1 code implementation • 27 Oct 2024 • Chu Xin Cheng, Raul Astudillo, Thomas Desautels, Yisong Yue
We consider Bayesian algorithm execution (BAX), a framework for efficiently selecting evaluation points of an expensive function to infer a property of interest encoded as the output of a base algorithm.
1 code implementation • 7 Oct 2024 • Yue Song, Thomas Anderson Keller, Yisong Yue, Pietro Perona, Max Welling
In this paper we propose to learn representations from sequence data by factorizing the transformations of the latent variables into sparse components.
1 code implementation • 30 Sep 2024 • Christopher Yeh, Nicolas Christianson, Alan Wu, Adam Wierman, Yisong Yue
However, ensuring robustness guarantees requires well-calibrated uncertainty estimates, which can be difficult to achieve in high-capacity prediction models such as deep neural networks.
no code implementations • 30 Sep 2024 • Hongkai Zheng, Wenda Chu, Austin Wang, Nikola Kovachki, Ricardo Baptista, Yisong Yue
This reliance poses a substantial limitation that restricts their use in a wide range of problems where such information is unavailable, such as in many scientific applications.
no code implementations • 27 Aug 2024 • William Compton, Ivan Dario Jimenez Rodriguez, Noel Csomay-Shanklin, Yisong Yue, Aaron D. Ames
Stabilizing underactuated systems is an inherently challenging control task due to fundamental limitations on how the control input affects the unactuated dynamics.
1 code implementation • 20 Aug 2024 • Jonathan Light, Min Cai, Weiqin Chen, Guanzhi Wang, Xiusi Chen, Wei Cheng, Yisong Yue, Ziniu Hu
In this paper, we propose a new method STRATEGIST that utilizes LLMs to acquire new skills for playing multi-agent games through a self-improvement process.
no code implementations • 2 Jul 2024 • Fanzeng Xia, Hao liu, Yisong Yue, Tongxin Li
Our results reveal that LLMs, particularly GPT-4 Turbo, quickly identify the Condorcet winner, thus outperforming existing state-of-the-art algorithms in terms of weak regret.
no code implementations • 22 Jun 2024 • Zhengfei Zhang, Kishan Panaganti, Laixi Shi, Yanan Sui, Adam Wierman, Yisong Yue
We study the problem of Distributionally Robust Constrained RL (DRC-RL), where the goal is to maximize the expected reward subject to environmental distribution shifts and constraints.
1 code implementation • 21 Jun 2024 • Jason Yang, Ariane Mora, Shengchao Liu, Bruce J. Wittmann, Anima Anandkumar, Frances H. Arnold, Yisong Yue
We introduce CARE, a benchmark and dataset suite for the Classification And Retrieval of Enzymes (CARE).
no code implementations • 20 Jun 2024 • Raul Astudillo, Kejun Li, Maegan Tucker, Chu Xin Cheng, Aaron D. Ames, Yisong Yue
As a direct consequence, this result provides, to our knowledge, the first convergence guarantee for dueling Thompson sampling in the PBO setting.
1 code implementation • 6 Jun 2024 • Dan Zhang, Sining Zhoubian, Ziniu Hu, Yisong Yue, Yuxiao Dong, Jie Tang
We first show that the tree-search policy in ReST-MCTS* achieves higher accuracy compared with prior LLM reasoning baselines such as Best-of-N and Tree-of-Thought, within the same search budget.
1 code implementation • 5 Jun 2024 • Geeling Chau, Christopher Wang, Sabera Talukder, Vighnesh Subramaniam, Saraswati Soedarmadji, Yisong Yue, Boris Katz, Andrei Barbu
We present a self-supervised framework that learns population-level codes for arbitrary ensembles of neural recordings at scale.
1 code implementation • 4 Jun 2024 • Min Cai, Yuchen Zhang, Shichang Zhang, Fan Yin, Dan Zhang, Difan Zou, Yisong Yue, Ziniu Hu
Given a desired behavior expressed in a natural language suffix string concatenated to the input prompt, SelfControl computes gradients of the LLM's self-evaluation of the suffix with respect to its latent representations.
1 code implementation • 29 May 2024 • Zihui Wu, Yu Sun, Yifan Chen, Bingliang Zhang, Yisong Yue, Katherine L. Bouman
Diffusion models (DMs) have recently shown outstanding capabilities in modeling complex image distributions, making them expressive image priors for solving Bayesian inverse problems.
no code implementations • 2 Mar 2024 • Ziniu Hu, Ahmet Iscen, Aashi Jain, Thomas Kipf, Yisong Yue, David A. Ross, Cordelia Schmid, Alireza Fathi
SceneCraft first models a scene graph as a blueprint, detailing the spatial relationships among assets in the scene.
no code implementations • 28 Feb 2024 • Geeling Chau, Yujin An, Ahamed Raffey Iqbal, Soon-Jo Chung, Yisong Yue, Sabera Talukder
A major goal in neuroscience is to discover neural data representations that generalize.
1 code implementation • 26 Feb 2024 • Sabera Talukder, Yisong Yue, Georgia Gkioxari
To this end, we explore the impact of discrete, learnt, time series data representations that enable generalist, cross-domain training.
1 code implementation • 22 Feb 2024 • Yujia Huang, Adishree Ghatare, Yuanzhe Liu, Ziniu Hu, Qinsheng Zhang, Chandramouli S Sastry, Siddharth Gururani, Sageev Oore, Yisong Yue
We propose Stochastic Control Guidance (SCG), a novel guidance method that only requires forward evaluation of rule functions that can work with pre-trained diffusion models in a plug-and-play way, thus achieving training-free guidance for non-differentiable rules for the first time.
1 code implementation • 21 Jan 2024 • Yihong Guo, Hao liu, Yisong Yue, Anqi Liu
Central to our methodology is the application of robust regression, a distributionally robust technique tailored here to improve the estimation of conditional reward distribution from logging data.
1 code implementation • 15 Jan 2024 • Dan Zhang, Ziniu Hu, Sining Zhoubian, Zhengxiao Du, Kaiyu Yang, Zihan Wang, Yisong Yue, Yuxiao Dong, Jie Tang
To bridge these gaps, we introduce SciGLM, a suite of scientific language models able to conduct college-level scientific reasoning.
no code implementations • 18 Nov 2023 • Uriah Israel, Markus Marks, Rohit Dilip, Qilin Li, Morgan Schwartz, Elora Pradhan, Edward Pao, Shenyi Li, Alexander Pearson-Goulart, Pietro Perona, Georgia Gkioxari, Ross Barnowski, Yisong Yue, David Van Valen
Methods that have learned the general notion of "what is a cell" and can identify them across different domains of cellular imaging data have proven elusive.
no code implementations • 25 Jul 2023 • Fengxue Zhang, Jialin Song, James Bowden, Alexander Ladd, Yisong Yue, Thomas A. Desautels, Yuxin Chen
Our approach is easy to tune, and is able to focus on local region of the optimization space that can be tackled by existing BO methods.
1 code implementation • 11 Apr 2023 • Jeremy Bernstein, Chris Mingard, Kevin Huang, Navid Azizan, Yisong Yue
Automatic gradient descent trains both fully-connected and convolutional networks out-of-the-box and at ImageNet scale.
1 code implementation • 17 Mar 2023 • Victor Dorobantu, Charlotte Borcherds, Yisong Yue
We propose conformal generative modeling, a framework for generative modeling on 2D surfaces approximated by discrete triangle meshes.
no code implementations • 3 Mar 2023 • Cameron Voloshin, Abhinav Verma, Yisong Yue
Linear temporal logic (LTL) offers a simplified way of specifying tasks for policy optimization that may otherwise be difficult to describe with scalar reward functions.
1 code implementation • 21 Dec 2022 • Ryan K. Cosner, Yisong Yue, Aaron D. Ames
Imitation learning (IL) is a learning paradigm which can be used to synthesize controllers for complex systems that mimic behavior demonstrated by an expert (user or control algorithm).
1 code implementation • CVPR 2023 • Jennifer J. Sun, Lili Karashchuk, Amil Dravid, Serim Ryou, Sonia Fereidooni, John Tuthill, Aggelos Katsaggelos, Bingni W. Brunton, Georgia Gkioxari, Ann Kennedy, Yisong Yue, Pietro Perona
In this way, we discover keypoints without requiring manual supervision in videos of humans and rats, demonstrating the potential of 3D keypoint discovery for studying behavior.
1 code implementation • 30 Oct 2022 • Yujia Huang, Ivan Dario Jimenez Rodriguez, huan zhang, Yuanyuan Shi, Yisong Yue
Forward invariance is a long-studied property in control theory that is used to certify that a dynamical system stays within some pre-specified set of states for all time, and also admits robustness guarantees (e. g., the certificate holds under perturbations).
no code implementations • 10 Oct 2022 • Jennifer J. Sun, Megan Tjandrasuwita, Atharva Sehgal, Armando Solar-Lezama, Swarat Chaudhuri, Yisong Yue, Omar Costilla-Reyes
Neurosymbolic Programming (NP) techniques have the potential to accelerate scientific discovery.
1 code implementation • 21 Jul 2022 • Jennifer J. Sun, Markus Marks, Andrew Ulmer, Dipam Chakraborty, Brian Geuther, Edward Hayes, Heng Jia, Vivek Kumar, Sebastian Oleszko, Zachary Partridge, Milan Peelman, Alice Robie, Catherine E. Schretter, Keith Sheppard, Chao Sun, Param Uttarwar, Julian M. Wagner, Eric Werner, Joseph Parker, Pietro Perona, Yisong Yue, Kristin Branson, Ann Kennedy
We introduce MABe22, a large-scale, multi-agent video and trajectory benchmark to assess the quality of learned behavior representations.
no code implementations • 12 Jul 2022 • Victor D. Dorobantu, Kamyar Azizzadenesheli, Yisong Yue
We study policy optimization problems for deterministic Markov decision processes (MDPs) with metric state and action spaces, which we refer to as Metric Policy Optimization Problems (MPOPs).
no code implementations • 20 Jun 2022 • Cameron Voloshin, Hoang M. Le, Swarat Chaudhuri, Yisong Yue
We study the problem of policy optimization (PO) with linear temporal logic (LTL) constraints.
no code implementations • 16 Jun 2022 • Sabera Talukder, Jennifer J. Sun, Matthew Leonard, Bingni W. Brunton, Yisong Yue
Neuroscientists and neuroengineers have long relied on multielectrode neural recordings to study the brain.
1 code implementation • 13 May 2022 • Michael O'Connell, Guanya Shi, Xichen Shi, Kamyar Azizzadenesheli, Anima Anandkumar, Yisong Yue, Soon-Jo Chung
Last, our control design extrapolates to unseen wind conditions, is shown to be effective for outdoor flights with only onboard sensors, and can transfer across drones with minimal performance degradation.
1 code implementation • 8 May 2022 • Alexander R. Farhang, Jeremy Bernstein, Kushal Tirumala, Yang Liu, Yisong Yue
Weight norm $\|w\|$ and margin $\gamma$ participate in learning theory via the normalized margin $\gamma/\|w\|$.
no code implementations • 22 Mar 2022 • Andrew J. Taylor, Victor D. Dorobantu, Ryan K. Cosner, Yisong Yue, Aaron D. Ames
Existing design paradigms do not address the gap between theory (controller design with continuous time models) and practice (the discrete time sampled implementation of the resulting controllers); this can lead to poor performance and violations of safety for hardware instantiations.
no code implementations • 9 Mar 2022 • Shreyansh Daftry, Neil Abcouwer, Tyler del Sesto, Siddarth Venkatraman, Jialin Song, Lucas Igel, Amos Byon, Ugo Rosolia, Yisong Yue, Masahiro Ono
We present MLNav, a learning-enhanced path planning framework for safety-critical and resource-limited systems operating in complex environments, such as rovers navigating on Mars.
1 code implementation • 5 Feb 2022 • Ivan Dario Jimenez Rodriguez, Aaron D. Ames, Yisong Yue
Our approach, called LyaNet, is based on a novel Lyapunov loss formulation that encourages the inference dynamics to converge quickly to the correct prediction.
1 code implementation • CVPR 2022 • Jennifer J. Sun, Serim Ryou, Roni Goldshmid, Brandon Weissbourd, John Dabiri, David J. Anderson, Ann Kennedy, Yisong Yue, Pietro Perona
We propose a method for learning the posture and structure of agents from unlabelled behavioral videos.
Ranked #1 on Unsupervised Human Pose Estimation on Human3.6M
1 code implementation • NeurIPS 2021 • Angela Gao, Jorge Castellanos, Yisong Yue, Zachary Ross, Katherine Bouman
In this paper, we study the problem of blind inversion: solving an inverse problem with unknown or imperfect knowledge of the forward model parameters.
1 code implementation • CVPR 2022 • Albert Tseng, Jennifer J. Sun, Yisong Yue
We evaluate AutoSWAP in three behavior analysis domains and demonstrate that AutoSWAP outperforms existing approaches using only a fraction of the data.
1 code implementation • 8 Oct 2021 • Jeremy Bernstein, Alex Farhang, Yisong Yue
A Bayes point machine is a single classifier that approximates the majority decision of an ensemble of classifiers.
no code implementations • 29 Sep 2021 • Jeremy Bernstein, Yisong Yue
Do neural networks generalise because of bias in the functions returned by gradient descent, or bias already present in the network architecture?
no code implementations • 29 Sep 2021 • Alycia Lee, Anthony L Pineci, Uriah Israel, Omer Bar-Tal, Leeat Keren, David A. Van Valen, Anima Anandkumar, Yisong Yue, Anqi Liu
For each layer, we also achieve higher accuracy when the overall accuracy is kept fixed across different methods.
1 code implementation • 28 Jul 2021 • Eric Zhan, Jennifer J. Sun, Ann Kennedy, Yisong Yue, Swarat Chaudhuri
We present a framework for the unsupervised learning of neurosymbolic encoders, which are encoders obtained by composing neural networks with symbolic programs from a domain-specific language.
no code implementations • 11 Jun 2021 • Megan Tjandrasuwita, Jennifer J. Sun, Ann Kennedy, Swarat Chaudhuri, Yisong Yue
Hand-annotated data can vary due to factors such as subjective differences, intra-rater variability, and differing annotator expertise.
1 code implementation • NeurIPS 2021 • Guanya Shi, Kamyar Azizzadenesheli, Michael O'Connell, Soon-Jo Chung, Yisong Yue
We provide instantiations of our approach under varying conditions, leading to the first non-asymptotic end-to-end convergence guarantee for multi-task nonlinear control.
1 code implementation • 9 Jun 2021 • Dawna Bagherian, James Gornet, Jeremy Bernstein, Yu-Li Ni, Yisong Yue, Markus Meister
We study the problem of sparse nonlinear model recovery of high dimensional compositional functions.
no code implementations • 9 Jun 2021 • Aaron Ferber, Jialin Song, Bistra Dilkina, Yisong Yue
In addition, we compare our learned approach against Gurobi, a state-of-the-art MIP solver, demonstrating that our method can be used to improve solver performance.
1 code implementation • 13 May 2021 • Tianwei Yin, Zihui Wu, He Sun, Adrian V. Dalca, Yisong Yue, Katherine L. Bouman
In this paper, we leverage the sequential nature of MRI measurements, and propose a fully differentiable framework that jointly learns a sequential sampling policy simultaneously with a reconstruction strategy.
1 code implementation • 6 Apr 2021 • Jennifer J. Sun, Tomomi Karigo, Dipam Chakraborty, Sharada P. Mohanty, Benjamin Wild, Quan Sun, Chen Chen, David J. Anderson, Pietro Perona, Yisong Yue, Ann Kennedy
Multi-agent behavior modeling aims to understand the interactions that occur between agents.
1 code implementation • 8 Mar 2021 • Ivan D. Jimenez Rodriguez, Ugo Rosolia, Aaron D. Ames, Yisong Yue
We present a straightforward and efficient way to control unstable robotic systems using an estimated dynamics model.
no code implementations • 2 Mar 2021 • Cameron Voloshin, Nan Jiang, Yisong Yue
We present a novel off-policy loss function for learning a transition model in model-based reinforcement learning.
1 code implementation • 1 Mar 2021 • Jeremy Bernstein, Yisong Yue
A simple resolution to this conundrum is that the number of weights is usually a bad proxy for the actual amount of information stored.
no code implementations • 18 Feb 2021 • Yidan Qin, Max Allan, Yisong Yue, Joel W. Burdick, Mahdi Azizian
The combination of high diversity and limited data calls for new learning methods that are robust and invariant to operating conditions and surgical techniques.
2 code implementations • 14 Feb 2021 • Yang Liu, Jeremy Bernstein, Markus Meister, Yisong Yue
To address this problem, this paper conducts a combined study of neural architecture and optimisation, leading to a new optimiser called Nero: the neuronal rotator.
no code implementations • 17 Jan 2021 • Anqi Liu, Hao liu, Tongxin Li, Saeed Karimi-Bidhendi, Yisong Yue, Anima Anandkumar
Thus, we provide a principled approach to tackling the joint problem of causal discovery and latent variable inference.
no code implementations • ICLR 2021 • Ayya Alieva, Aiden Aceves, Jialin Song, Stephen Mayo, Yisong Yue, Yuxin Chen
In particular, we focus on a class of combinatorial problems that can be solved via submodular maximization (either directly on the objective function or via submodular surrogates).
no code implementations • 10 Dec 2020 • Guanya Shi, Wolfgang Hönig, Xichen Shi, Yisong Yue, Soon-Jo Chung
We present Neural-Swarm2, a learning-based method for motion planning and control that allows heterogeneous multirotors in a swarm to safely fly in close proximity.
no code implementations • NeurIPS 2020 • Chenkai Yu, Guanya Shi, Soon-Jo Chung, Yisong Yue, Adam Wierman
We study the impact of predictions in online Linear Quadratic Regulator control with both stochastic and adversarial disturbances in the dynamics.
1 code implementation • CVPR 2021 • Jennifer J. Sun, Ann Kennedy, Eric Zhan, David J. Anderson, Yisong Yue, Pietro Perona
The tasks in our method can be efficiently engineered by domain experts through a process we call "task programming", which uses programs to explicitly encode structured knowledge from domain experts.
no code implementations • 21 Nov 2020 • Andrew J. Taylor, Victor D. Dorobantu, Sarah Dean, Benjamin Recht, Yisong Yue, Aaron D. Ames
Modern nonlinear control theory seeks to endow systems with properties such as stability and safety, and has been deployed successfully across various domains.
no code implementations • NeurIPS Workshop SVRHM 2020 • George Barnum, Sabera Talukder, Yisong Yue
To facilitate the study of early multimodal fusion, we create a convolutional LSTM network architecture that simultaneously processes both audio and visual inputs, and allows us to select the layer at which audio and visual information combines.
no code implementations • 11 Nov 2020 • Neil Abcouwer, Shreyansh Daftry, Siddarth Venkatraman, Tyler del Sesto, Olivier Toupet, Ravi Lanka, Jialin Song, Yisong Yue, Masahiro Ono
Enhanced AutoNav (ENav), the baseline surface navigation software for NASA's Perseverance rover, sorts a list of candidate paths for the rover to traverse, then uses the Approximate Clearance Evaluation (ACE) algorithm to evaluate whether the most highly ranked paths are safe.
1 code implementation • 9 Nov 2020 • Kejun Li, Maegan Tucker, Erdem Biyik, Ellen Novoseller, Joel W. Burdick, Yanan Sui, Dorsa Sadigh, Yisong Yue, Aaron D. Ames
ROIAL learns Bayesian posteriors that predict each exoskeleton user's utility landscape across four exoskeleton gait parameters.
no code implementations • 5 Nov 2020 • Sabera Talukder, Guruprasad Raghavan, Yisong Yue
Within this sparse, binary paradigm we sample many binary architectures to create families of architecture agnostic neural networks not trained via backpropagation.
1 code implementation • NeurIPS 2021 • Joseph Marino, Alexandre Piché, Alessandro Davide Ialongo, Yisong Yue
Policy networks are a central feature of deep reinforcement learning (RL) algorithms for continuous control, enabling the estimation and sampling of high-value actions.
no code implementations • 8 Oct 2020 • Haoxuan Wang, Zhiding Yu, Yisong Yue, Anima Anandkumar, Anqi Liu, Junchi Yan
We propose a framework for learning calibrated uncertainties under domain shifts, where the source (training) distribution differs from the target (test) distribution.
no code implementations • 28 Sep 2020 • Haoxuan Wang, Anqi Liu, Zhiding Yu, Yisong Yue, Anima Anandkumar
This formulation motivates the use of two neural networks that are jointly trained --- a discriminative network between the source and target domains for density-ratio estimation, in addition to the standard classification network.
1 code implementation • NeurIPS 2020 • Ameesh Shah, Eric Zhan, Jennifer J. Sun, Abhinav Verma, Yisong Yue, Swarat Chaudhuri
This relaxed program is differentiable and can be trained end-to-end, and the resulting training loss is an approximately admissible heuristic that can guide the combinatorial search.
no code implementations • 16 Jul 2020 • Eric Zhao, Anqi Liu, Animashree Anandkumar, Yisong Yue
We address the problem of active learning under label shift: when the class proportions of source and target domains differ.
1 code implementation • 8 Jul 2020 • Serim Ryou, Michael R. Maser, Alexander Y. Cui, Travis J. DeLano, Yisong Yue, Sarah E. Reisman
We present a systematic investigation using graph neural networks (GNNs) to model organic chemical reactions.
1 code implementation • 28 Jun 2020 • Akella Ravi Tej, Kamyar Azizzadenesheli, Mohammad Ghavamzadeh, Anima Anandkumar, Yisong Yue
On the other hand, more sample efficient alternatives like Bayesian quadrature methods have received little attention due to their high computational complexity.
no code implementations • 25 Jun 2020 • Akash Kumar, Adish Singla, Yisong Yue, Yuxin Chen
We investigate the average teaching complexity of the task, i. e., the minimal number of samples (halfspace queries) required by a teacher to help a version-space learner in locating a randomly selected target.
1 code implementation • NeurIPS 2020 • Jeremy Bernstein, Jia-Wei Zhao, Markus Meister, Ming-Yu Liu, Anima Anandkumar, Yisong Yue
This paper proves that multiplicative weight updates satisfy a descent lemma tailored to compositional functions.
4 code implementations • 18 Jun 2020 • Manish Prajapat, Kamyar Azizzadenesheli, Alexander Liniger, Yisong Yue, Anima Anandkumar
A core challenge in policy optimization in competitive Markov decision processes is the design of efficient optimization methods with desirable convergence and stability properties.
no code implementations • 9 May 2020 • Yashwanth Kumar Nakka, Anqi Liu, Guanya Shi, Anima Anandkumar, Yisong Yue, Soon-Jo Chung
The Info-SNOC algorithm is used to compute a sub-optimal pool of safe motion plans that aid in exploration for learning unknown residual dynamics under safety constraints.
no code implementations • NeurIPS 2020 • Jialin Song, Ravi Lanka, Yisong Yue, Bistra Dilkina
This paper studies a strategy for data-driven algorithm design for large-scale combinatorial optimization problems that can leverage existing state-of-the-art solvers in general purpose ways.
1 code implementation • 13 Mar 2020 • Maegan Tucker, Myra Cheng, Ellen Novoseller, Richard Cheng, Yisong Yue, Joel W. Burdick, Aaron D. Ames
Optimizing lower-body exoskeleton walking gaits for user comfort requires understanding users' preferences over a high-dimensional gait parameter space.
no code implementations • 6 Mar 2020 • Guanya Shi, Wolfgang Hönig, Yisong Yue, Soon-Jo Chung
We design a stable nonlinear tracking controller using the learned model.
1 code implementation • 26 Feb 2020 • Benjamin Rivière, Wolfgang Hoenig, Yisong Yue, Soon-Jo Chung
We present GLAS: Global-to-Local Autonomy Synthesis, a provably-safe, automated distributed policy generation for multi-robot motion planning.
Robotics
1 code implementation • NeurIPS 2020 • Guanya Shi, Yiheng Lin, Soon-Jo Chung, Yisong Yue, Adam Wierman
This paper presents competitive algorithms for a novel class of online optimization problems with memory.
1 code implementation • ICML 2020 • Jung Yeon Park, Kenneth Theo Carr, Stephan Zheng, Yisong Yue, Rose Yu
Efficient and interpretable spatial analysis is crucial in many fields such as geology, sports, and climate science.
2 code implementations • NeurIPS 2020 • Jeremy Bernstein, Arash Vahdat, Yisong Yue, Ming-Yu Liu
This paper relates parameter distance to gradient breakdown for a broad class of nonlinear compositional functions.
no code implementations • L4DC 2020 • Andrew Taylor, Andrew Singletary, Yisong Yue, Aaron Ames
Modern nonlinear control theory seeks to endow systems with properties of stability and safety, and have been deployed successfully in multiple domains.
3 code implementations • 15 Nov 2019 • Cameron Voloshin, Hoang M. Le, Nan Jiang, Yisong Yue
We offer an experimental benchmark and empirical study for off-policy policy evaluation (OPE) in reinforcement learning, which is a key problem in many safety critical applications.
no code implementations • 13 Nov 2019 • Anqi Liu, Hao liu, Anima Anandkumar, Yisong Yue
Ours is a general approach that can be used to augment any existing OPE method that utilizes the direct method.
no code implementations • NeurIPS 2019 • Nikhil Ghosh, Yuxin Chen, Yisong Yue
In this paper, we aim to learn a low-dimensional Euclidean representation from a set of constraints of the form "item j is closer to item i than item k".
2 code implementations • ICML 2020 • Eric Zhan, Albert Tseng, Yisong Yue, Adith Swaminathan, Matthew Hausknecht
We study the problem of controllable generation of long-term sequential behaviors, where the goal is to calibrate to multiple behavior styles simultaneously.
1 code implementation • 4 Aug 2019 • Ellen R. Novoseller, Yibing Wei, Yanan Sui, Yisong Yue, Joel W. Burdick
In preference-based reinforcement learning (RL), an agent interacts with the environment while receiving preferences instead of absolute feedback.
no code implementations • 26 Jul 2019 • Baihong Jin, Yingshui Tan, Alexander Nettekoven, Yuxin Chen, Ufuk Topcu, Yisong Yue, Alberto Sangiovanni Vincentelli
We show that the encoder-decoder model is able to identify the injected anomalies in a modern manufacturing process in an unsupervised fashion.
no code implementations • NeurIPS 2019 • Abhinav Verma, Hoang M. Le, Yisong Yue, Swarat Chaudhuri
First, we view our learning task as optimization in policy space, modulo the constraint that the desired policy has a programmatic representation, and solve this optimization problem using a form of mirror descent that takes a gradient step into the unconstrained policy space and then projects back onto the constrained space.
1 code implementation • 3 Jul 2019 • Jialin Song, Ravi Lanka, Yisong Yue, Masahiro Ono
We study the problem of learning sequential decision-making policies in settings with multiple state-action representations.
no code implementations • L4DC 2020 • Anqi Liu, Guanya Shi, Soon-Jo Chung, Anima Anandkumar, Yisong Yue
To address this challenge, we present a deep robust regression model that is trained to directly predict the uncertainty bounds for safe exploration.
1 code implementation • 14 May 2019 • Richard Cheng, Abhinav Verma, Gabor Orosz, Swarat Chaudhuri, Yisong Yue, Joel W. Burdick
We show that functional regularization yields a bias-variance trade-off, and propose an adaptive tuning strategy to optimize this trade-off.
no code implementations • 17 Apr 2019 • Kevin K. Yang, Yuxin Chen, Alycia Lee, Yisong Yue
Importantly, we show that our objective function can be efficiently decomposed as a difference of submodular functions (DS), which allows us to employ DS optimization tools to greedily identify sets of constraints that increase the likelihood of finding items with high utility.
2 code implementations • 20 Mar 2019 • Hoang M. Le, Cameron Voloshin, Yisong Yue
When learning policies for real-world domains, two important questions arise: (i) how to efficiently use pre-collected off-policy, non-optimal behavior data; and (ii) how to mediate among different competing objectives and constraints.
no code implementations • 18 Mar 2019 • Andrew J. Taylor, Victor D. Dorobantu, Meera Krishnamoorthy, Hoang M. Le, Yisong Yue, Aaron D. Ames
The goal of this paper is to understand the impact of learning on control synthesis from a Lyapunov function perspective.
no code implementations • 4 Mar 2019 • Andrew J. Taylor, Victor D. Dorobantu, Hoang M. Le, Yisong Yue, Aaron D. Ames
Many modern nonlinear control methods aim to endow systems with guaranteed properties, such as stability or safety, and have been successfully applied to the domain of robotics.
1 code implementation • NeurIPS 2019 • Yukai Liu, Rose Yu, Stephan Zheng, Eric Zhan, Yisong Yue
Missing value imputation is a fundamental problem in spatiotemporal modeling, from motion tracking to the dynamics of physical systems.
Ranked #1 on Multivariate Time Series Imputation on PEMS-SF
2 code implementations • 19 Nov 2018 • Guanya Shi, Xichen Shi, Michael O'Connell, Rose Yu, Kamyar Azizzadenesheli, Animashree Anandkumar, Yisong Yue, Soon-Jo Chung
To the best of our knowledge, this is the first DNN-based nonlinear feedback controller with stability guarantees that can utilize arbitrarily large neural nets.
no code implementations • 15 Nov 2018 • Jialin Song, Yury S. Tokpanov, Yuxin Chen, Dagny Fleischman, Kate T. Fountaine, Harry A. Atwater, Yisong Yue
We apply numerical methods in combination with finite-difference-time-domain (FDTD) simulations to optimize transmission properties of plasmonic mirror color filters using a multi-objective figure of merit over a five-dimensional parameter space by utilizing novel multi-fidelity Gaussian processes approach.
1 code implementation • NeurIPS 2018 • Joseph Marino, Milan Cvitkovic, Yisong Yue
We introduce the variational filtering EM algorithm, a simple, general-purpose method for performing variational inference in dynamical latent variable models using information from only past and present variables, i. e. filtering.
no code implementations • 2 Nov 2018 • Jialin Song, Yuxin Chen, Yisong Yue
How can we efficiently gather information to optimize an unknown function, when presented with multiple, mutually dependent information sources with different costs?
no code implementations • 18 Oct 2018 • Kamyar Azizzadenesheli, Yisong Yue, Animashree Anandkumar
Deploying these tools, we generalize a variety of existing theoretical guarantees, such as policy gradient and convergence theorems, to partially observable domains, those which also could be carried to more settings of interest.
no code implementations • 8 Sep 2018 • Zachary E. Ross, Yisong Yue, Men-Andrin Meier, Egill Hauksson, Thomas H. Heaton
For the examined datasets, PhaseLink can precisely associate P- and S-picks to events that are separated by ~12 seconds in origin time.
1 code implementation • ICML 2018 • Joseph Marino, Yisong Yue, Stephan Mandt
The failure of these models to reach fully optimized approximate posterior estimates results in an amortization gap.
no code implementations • ICML 2018 • Yanan Sui, Vincent Zhuang, Joel W. Burdick, Yisong Yue
We provide theoretical guarantees for both the satisfaction of safety constraints as well as convergence to the optimal utility value.
no code implementations • NeurIPS 2019 • Anette Hunziker, Yuxin Chen, Oisin Mac Aodha, Manuel Gomez Rodriguez, Andreas Krause, Pietro Perona, Yisong Yue, Adish Singla
Our framework is both generic, allowing the design of teaching schedules for different memory models, and also interactive, allowing the teacher to adapt the schedule to the underlying forgetting mechanisms of the learner.
no code implementations • 3 Apr 2018 • Jialin Song, Ravi Lanka, Albert Zhao, Aadyot Bhatnagar, Yisong Yue, Masahiro Ono
We study the problem of learning a good search policy for combinatorial search spaces.
2 code implementations • ICLR 2019 • Eric Zhan, Stephan Zheng, Yisong Yue, Long Sha, Patrick Lucey
We study the problem of training sequential generative models for capturing coordinated multi-agent trajectory behavior, such as offensive basketball gameplay.
1 code implementation • 11 Mar 2018 • Sumanth Dathathri, Stephan Zheng, Tianwei Yin, Richard M. Murray, Yisong Yue
Deep neural networks are vulnerable to adversarial examples, which dramatically alter model output using small input changes.
no code implementations • ICML 2018 • Hoang M. Le, Nan Jiang, Alekh Agarwal, Miroslav Dudík, Yisong Yue, Hal Daumé III
We study how to effectively leverage expert feedback to learn sequential decision-making policies.
no code implementations • CVPR 2018 • Oisin Mac Aodha, Shih-An Su, Yuxin Chen, Pietro Perona, Yisong Yue
We study the problem of computer-assisted teaching with explanations.
no code implementations • 19 Feb 2018 • Stephan Zheng, Rose Yu, Yisong Yue
High-dimensional tensor models are notoriously computationally expensive to train.
no code implementations • NeurIPS 2018 • Yuxin Chen, Adish Singla, Oisin Mac Aodha, Pietro Perona, Yisong Yue
We highlight that adaptivity does not speed up the teaching process when considering existing models of version space learners, such as "worst-case" (the learner picks the next hypothesis randomly from the version space) and "preference-based" (the learner picks hypothesis according to some global preference).
no code implementations • ICLR 2018 • Stephan Zheng, Yisong Yue
Reinforcement learning in environments with large state-action spaces is challenging, as exploration can be highly inefficient.
no code implementations • ICLR 2018 • Joseph Marino, Yisong Yue, Stephan Mandt
Inference models, which replace an optimization-based inference procedure with a learned model, have been fundamental in advancing Bayesian deep learning, the most notable example being variational auto-encoders (VAEs).
no code implementations • ICLR 2018 • Rose Yu, Stephan Zheng, Anima Anandkumar, Yisong Yue
We present Tensor-Train RNN (TT-RNN), a novel family of neural sequence architectures for multivariate forecasting in environments with nonlinear dynamics.
1 code implementation • ICLR 2018 • Rose Yu, Stephan Zheng, Anima Anandkumar, Yisong Yue
We present Higher-Order Tensor RNN (HOT-RNN), a novel family of neural sequence architectures for multivariate forecasting in environments with nonlinear dynamics.
no code implementations • 8 Jul 2017 • Yanan Sui, Yisong Yue, Joel W. Burdick
This problem can be formulated as a $K$-armed Dueling Bandits problem where $K$ is the total number of decisions.
no code implementations • CVPR 2017 • Zhiwei Deng, Rajitha Navarathna, Peter Carr, Stephan Mandt, Yisong Yue, Iain Matthews, Greg Mori
Matrix and tensor factorization methods are often used for finding underlying low-dimensional patterns from noisy data.
no code implementations • NeurIPS 2016 • Stephan Zheng, Yisong Yue, Patrick Lucey
We study the problem of modeling spatiotemporal trajectories over long time horizons using expert demonstrations.
no code implementations • 29 Apr 2017 • Yanan Sui, Vincent Zhuang, Joel W. Burdick, Yisong Yue
The dueling bandits problem is an online learning framework for learning from pairwise preference feedback, and is particularly well-suited for modeling settings that elicit subjective or implicit human feedback.
no code implementations • ICML 2017 • Hoang M. Le, Yisong Yue, Peter Carr, Patrick Lucey
We study the problem of imitation learning from demonstrations of multiple coordinating agents.
no code implementations • 1 Nov 2016 • Eyrun Eyjolfsdottir, Kristin Branson, Yisong Yue, Pietro Perona
We propose a framework for detecting action patterns from motion sequences and modeling the sensory-motor relationship of animals, using a generative recurrent neural network.
no code implementations • 23 Sep 2016 • Matteo Ruggero Ronchi, Joon Sik Kim, Yisong Yue
We tackle the problem of learning a rotation invariant latent factor model when the training data is comprised of lower-dimensional projections of the original feature space.
2 code implementations • 3 Jun 2016 • Hoang M. Le, Andrew Kang, Yisong Yue, Peter Carr
We study the problem of smooth imitation learning for online sequence prediction, where the goal is to train a policy that can smoothly imitate demonstrated behavior in a dynamic and continuous environment in response to online, sequential context input.
no code implementations • CVPR 2016 • Jianhui Chen, Hoang M. Le, Peter Carr, Yisong Yue, James J. Little
We study the problem of online prediction for realtime camera planning, where the goal is to predict smooth trajectories that correctly track and frame objects of interest (e. g., players in a basketball game).
no code implementations • NeurIPS 2015 • Bryan D. He, Yisong Yue
Interactive submodular set cover is an interactive variant of submodular set cover over a hypothesis class of submodular functions, where the goal is to satisfy all sufficiently plausible submodular functions to a target threshold using as few (cost-weighted) actions as possible.
no code implementations • 16 Aug 2013 • Jiaji Zhou, Stephane Ross, Yisong Yue, Debadeepta Dey, J. Andrew Bagnell
We study the problem of predicting a set or list of options under knapsack constraint.
no code implementations • 11 May 2013 • Stephane Ross, Jiaji Zhou, Yisong Yue, Debadeepta Dey, J. Andrew Bagnell
Many prediction domains, such as ad placement, recommendation, trajectory prediction, and document summarization, require predicting a set or list of options.
no code implementations • NeurIPS 2011 • Yisong Yue, Carlos Guestrin
Diversified retrieval and online learning are two core research areas in the design of modern information retrieval systems. In this paper, we propose the linear submodular bandits problem, which is an online learning setting for optimizing a general class of feature-rich submodular utility models for diversified retrieval.