290 citations · 1.9k across the 78 of their papers we have counts for
53 papers · 1 filter
Learning Regions of Interest for Bayesian Optimization with Adaptive Level-Set Estimation
Fengxue Zhang, Jialin Song, James Bowden +4
We study Bayesian optimization (BO) in high-dimensional and non-stationary scenarios. Existing algorithms for such scenarios typically require extensive hyperparameter tuning, whic…
Conformal Generative Modeling on Triangulated Surfaces
Victor Dorobantu, Charlotte Borcherds, Yisong Yue
We propose conformal generative modeling, a framework for generative modeling on 2D surfaces approximated by discrete triangle meshes. Our approach leverages advances in discrete c…
Eventual Discounting Temporal Logic Counterfactual Experience Replay
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. However, t…
Automatic Synthesis of Diverse Weak Supervision Sources for Behavior Analysis
Albert Tseng, Jennifer J. Sun, Yisong Yue
Obtaining annotations for large training sets is expensive, especially in settings where domain knowledge is required, such as behavior analysis. Weak supervision has been studied…
Interpreting Expert Annotation Differences in Animal Behavior
Megan Tjandrasuwita, Jennifer J. Sun, Ann Kennedy +2
Hand-annotated data can vary due to factors such as subjective differences, intra-rater variability, and differing annotator expertise. We study annotations from different experts…
Learning Pseudo-Backdoors for Mixed Integer Programs
Aaron Ferber, Jialin Song, Bistra Dilkina +1
We propose a machine learning approach for quickly solving Mixed Integer Programs (MIP) by learning to prioritize a set of decision variables, which we call pseudo-backdoors, for b…
The Multi-Agent Behavior Dataset: Mouse Dyadic Social Interactions
Jennifer J. Sun, Tomomi Karigo, Dipam Chakraborty +8
Multi-agent behavior modeling aims to understand the interactions that occur between agents. We present a multi-agent dataset from behavioral neuroscience, the Caltech Mouse Social…
Computing the Information Content of Trained Neural Networks
Jeremy Bernstein, Yisong Yue
How much information does a learning algorithm extract from the training data and store in a neural network's weights? Too much, and the network would overfit to the training data.…
Disentangling Observed Causal Effects from Latent Confounders using Method of Moments
Anqi Liu, Hao Liu, Tongxin Li +3
Discovering the complete set of causal relations among a group of variables is a challenging unsupervised learning problem. Often, this challenge is compounded by the fact that the…
Architecture Agnostic Neural Networks
Sabera Talukder, Guruprasad Raghavan, Yisong Yue
In this paper, we explore an alternate method for synthesizing neural network architectures, inspired by the brain's stochastic synaptic pruning. During a person's lifetime, numero…
On the Benefits of Early Fusion in Multimodal Representation Learning
George Barnum, Sabera Talukder, Yisong Yue
Intelligently reasoning about the world often requires integrating data from multiple modalities, as any individual modality may contain unreliable or incomplete information. Prior…
Iterative Amortized Policy Optimization
Joseph Marino, Alexandre Piché, Alessandro Davide Ialongo +1
Policy networks are a central feature of deep reinforcement learning (RL) algorithms for continuous control, enabling the estimation and sampling of high-value actions. From the va…
Graph Neural Networks for the Prediction of Substrate-Specific Organic Reaction Conditions
Serim Ryou, Michael R. Maser, Alexander Y. Cui +3
We present a systematic investigation using graph neural networks (GNNs) to model organic chemical reactions. To do so, we prepared a dataset collection of four ubiquitous reaction…
Learning Differentiable Programs with Admissible Neural Heuristics
Ameesh Shah, Eric Zhan, Jennifer J. Sun +3
We study the problem of learning differentiable functions expressed as programs in a domain-specific language. Such programmatic models can offer benefits such as composability and…
Active Learning under Label Shift
Eric Zhao, Anqi Liu, Animashree Anandkumar +1
We address the problem of active learning under label shift: when the class proportions of source and target domains differ. We introduce a "medial distribution" to incorporate a t…
Competitive Policy Optimization
Manish Prajapat, Kamyar Azizzadenesheli, Alexander Liniger +2
A core challenge in policy optimization in competitive Markov decision processes is the design of efficient optimization methods with desirable convergence and stability properties…
Deep Bayesian Quadrature Policy Optimization
Akella Ravi Tej, Kamyar Azizzadenesheli, Mohammad Ghavamzadeh +2
We study the problem of obtaining accurate policy gradient estimates using a finite number of samples. Monte-Carlo methods have been the default choice for policy gradient estimati…
Average-case Complexity of Teaching Convex Polytopes via Halfspace Queries
Akash Kumar, Adish Singla, Yisong Yue +1
We examine the task of locating a target region among those induced by intersections of halfspaces in . This generic task connects to fundamental machine learning…
Multiresolution Tensor Learning for Efficient and Interpretable Spatial Analysis
Jung Yeon Park, Kenneth Theo Carr, Stephan Zheng +2
Efficient and interpretable spatial analysis is crucial in many fields such as geology, sports, and climate science. Tensor latent factor models can describe higher-order correlati…
Online Optimization with Memory and Competitive Control
Guanya Shi, Yiheng Lin, Soon-Jo Chung +2
This paper presents competitive algorithms for a novel class of online optimization problems with memory. We consider a setting where the learner seeks to minimize the sum of a hit…
On the distance between two neural networks and the stability of learning
Jeremy Bernstein, Arash Vahdat, Yisong Yue +1
This paper relates parameter distance to gradient breakdown for a broad class of nonlinear compositional functions. The analysis leads to a new distance function called deep relati…
Triply Robust Off-Policy Evaluation
Anqi Liu, Hao Liu, Anima Anandkumar +1
We propose a robust regression approach to off-policy evaluation (OPE) for contextual bandits. We frame OPE as a covariate-shift problem and leverage modern robust regression tools…
Empirical Study of Off-Policy Policy Evaluation for Reinforcement Learning
Cameron Voloshin, Hoang M. Le, Nan Jiang +1
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…
Landmark Ordinal Embedding
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". Existing approaches for th…