Search Results for author: Jeff Schneider

Found 75 papers, 21 papers with code

Sample Efficient Reinforcement Learning from Human Feedback via Active Exploration

no code implementations1 Dec 2023 Viraj Mehta, Vikramjeet Das, Ojash Neopane, Yijia Dai, Ilija Bogunovic, Jeff Schneider, Willie Neiswanger

Preference-based feedback is important for many applications in reinforcement learning where direct evaluation of a reward function is not feasible.


Reasoning with Latent Diffusion in Offline Reinforcement Learning

1 code implementation12 Sep 2023 Siddarth Venkatraman, Shivesh Khaitan, Ravi Tej Akella, John Dolan, Jeff Schneider, Glen Berseth

However, a key challenge in offline RL lies in effectively stitching portions of suboptimal trajectories from the static dataset while avoiding extrapolation errors arising due to a lack of support in the dataset.

D4RL Offline RL +3

Kernelized Offline Contextual Dueling Bandits

no code implementations21 Jul 2023 Viraj Mehta, Ojash Neopane, Vikramjeet Das, Sen Lin, Jeff Schneider, Willie Neiswanger

Preference-based feedback is important for many applications where direct evaluation of a reward function is not feasible.

Data Cross-Segmentation for Improved Generalization in Reinforcement Learning Based Algorithmic Trading

no code implementations18 Jul 2023 Vikram Duvvur, Aashay Mehta, Edward Sun, Bo Wu, Ken Yew Chan, Jeff Schneider

In a typical set-up, supervised learning is used to predict the future prices of assets, and those predictions drive a simple trading and execution strategy.

Algorithmic Trading reinforcement-learning +1

Enhancing Visual Domain Adaptation with Source Preparation

no code implementations16 Jun 2023 Anirudha Ramesh, Anurag Ghosh, Christoph Mertz, Jeff Schneider

Our Almost Unsupervised Domain Adaptation (AUDA) framework, a label-efficient semi-supervised approach for robotic scenarios -- employs Source Preparation (SP), Unsupervised Domain Adaptation (UDA) and Supervised Alignment (SA) from limited labeled data.

object-detection Object Detection +2

GUTS: Generalized Uncertainty-Aware Thompson Sampling for Multi-Agent Active Search

no code implementations4 Apr 2023 Nikhil Angad Bakshi, Tejus Gupta, Ramina Ghods, Jeff Schneider

We conduct field tests using our multi-robot system in an unstructured environment with a search area of approximately 75, 000 sq.

Disaster Response Thompson Sampling

Near-optimal Policy Identification in Active Reinforcement Learning

no code implementations19 Dec 2022 Xiang Li, Viraj Mehta, Johannes Kirschner, Ian Char, Willie Neiswanger, Jeff Schneider, Andreas Krause, Ilija Bogunovic

Many real-world reinforcement learning tasks require control of complex dynamical systems that involve both costly data acquisition processes and large state spaces.

Bayesian Optimization reinforcement-learning +1

Exploration via Planning for Information about the Optimal Trajectory

1 code implementation6 Oct 2022 Viraj Mehta, Ian Char, Joseph Abbate, Rory Conlin, Mark D. Boyer, Stefano Ermon, Jeff Schneider, Willie Neiswanger

In this work, we develop a method that allows us to plan for exploration while taking both the task and the current knowledge about the dynamics into account.

Reinforcement Learning (RL)

Cost Aware Asynchronous Multi-Agent Active Search

no code implementations5 Oct 2022 Arundhati Banerjee, Ramina Ghods, Jeff Schneider

Multi-agent active search requires autonomous agents to choose sensing actions that efficiently locate targets.

Decision Making Thompson Sampling

Addressing Optimism Bias in Sequence Modeling for Reinforcement Learning

no code implementations21 Jul 2022 Adam Villaflor, Zhe Huang, Swapnil Pande, John Dolan, Jeff Schneider

Impressive results in natural language processing (NLP) based on the Transformer neural network architecture have inspired researchers to explore viewing offline reinforcement learning (RL) as a generic sequence modeling problem.

Autonomous Driving D4RL +2

How Useful are Gradients for OOD Detection Really?

no code implementations20 May 2022 Conor Igoe, Youngseog Chung, Ian Char, Jeff Schneider

One critical challenge in deploying highly performant machine learning models in real-life applications is out of distribution (OOD) detection.

Misconceptions Out of Distribution (OOD) Detection

BATS: Best Action Trajectory Stitching

no code implementations26 Apr 2022 Ian Char, Viraj Mehta, Adam Villaflor, John M. Dolan, Jeff Schneider

Past efforts for developing algorithms in this area have revolved around introducing constraints to online reinforcement learning algorithms to ensure the actions of the learned policy are constrained to the logged data.

reinforcement-learning Reinforcement Learning (RL)

Multi-Agent Active Search using Detection and Location Uncertainty

no code implementations9 Mar 2022 Arundhati Banerjee, Ramina Ghods, Jeff Schneider

We then build a decision making algorithm on this inference method that uses Thompson sampling to enable decentralized multi-agent active search.

Decision Making Disaster Response +1

Robust Reinforcement Learning via Genetic Curriculum

no code implementations17 Feb 2022 Yeeho Song, Jeff Schneider

Some of the state of the art approaches try to address the problem with adversarial agents, but these agents often require expert supervision to fine tune and prevent the adversary from becoming too challenging to the trainee agent.

reinforcement-learning Reinforcement Learning (RL)

Learning Cooperative Multi-Agent Policies with Partial Reward Decoupling

no code implementations23 Dec 2021 Benjamin Freed, Aditya Kapoor, Ian Abraham, Jeff Schneider, Howie Choset

One of the preeminent obstacles to scaling multi-agent reinforcement learning to large numbers of agents is assigning credit to individual agents' actions.

counterfactual Multi-agent Reinforcement Learning

An Experimental Design Perspective on Model-Based Reinforcement Learning

1 code implementation9 Dec 2021 Viraj Mehta, Biswajit Paria, Jeff Schneider, Stefano Ermon, Willie Neiswanger

In particular, we leverage ideas from Bayesian optimal experimental design to guide the selection of state-action queries for efficient learning.

Continuous Control Experimental Design +3

An Experimental Design Perspective on Exploration in Reinforcement Learning

no code implementations ICLR 2022 Viraj Mehta, Biswajit Paria, Jeff Schneider, Willie Neiswanger, Stefano Ermon

In particular, we leverage ideas from Bayesian optimal experimental design to guide the selection of state-action queries for efficient learning.

Continuous Control Experimental Design +2

Uncertainty Toolbox: an Open-Source Library for Assessing, Visualizing, and Improving Uncertainty Quantification

1 code implementation21 Sep 2021 Youngseog Chung, Ian Char, Han Guo, Jeff Schneider, Willie Neiswanger

With increasing deployment of machine learning systems in various real-world tasks, there is a greater need for accurate quantification of predictive uncertainty.

BIG-bench Machine Learning

SBEVNet: End-to-End Deep Stereo Layout Estimation

1 code implementation25 May 2021 Divam Gupta, Wei Pu, Trenton Tabor, Jeff Schneider

Instead, the learning of a good internal bird's eye view feature representation is effective for layout estimation.

Depth Estimation Disparity Estimation

Computational catalyst discovery: Active classification through myopic multiscale sampling

no code implementations2 Feb 2021 Kevin Tran, Willie Neiswanger, Kirby Broderick, Erix Xing, Jeff Schneider, Zachary W. Ulissi

We address this issue by relaxing the catalyst discovery goal into a classification problem: "What is the set of catalysts that is worth testing experimentally?"

Chemical Physics

Affordance-based Reinforcement Learning for Urban Driving

no code implementations15 Jan 2021 Tanmay Agarwal, Hitesh Arora, Jeff Schneider

Traditional autonomous vehicle pipelines that follow a modular approach have been very successful in the past both in academia and industry, which has led to autonomy deployed on road.

reinforcement-learning Reinforcement Learning (RL)

Fine-Tuning Offline Reinforcement Learning with Model-Based Policy Optimization

no code implementations1 Jan 2021 Adam Villaflor, John Dolan, Jeff Schneider

Then, we can optionally enter a second stage where we fine-tune the policy using our novel Model-Based Behavior-Regularized Policy Optimization (MB2PO) algorithm.

D4RL reinforcement-learning +1

Beyond Pinball Loss: Quantile Methods for Calibrated Uncertainty Quantification

2 code implementations NeurIPS 2021 Youngseog Chung, Willie Neiswanger, Ian Char, Jeff Schneider

However, this loss restricts the scope of applicable regression models, limits the ability to target many desirable properties (e. g. calibration, sharpness, centered intervals), and may produce poor conditional quantiles.


Behavior Planning at Urban Intersections through Hierarchical Reinforcement Learning

no code implementations9 Nov 2020 Zhiqian Qiao, Jeff Schneider, John M. Dolan

In this work, we propose a behavior planning structure based on reinforcement learning (RL) which is capable of performing autonomous vehicle behavior planning with a hierarchical structure in simulated urban environments.

Autonomous Vehicles Hierarchical Reinforcement Learning +2

Multi-Agent Active Search using Realistic Depth-Aware Noise Model

1 code implementation9 Nov 2020 Ramina Ghods, William J. Durkin, Jeff Schneider

The active search for objects of interest in an unknown environment has many robotics applications including search and rescue, detecting gas leaks or locating animal poachers.

object-detection Object Detection +1

Interactive Visualization for Debugging RL

no code implementations14 Aug 2020 Shuby Deshpande, Benjamin Eysenbach, Jeff Schneider

Visualization tools for supervised learning allow users to interpret, introspect, and gain an intuition for the successes and failures of their models.

Vizarel: A System to Help Better Understand RL Agents

no code implementations10 Jul 2020 Shuby Deshpande, Jeff Schneider

Visualization tools for supervised learning have allowed users to interpret, introspect, and gain intuition for the successes and failures of their models.

reinforcement-learning Reinforcement Learning (RL)

Asynchronous Multi Agent Active Search

no code implementations25 Jun 2020 Ramina Ghods, Arundhati Banerjee, Jeff Schneider

Active search refers to the problem of efficiently locating targets in an unknown environment by actively making data-collection decisions, and has many applications including detecting gas leaks, radiation sources or human survivors of disasters using aerial and/or ground robots (agents).

Bayesian Optimization Compressive Sensing +1

Neural Dynamical Systems: Balancing Structure and Flexibility in Physical Prediction

no code implementations23 Jun 2020 Viraj Mehta, Ian Char, Willie Neiswanger, Youngseog Chung, Andrew Oakleigh Nelson, Mark D Boyer, Egemen Kolemen, Jeff Schneider

We introduce Neural Dynamical Systems (NDS), a method of learning dynamical models in various gray-box settings which incorporates prior knowledge in the form of systems of ordinary differential equations.

Neural Dynamical Systems

no code implementations ICLR Workshop DeepDiffEq 2019 Viraj Mehta, Ian Char, Willie Neiswanger, Youngseog Chung, Andrew Oakleigh Nelson, Mark D Boyer, Egemen Kolemen, Jeff Schneider

We introduce Neural Dynamical Systems (NDS), a method of learning dynamical models which incorporates prior knowledge in the form of systems of ordinary differential equations.

Human Driver Behavior Prediction based on UrbanFlow

no code implementations9 Nov 2019 Zhiqian Qiao, Jing Zhao, Zachariah Tyree, Priyantha Mudalige, Jeff Schneider, John M. Dolan

How autonomous vehicles and human drivers share public transportation systems is an important problem, as fully automatic transportation environments are still a long way off.

Autonomous Vehicles Decision Making +1

Hierarchical Reinforcement Learning Method for Autonomous Vehicle Behavior Planning

no code implementations9 Nov 2019 Zhiqian Qiao, Zachariah Tyree, Priyantha Mudalige, Jeff Schneider, John M. Dolan

In this work, we propose a hierarchical reinforcement learning (HRL) structure which is capable of performing autonomous vehicle planning tasks in simulated environments with multiple sub-goals.

Hierarchical Reinforcement Learning reinforcement-learning +1

ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations

1 code implementation5 Aug 2019 Ksenia Korovina, Sailun Xu, Kirthevasan Kandasamy, Willie Neiswanger, Barnabas Poczos, Jeff Schneider, Eric P. Xing

In applications such as molecule design or drug discovery, it is desirable to have an algorithm which recommends new candidate molecules based on the results of past tests.

Bayesian Optimization Drug Discovery

Deep Kinematic Models for Kinematically Feasible Vehicle Trajectory Predictions

no code implementations1 Aug 2019 Henggang Cui, Thi Nguyen, Fang-Chieh Chou, Tsung-Han Lin, Jeff Schneider, David Bradley, Nemanja Djuric

Self-driving vehicles (SDVs) hold great potential for improving traffic safety and are poised to positively affect the quality of life of millions of people.

motion prediction

Predicting Motion of Vulnerable Road Users using High-Definition Maps and Efficient ConvNets

1 code implementation20 Jun 2019 Fang-Chieh Chou, Tsung-Han Lin, Henggang Cui, Vladan Radosavljevic, Thi Nguyen, Tzu-Kuo Huang, Matthew Niedoba, Jeff Schneider, Nemanja Djuric

Following detection and tracking of traffic actors, prediction of their future motion is the next critical component of a self-driving vehicle (SDV) technology, allowing the SDV to operate safely and efficiently in its environment.

motion prediction

Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly

1 code implementation15 Mar 2019 Kirthevasan Kandasamy, Karun Raju Vysyaraju, Willie Neiswanger, Biswajit Paria, Christopher R. Collins, Jeff Schneider, Barnabas Poczos, Eric P. Xing

We compare Dragonfly to a suite of other packages and algorithms for global optimisation and demonstrate that when the above methods are integrated, they enable significant improvements in the performance of BO.

Bayesian Optimisation

ProBO: Versatile Bayesian Optimization Using Any Probabilistic Programming Language

1 code implementation31 Jan 2019 Willie Neiswanger, Kirthevasan Kandasamy, Barnabas Poczos, Jeff Schneider, Eric Xing

Optimizing an expensive-to-query function is a common task in science and engineering, where it is beneficial to keep the number of queries to a minimum.

Bayesian Optimization Gaussian Processes +1

Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks

2 code implementations18 Sep 2018 Henggang Cui, Vladan Radosavljevic, Fang-Chieh Chou, Tsung-Han Lin, Thi Nguyen, Tzu-Kuo Huang, Jeff Schneider, Nemanja Djuric

Autonomous driving presents one of the largest problems that the robotics and artificial intelligence communities are facing at the moment, both in terms of difficulty and potential societal impact.

Autonomous Driving Motion Planning +1

Uncertainty-aware Short-term Motion Prediction of Traffic Actors for Autonomous Driving

no code implementations17 Aug 2018 Nemanja Djuric, Vladan Radosavljevic, Henggang Cui, Thi Nguyen, Fang-Chieh Chou, Tsung-Han Lin, Nitin Singh, Jeff Schneider

We address one of the crucial aspects necessary for safe and efficient operations of autonomous vehicles, namely predicting future state of traffic actors in the autonomous vehicle's surroundings.

Autonomous Driving motion prediction

Myopic Bayesian Design of Experiments via Posterior Sampling and Probabilistic Programming

1 code implementation25 May 2018 Kirthevasan Kandasamy, Willie Neiswanger, Reed Zhang, Akshay Krishnamurthy, Jeff Schneider, Barnabas Poczos

We design a new myopic strategy for a wide class of sequential design of experiment (DOE) problems, where the goal is to collect data in order to to fulfil a certain problem specific goal.

Multi-Armed Bandits Probabilistic Programming +2

Neural Architecture Search with Bayesian Optimisation and Optimal Transport

1 code implementation NeurIPS 2018 Kirthevasan Kandasamy, Willie Neiswanger, Jeff Schneider, Barnabas Poczos, Eric Xing

A common use case for BO in machine learning is model selection, where it is not possible to analytically model the generalisation performance of a statistical model, and we resort to noisy and expensive training and validation procedures to choose the best model.

Bayesian Optimisation BIG-bench Machine Learning +2

Transformation Autoregressive Networks

no code implementations ICML 2018 Junier B. Oliva, Avinava Dubey, Manzil Zaheer, Barnabás Póczos, Ruslan Salakhutdinov, Eric P. Xing, Jeff Schneider

Further, through a comprehensive study over both real world and synthetic data, we show for that jointly leveraging transformations of variables and autoregressive conditional models, results in a considerable improvement in performance.

Density Estimation Outlier Detection

Estimating Cosmological Parameters from the Dark Matter Distribution

no code implementations6 Nov 2017 Siamak Ravanbakhsh, Junier Oliva, Sebastien Fromenteau, Layne C. Price, Shirley Ho, Jeff Schneider, Barnabas Poczos

A major approach to estimating the cosmological parameters is to use the large-scale matter distribution of the Universe.

Asynchronous Parallel Bayesian Optimisation via Thompson Sampling

1 code implementation25 May 2017 Kirthevasan Kandasamy, Akshay Krishnamurthy, Jeff Schneider, Barnabas Poczos

We design and analyse variations of the classical Thompson sampling (TS) procedure for Bayesian optimisation (BO) in settings where function evaluations are expensive, but can be performed in parallel.

Bayesian Optimisation Thompson Sampling

Scaling Active Search using Linear Similarity Functions

1 code implementation30 Apr 2017 Sibi Venkatesan, James K. Miller, Jeff Schneider, Artur Dubrawski

In this paper, we consider the problem of Active Search where we are given a similarity function between data points.

Information Retrieval Retrieval

Multi-fidelity Bayesian Optimisation with Continuous Approximations

no code implementations ICML 2017 Kirthevasan Kandasamy, Gautam Dasarathy, Jeff Schneider, Barnabas Poczos

Bandit methods for black-box optimisation, such as Bayesian optimisation, are used in a variety of applications including hyper-parameter tuning and experiment design.

Bayesian Optimisation

The Statistical Recurrent Unit

2 code implementations ICML 2017 Junier B. Oliva, Barnabas Poczos, Jeff Schneider

Sophisticated gated recurrent neural network architectures like LSTMs and GRUs have been shown to be highly effective in a myriad of applications.

Bayesian Optimization

Equivariance Through Parameter-Sharing

1 code implementation ICML 2017 Siamak Ravanbakhsh, Jeff Schneider, Barnabas Poczos

We propose to study equivariance in deep neural networks through parameter symmetries.

Active Search for Sparse Signals with Region Sensing

no code implementations2 Dec 2016 Yifei Ma, Roman Garnett, Jeff Schneider

Autonomous systems can be used to search for sparse signals in a large space; e. g., aerial robots can be deployed to localize threats, detect gas leaks, or respond to distress calls.

Bayesian Optimization Compressive Sensing +1

Deep Learning with Sets and Point Clouds

no code implementations14 Nov 2016 Siamak Ravanbakhsh, Jeff Schneider, Barnabas Poczos

We introduce a simple permutation equivariant layer for deep learning with set structure. This type of layer, obtained by parameter-sharing, has a simple implementation and linear-time complexity in the size of each set.

Clustering General Classification +1

The Multi-fidelity Multi-armed Bandit

no code implementations NeurIPS 2016 Kirthevasan Kandasamy, Gautam Dasarathy, Jeff Schneider, Barnabás Póczos

We study a variant of the classical stochastic $K$-armed bandit where observing the outcome of each arm is expensive, but cheap approximations to this outcome are available.

Enabling Dark Energy Science with Deep Generative Models of Galaxy Images

no code implementations19 Sep 2016 Siamak Ravanbakhsh, Francois Lanusse, Rachel Mandelbaum, Jeff Schneider, Barnabas Poczos

To this end, we study the application of deep conditional generative models in generating realistic galaxy images.

Detecting Damped Lyman-$α$ Absorbers with Gaussian Processes

4 code implementations14 May 2016 Roman Garnett, Shirley Ho, Simeon Bird, Jeff Schneider

We develop an automated technique for detecting damped Lyman-$\alpha$ absorbers (DLAs) along spectroscopic lines of sight to quasi-stellar objects (QSOs or quasars).

Cosmology and Nongalactic Astrophysics Data Analysis, Statistics and Probability

Stochastic Neural Networks with Monotonic Activation Functions

no code implementations1 Jan 2016 Siamak Ravanbakhsh, Barnabas Poczos, Jeff Schneider, Dale Schuurmans, Russell Greiner

We propose a Laplace approximation that creates a stochastic unit from any smooth monotonic activation function, using only Gaussian noise.

Deep Mean Maps

no code implementations13 Nov 2015 Junier B. Oliva, Danica J. Sutherland, Barnabás Póczos, Jeff Schneider

The use of distributions and high-level features from deep architecture has become commonplace in modern computer vision.

Linear-time Learning on Distributions with Approximate Kernel Embeddings

no code implementations24 Sep 2015 Danica J. Sutherland, Junier B. Oliva, Barnabás Póczos, Jeff Schneider

This work develops the first random features for pdfs whose dot product approximates kernels using these non-Euclidean metrics, allowing estimators using such kernels to scale to large datasets by working in a primal space, without computing large Gram matrices.

BIG-bench Machine Learning

Bayesian Nonparametric Kernel-Learning

no code implementations29 Jun 2015 Junier Oliva, Avinava Dubey, Andrew G. Wilson, Barnabas Poczos, Jeff Schneider, Eric P. Xing

In this paper we introduce Bayesian nonparmetric kernel-learning (BaNK), a generic, data-driven framework for scalable learning of kernels.

On the Error of Random Fourier Features

no code implementations9 Jun 2015 Dougal J. Sutherland, Jeff Schneider

Kernel methods give powerful, flexible, and theoretically grounded approaches to solving many problems in machine learning.

BIG-bench Machine Learning

High Dimensional Bayesian Optimisation and Bandits via Additive Models

no code implementations5 Mar 2015 Kirthevasan Kandasamy, Jeff Schneider, Barnabas Poczos

We prove that, for additive functions the regret has only linear dependence on $D$ even though the function depends on all $D$ dimensions.

Additive models Bayesian Optimisation +2

Flexible Transfer Learning under Support and Model Shift

no code implementations NeurIPS 2014 Xuezhi Wang, Jeff Schneider

Similarly, work on target/conditional shift focuses on matching marginal distributions on labels $Y$ and adjusting conditional distributions $P(X|Y)$, such that $P(X)$ can be matched across domains.

Test Transfer Learning

Fast Function to Function Regression

no code implementations27 Oct 2014 Junier Oliva, Willie Neiswanger, Barnabas Poczos, Eric Xing, Jeff Schneider

Function to function regression (FFR) covers a large range of interesting applications including time-series prediction problems, and also more general tasks like studying a mapping between two separate types of distributions.

regression Time Series +1

A Machine Learning Approach for Dynamical Mass Measurements of Galaxy Clusters

no code implementations2 Oct 2014 Michelle Ntampaka, Hy Trac, Dougal J. Sutherland, Nicholas Battaglia, Barnabas Poczos, Jeff Schneider

In the conventional method, we use a standard M(sigma_v) power law scaling relation to infer cluster mass, M, from line-of-sight (LOS) galaxy velocity dispersion, sigma_v.

Cosmology and Nongalactic Astrophysics

Learning Hidden Markov Models from Non-sequence Data via Tensor Decomposition

no code implementations NeurIPS 2013 Tzu-Kuo Huang, Jeff Schneider

Under that framework, we identify reasonable assumptions on the generative process of non-sequence data, and propose learning algorithms based on the tensor decomposition method \cite{anandkumar2012tensor} to \textit{provably} recover first-order Markov models and hidden Markov models.

Tensor Decomposition

Σ-Optimality for Active Learning on Gaussian Random Fields

no code implementations NeurIPS 2013 Yifei Ma, Roman Garnett, Jeff Schneider

For active learning on GRFs, the commonly used V-optimality criterion queries nodes that reduce the L2 (regression) loss.

Active Learning General Classification +1

Fast Distribution To Real Regression

no code implementations10 Nov 2013 Junier B. Oliva, Willie Neiswanger, Barnabas Poczos, Jeff Schneider, Eric Xing

We study the problem of distribution to real-value regression, where one aims to regress a mapping $f$ that takes in a distribution input covariate $P\in \mathcal{I}$ (for a non-parametric family of distributions $\mathcal{I}$) and outputs a real-valued response $Y=f(P) + \epsilon$.


FuSSO: Functional Shrinkage and Selection Operator

no code implementations10 Nov 2013 Junier B. Oliva, Barnabas Poczos, Timothy Verstynen, Aarti Singh, Jeff Schneider, Fang-Cheng Yeh, Wen-Yih Tseng

We present the FuSSO, a functional analogue to the LASSO, that efficiently finds a sparse set of functional input covariates to regress a real-valued response against.

A First Look at creating mock catalogs with machine learning techniques

no code implementations5 Mar 2013 Xiaoying Xu, Shirley Ho, Hy Trac, Jeff Schneider, Barnabas Poczos, Michelle Ntampaka

We investigate machine learning (ML) techniques for predicting the number of galaxies (N_gal) that occupy a halo, given the halo's properties.

Cosmology and Nongalactic Astrophysics

Bayesian Optimal Active Search and Surveying

no code implementations27 Jun 2012 Roman Garnett, Yamuna Krishnamurthy, Xuehan Xiong, Jeff Schneider, Richard Mann

In the second, active surveying, our goal is to actively query points to ultimately predict the proportion of a given class.

Binary Classification

Kernels on Sample Sets via Nonparametric Divergence Estimates

no code implementations1 Feb 2012 Danica J. Sutherland, Liang Xiong, Barnabás Póczos, Jeff Schneider

Most machine learning algorithms, such as classification or regression, treat the individual data point as the object of interest.

Anomaly Detection BIG-bench Machine Learning +2

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