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Zoran Popovic

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7 papers

2 author rows

NeurIPS Conference 2023 Conference Paper

Benchmarking Distribution Shift in Tabular Data with TableShift

Josh Gardner
Zoran Popovic
Ludwig Schmidt

Robustness to distribution shift has become a growing concern for text and image models as they transition from research subjects to deployment in the real world. However, high-quality benchmarks for distribution shift in tabular machine learning tasks are still lacking despite the widespread real-world use of tabular data and differences in the models used for tabular data in comparison to text and images. As a consequence, the robustness of tabular models to distribution shift is poorly understood. To address this issue, we introduce TableShift, a distribution shift benchmark for tabular data. TableShift contains 15 binary classification tasks in total, each with an associated shift, and includes a diverse set of data sources, prediction targets, and distribution shifts. The benchmark covers domains including finance, education, public policy, healthcare, and civic participation, and is accessible using only a few lines of Python code via the TableShift API. We conduct a large-scale study comparing several state-of-the-art tabular data models alongside robust learning and domain generalization methods on the benchmark tasks. Our study demonstrates (1) a linear trend between in-distribution (ID) and out-of-distribution (OOD) accuracy; (2) domain robustness methods can reduce shift gaps but at the cost of reduced ID accuracy; (3) a strong relationship between shift gap (difference between ID and OOD performance) and shifts in the label distribution. The benchmark data, Python package, model implementations, and more information about TableShift are available at https: //github. com/mlfoundations/tableshift and https: //tableshift. org.

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NeurIPS Conference 2022 Conference Paper

Subgroup Robustness Grows On Trees: An Empirical Baseline Investigation

Josh Gardner
Zoran Popovic
Ludwig Schmidt

Researchers have proposed many methods for fair and robust machine learning, but comprehensive empirical evaluation of their subgroup robustness is lacking. In this work, we address this gap in the context of tabular data, where sensitive subgroups are clearly-defined, real-world fairness problems abound, and prior works often do not compare to state-of-the-art tree-based models as baselines. We conduct an empirical comparison of several previously-proposed methods for fair and robust learning alongside state-of-the-art tree-based methods and other baselines. Via experiments with more than $340{, }000$ model configurations on eight datasets, we show that tree-based methods have strong subgroup robustness, even when compared to robustness- and fairness-enhancing methods. Moreover, the best tree-based models tend to show good performance over a range of metrics, while robust or group-fair models can show brittleness, with significant performance differences across different metrics for a fixed model. We also demonstrate that tree-based models show less sensitivity to hyperparameter configurations, and are less costly to train. Our work suggests that tree-based ensemble models make an effective baseline for tabular data, and are a sensible default when subgroup robustness is desired. See https: //github. com/jpgard/subgroup-robustness-grows-on-trees for code to reproduce our experiments and detailed experimental results.

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IJCAI Conference 2016 Conference Paper

Efficient Bayesian Clustering for Reinforcement Learning

Travis Mandel
Yun-En Liu
Emma Brunskill
Zoran Popovic

A fundamental artificial intelligence challenge is how to design agents that intelligently trade off exploration and exploitation while quickly learning about an unknown environment. However, in order to learn quickly, we must somehow generalize experience across states. One promising approach is to use Bayesian methods to simultaneously cluster dynamics and control exploration; unfortunately, these methods tend to require computationally intensive MCMC approximation techniques which lack guarantees. We propose Thompson Clustering for Reinforcement Learning (TCRL), a family of Bayesian clustering algorithms for reinforcement learning that leverage structure in the state space to remain computationally efficient while controlling both exploration and generalization. TCRL-Theoretic achieves near-optimal Bayesian regret bounds while consistently improving over a standard Bayesian exploration approach. TCRL-Relaxed is guaranteed to converge to acting optimally, and empirically outperforms state-of-the-art Bayesian clustering algorithms across a variety of simulated domains, even in cases where no states are similar.

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NeurIPS Conference 2015 Conference Paper

Interactive Control of Diverse Complex Characters with Neural Networks

Igor Mordatch
Kendall Lowrey
Galen Andrew
Zoran Popovic
Emanuel Todorov

We present a method for training recurrent neural networks to act as near-optimal feedback controllers. It is able to generate stable and realistic behaviors for a range of dynamical systems and tasks -- swimming, flying, biped and quadruped walking with different body morphologies. It does not require motion capture or task-specific features or state machines. The controller is a neural network, having a large number of feed-forward units that learn elaborate state-action mappings, and a small number of recurrent units that implement memory states beyond the physical system state. The action generated by the network is defined as velocity. Thus the network is not learning a control policy, but rather the dynamics under an implicit policy. Essential features of the method include interleaving supervised learning with trajectory optimization, injecting noise during training, training for unexpected changes in the task specification, and using the trajectory optimizer to obtain optimal feedback gains in addition to optimal actions.

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NeurIPS Conference 2011 Conference Paper

Nonlinear Inverse Reinforcement Learning with Gaussian Processes

Sergey Levine
Zoran Popovic
Vladlen Koltun

We present a probabilistic algorithm for nonlinear inverse reinforcement learning. The goal of inverse reinforcement learning is to learn the reward function in a Markov decision process from expert demonstrations. While most prior inverse reinforcement learning algorithms represent the reward as a linear combination of a set of features, we use Gaussian processes to learn the reward as a nonlinear function, while also determining the relevance of each feature to the expert's policy. Our probabilistic algorithm allows complex behaviors to be captured from suboptimal stochastic demonstrations, while automatically balancing the simplicity of the learned reward structure against its consistency with the observed actions.

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NeurIPS Conference 2010 Conference Paper

Feature Construction for Inverse Reinforcement Learning

Sergey Levine
Zoran Popovic
Vladlen Koltun

The goal of inverse reinforcement learning is to find a reward function for a Markov decision process, given example traces from its optimal policy. Current IRL techniques generally rely on user-supplied features that form a concise basis for the reward. We present an algorithm that instead constructs reward features from a large collection of component features, by building logical conjunctions of those component features that are relevant to the example policy. Given example traces, the algorithm returns a reward function as well as the constructed features. The reward function can be used to recover a full, deterministic, stationary policy, and the features can be used to transplant the reward function into any novel environment on which the component features are well defined.

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ICRA Conference 2000 Conference Paper

Editing Dynamic Properties of Captured Human Motion

Zoran Popovic

In contrast to most physically based animation techniques that synthesize human motion from scratch, we take the approach of motion transformation as the underlying paradigm for generating computer animation. In doing so we combine the expressive richness of an input animation sequence with the controllability of space-time optimization to create a wide range of realistic character animation. The space-time dynamics formulation also allows editing of intuitive, high-level motion concepts such as the time and placement of footprints, length and mass of various extremities number of body joints and gravity. Our algorithm is well suited for the reuse of highly-detailed captured motion animation. We report application of our algorithm on two such sequences: human run and human jump. As a result, both of these sequences produced a wide range of realistic motions. We show by DOF comparison how closely the resulting motion matches the reality.

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