshop: Discrete Optimization in Machine Learning (DISCML): Uncertainty, Generalization and Feedback »
Andreas Krause · Pradeep Ravikumar · Stefanie S Jegelka · Jeffrey A Bilmes -
2011 Oral: Scalable Training of Mixture Models via Coresets »
Dan Feldman · Matthew Faulkner · Andreas Krause -
2011 Poster: Improving Topic Coherence with Regularized Topic Models »
David Newman · Edwin Bonilla · Wray Buntine -
2011 Poster: Scalable Training of Mixture Models via Coresets »
Dan Feldman · Matthew Faulkner · Andreas Krause -
2011 Poster: Accelerated Adaptive Markov Chain for Partition Function Computation »
Stefano Ermon · Carla Gomes · Ashish Sabharwal · Bart Selman -
2011 Poster: Contextual Gaussian Process Bandit Optimization »
Andreas Krause · Cheng Soon Ong -
2011 Poster: Crowdclustering »
Ryan G Gomes · Peter Welinder · Andreas Krause · Pietro Perona -
2011 Poster: The Fixed Points of Off-Policy TD »
J. Zico Kolter -
2011 Spotlight: Accelerated Adaptive Markov Chain for Partition Function Computation »
Stefano Ermon · Carla Gomes · Ashish Sabharwal · Bart Selman -
2011 Spotlight: The Fixed Points of Off-Policy TD »
J. Zico Kolter -
2011 Poster: Collective Graphical Models »
Daniel Sheldon · Thomas Dietterich -
2011 Poster: Inverting Grice's Maxims to Learn Rules from Natural Language Extractions »
M. Shahed Sorower · Thomas Dietterich · Janardhan Rao Doppa · Walker Orr · Prasad Tadepalli · Xiaoli Fern -
2010 Workshop: Discrete Optimization in Machine Learning: Structures, Algorithms and Applications »
Andreas Krause · Pradeep Ravikumar · Jeffrey A Bilmes · Stefanie Jegelka -
2010 Spotlight: Efficient Minimization of Decomposable Submodular Functions »
Peter G Stobbe · Andreas Krause -
2010 Poster: Discriminative Clustering by Regularized Information Maximization »
Ryan G Gomes · Andreas Krause · Pietro Perona -
2010 Poster: Efficient Minimization of Decomposable Submodular Functions »
Peter G Stobbe · Andreas Krause -
2010 Poster: Gaussian Process Preference Elicitation »
Edwin Bonilla · Shengbo Guo · Scott Sanner -
2010 Poster: Near-Optimal Bayesian Active Learning with Noisy Observations »
Daniel Golovin · Andreas Krause · Debajyoti Ray -
2010 Poster: Energy Disaggregation via Discriminative Sparse Coding »
J. Zico Kolter · Siddarth Batra · Andrew Y Ng -
2009 Workshop: Discrete Optimization in Machine Learning: Submodularity, Polyhedra and Sparsity »
Andreas Krause · Pradeep Ravikumar · Jeffrey A Bilmes -
2009 Mini Symposium: Machine Learning for Sustainability »
J. Zico Kolter · Thomas Dietterich · Andrew Y Ng -
2009 Poster: Online Learning of Assignments »
Matthew Streeter · Daniel Golovin · Andreas Krause -
2009 Spotlight: Online Learning of Assignments »
Matthew Streeter · Daniel Golovin · Andreas Krause -
2007 Poster: Multi-task Gaussian Process Prediction »
Edwin Bonilla · Kian Ming A Chai · Chris Williams -
2007 Spotlight: Multi-task Gaussian Process Prediction »
Edwin Bonilla · Kian Ming A Chai · Chris Williams -
2007 Spotlight: Selecting Observations against Adversarial Objectives »
Andreas Krause · H. Brendan McMahan · Carlos Guestrin · Anupam Gupta -
2007 Spotlight: Hierarchical Apprenticeship Learning with Application to Quadruped Locomotion »
J. Zico Kolter · Pieter Abbeel · Andrew Y Ng -
2007 Spotlight: Collective Inference on Markov Models for Modeling Bird Migration »
Daniel Sheldon · M.A. Saleh Elmohamed · Dexter Kozen -
2007 Poster: Collective Inference on Markov Models for Modeling Bird Migration »
Daniel Sheldon · M.A. Saleh Elmohamed · Dexter Kozen -
2007 Poster: Selecting Observations against Adversarial Objectives »
Andreas Krause · H. Brendan McMahan · Carlos Guestrin · Anupam Gupta -
2007 Poster: Hierarchical Apprenticeship Learning with Application to Quadruped Locomotion »
J. Zico Kolter · Pieter Abbeel · Andrew Y Ng -
2006 Poster: Near-Uniform Sampling of Combinatorial Spaces Using XOR Constraints »
Carla Gomes · Ashish Sabharwal · Bart Selman