The International Conference on Machine Learning (ICML) 2021 is being hosted virtually from July 18th – July 24th. We’re excited to share all the work from SAIL that’s being presented, and you’ll find links to papers, videos and blogs below. Feel free to reach out to the contact authors directly to learn more about the work that’s happening at Stanford!
List of Accepted Papers
Deep Reinforcement Learning amidst Continual Structured Non-Stationarity
Authors: Annie Xie, James Harrison, Chelsea Finn
Contact: anniexie@stanford.edu
Keywords: deep reinforcement learning, non-stationarity
Just Train Twice: Improving Group Robustness without Training Group Information
Authors: Evan Zheran Liu*, Behzad Haghgoo*, Annie S. Chen*, Aditi Raghunathan, Pang Wei Koh, Shiori Sagawa, Percy Liang, Chelsea Finn
Contact: evanliu@cs.stanford.edu
Links: Paper | Video
Keywords: robustness, spurious correlations
A theory of high dimensional regression with arbitrary correlations between input features and target functions: sample complexity, multiple descent curves and a hierarchy of phase transitions
Authors: Gabriel Mel, Surya Ganguli
Contact: sganguli@stanford.edu
Links: Paper
Keywords: high dimensional statistics, random matrix theory, regularization
Accelerating Feedforward Computation via Parallel Nonlinear Equation Solving
Authors: Yang Song, Chenlin Meng, Renjie Liao, Stefano Ermon
Contact: songyang@stanford.edu
Links: Paper | Website
Keywords: parallel computing, autoregressive models, densenets, rnns
Accuracy on the Line: on the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization
Authors: John Miller, Rohan Taori, Aditi Raghunathan, Shiori Sagawa, Pang Wei Koh, Vaishaal Shankar, Percy Liang, Yair Carmon, Ludwig Schmidt
Contact: rtaori@stanford.edu
Links: Paper
Keywords: out of distribution, generalization, robustness, distribution shift, machine learning
Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual Information
Authors: Willie Neiswanger, Ke Alexander Wang, Stefano Ermon
Contact: neiswanger@cs.stanford.edu
Links: Paper | Blog Post | Video | Website
Keywords: bayesian optimization, experimental design, algorithm execution, information theory
Break-It-Fix-It: Unsupervised Learning for Program Repair
Authors: Michihiro Yasunaga, Percy Liang
Contact: myasu@cs.stanford.edu
Links: Paper | Website
Keywords: program repair, unsupervised learning, translation, domain adaptation, self-supervised learning
Composed Fine-Tuning: Freezing Pre-Trained Denoising Autoencoders for Improved Generalization
Authors: Sang Michael Xie, Tengyu Ma, Percy Liang
Contact: xie@cs.stanford.edu
Links: Paper | Website
Keywords: fine-tuning, adaptation, freezing, ood generalization, structured prediction, semi-supervised learning, unlabeled outputs
Decoupling Exploration and Exploitation for Meta-Reinforcement Learning without Sacrifices
Authors: Evan Zheran Liu, Aditi Raghunathan, Percy Liang, Chelsea Finn
Contact: evanliu@cs.stanford.edu
Links: Paper | Blog Post | Video | Website
Keywords: meta-reinforcement learning, exploration
Exponential Lower Bounds for Batch Reinforcement Learning: Batch RL can be Exponentially Harder than Online RL
Authors: Andrea Zanette
Contact: zanette@stanford.edu
Links: Paper | Video
Keywords: reinforcement learning, lower bounds, linear value functions, off-policy evaluation, policy learning
Federated Composite Optimization
Authors: Honglin Yuan, Manzil Zaheer, Sashank Reddi
Contact: yuanhl@cs.stanford.edu
Links: Paper | Video | Website
Keywords: federated learning, distributed optimization, convex optimization
Generative Adversarial Transformers
Authors: Drew A. Hudson, C. Lawrence Zitnick
Contact: dorarad@stanford.edu
Links: Paper | Website
Keywords: gans, transformers, compositionality, attention, bottom-up, top-down, disentanglement, object-oriented, representation learning, scenes
Improving Generalization in Meta-learning via Task Augmentation
Authors: Huaxiu Yao, Longkai Huang, Linjun Zhang, Ying Wei, Li Tian, James Zou, Junzhou Huang, Zhenhui Li
Contact: huaxiu@cs.stanford.edu
Links: Paper
Keywords: meta-learning
Mandoline: Model Evaluation under Distribution Shift
Authors: Mayee Chen, Karan Goel, Nimit Sohoni, Fait Poms, Kayvon Fatahalian, Christopher Ré
Contact: mfchen@stanford.edu
Links: Paper
Keywords: evaluation, distribution shift, importance weighting
Memory-Efficient Pipeline-Parallel DNN Training
Authors: Deepak Narayanan
Contact: deepakn@stanford.edu
Links: Paper
Keywords: distributed training, pipeline model parallelism, large language model training
Offline Meta-Reinforcement Learning with Advantage Weighting
Authors: Eric Mitchell, Rafael Rafailov, Xue Bin Peng, Sergey Levine, Chelsea Finn
Contact: em7@stanford.edu
Links: Paper | Website
Keywords: meta-rl offline rl batch meta-learning
SECANT: Self-Expert Cloning for Zero-Shot Generalization of Visual Policies
Authors: Linxi Fan, Guanzhi Wang, De-An Huang, Zhiding Yu, Li Fei-Fei, Yuke Zhu, Anima Anandkumar
Contact: jimfan@cs.stanford.edu
Links: Paper | Website
Keywords: reinforcement learning, computer vision, sim-to-real, robotics, simulation
Targeted Data Acquisition for Evolving Negotiation Agents
Authors: Minae Kwon, Siddharth Karamcheti, Mariano-Florentino Cuéllar, Dorsa Sadigh
Contact: minae@cs.stanford.edu
Links: Paper | Video
Keywords: negotiation, targeted data acquisition, active learning
Understanding self-supervised Learning Dynamics without Contrastive Pairs
Authors: Yuandong Tian, Xinlei Chen, Surya Ganguli
Contact: sganguli@stanford.edu
Links: Paper
Keywords: self-supervised learning
WILDS: A Benchmark of in-the-Wild Distribution Shifts
Authors: Pang Wei Koh*, Shiori Sagawa*, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Irena Gao, Tony Lee, Etienne David, Ian Stavness, Wei Guo, Berton A. Earnshaw, Imran S. Haque, Sara Beery, Jure Leskovec, Anshul Kundaje, Emma Pierson, Sergey Levine, Chelsea Finn, Percy Liang
Contact: pangwei@cs.stanford.edu, ssagawa@cs.stanford.edu
Links: Paper | Website
Keywords: robustness, distribution shifts, benchmark
LEGO: Latent Execution-Guided Reasoning for Multi-Hop Question Answering on Knowledge Graphs
Authors: Hongyu Ren, Hanjun Dai, Bo Dai, Xinyun Chen, Michihiro Yasunaga, Haitian Sun, Dale Schuurmans, Jure Leskovec, Denny Zhou
Contact: hyren@cs.stanford.edu
Keywords: knowledge graphs, question answering, multi-hop reasoning
We look forward to seeing you at ICML 2021!