Author name cluster

Weiwei Pan

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12 papers

2 author rows

TMLR Journal 2025 Journal Article

Is What You Ask For What You Get? Investigating Concept Associations in Text-to-Image Models

Salma Abdel Magid
Weiwei Pan
Simon Warchol
Grace Guo
Junsik Kim
Mahia Rahman
Hanspeter Pfister

Text-to-image (T2I) models are increasingly used in impactful real-life applications. As such, there is a growing need to audit these models to ensure that they generate desirable, task-appropriate images. However, systematically inspecting the associations between prompts and generated content in a human-understandable way remains challenging. To address this, we propose \emph{Concept2Concept}, a framework where we characterize conditional distributions of vision language models using interpretable concepts and metrics that can be defined in terms of these concepts. This characterization allows us to use our framework to audit models and prompt-datasets. To demonstrate, we investigate several case studies of conditional distributions of prompts, such as user-defined distributions or empirical, real-world distributions. Lastly, we implement Concept2Concept as an open-source interactive visualization tool to facilitate use by non-technical end-users. A demo is available at https://tinyurl.com/Concept2ConceptDemo. Warning: This paper contains discussions of harmful content, including CSAM and NSFW material, which may be disturbing to some readers.

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UAI Conference 2025 Conference Paper

Transparent Trade-offs between Properties of Explanations

Hiwot Belay Tadesse
Alihan Hüyük
Yaniv Yacoby
Weiwei Pan
Finale Doshi

When explaining machine learning models, it is important for explanations to have certain properties like faithfulness, robustness, smoothness, low complexity, etc. However, many properties are in tension with each other, making it challenging to achieve them simultaneously. For example, reducing the complexity of an explanation can make it less expressive, compromising its faithfulness. The ideal balance of trade-offs between properties tends to vary across different tasks and users. Motivated by these varying needs, we aim to find explanations that make optimal trade-offs while allowing for transparent control over the balance between different properties. Unlike existing methods that encourage desirable properties implicitly through their design, our approach optimizes explanations explicitly for a linear mixture of multiple properties. By adjusting the mixture weights, users can control the balance between those properties and create explanations with precisely what is needed for their particular task.

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RLC Conference 2025 Conference Paper

When and Why Hyperbolic Discounting Matters for Reinforcement Learning Interventions

Ian M. Moore
Eura Nofshin
Siddharth Swaroop
Susan Murphy
Finale Doshi-Velez
Weiwei Pan

In settings where an AI agent nudges a human agent toward a goal, the AI can quickly learn a high-quality policy by modeling the human well. Despite behavioral evidence that humans hyperbolically discount future rewards, we model human as Markov Decision Processes (MDPs) with exponential discounting. This is because planning is difficult with non-exponential discounts. In this work, we investigate whether the performance benefits of modeling humans as hyperbolic discounters outweigh the computational costs. We focus on AI interventions that change the human's discounting (i. e. decreases the human's ""nearsightedness"" to help them toward distant goals). We derive a fixed exponential discount factor that can approximate hyperbolic discounting, and prove that this approximation guarantees the AI will never miss a necessary intervention. We also prove that our approximation causes fewer false positives (unnecessary interventions) than the mean hazard rate, another well-known method for approximating hyperbolic MDPs as exponential ones. Surprisingly, our experiments demonstrate that exponential approximations outperform hyperbolic ones in online learning, even when the ground-truth human MDP is hyperbolically discounted.

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RLJ Journal 2025 Journal Article

When and Why Hyperbolic Discounting Matters for Reinforcement Learning Interventions

Ian M. Moore
Eura Nofshin
Siddharth Swaroop
Susan Murphy
Finale Doshi-Velez
Weiwei Pan

In settings where an AI agent nudges a human agent toward a goal, the AI can quickly learn a high-quality policy by modeling the human well. Despite behavioral evidence that humans hyperbolically discount future rewards, we model human as Markov Decision Processes (MDPs) with exponential discounting. This is because planning is difficult with non-exponential discounts. In this work, we investigate whether the performance benefits of modeling humans as hyperbolic discounters outweigh the computational costs. We focus on AI interventions that change the human's discounting (i.e. decreases the human's ""nearsightedness"" to help them toward distant goals). We derive a fixed exponential discount factor that can approximate hyperbolic discounting, and prove that this approximation guarantees the AI will never miss a necessary intervention. We also prove that our approximation causes fewer false positives (unnecessary interventions) than the mean hazard rate, another well-known method for approximating hyperbolic MDPs as exponential ones. Surprisingly, our experiments demonstrate that exponential approximations outperform hyperbolic ones in online learning, even when the ground-truth human MDP is hyperbolically discounted.

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RLC Conference 2024 Conference Paper

Inverse Reinforcement Learning with Multiple Planning Horizons

Jiayu Yao
Weiwei Pan
Finale Doshi-Velez
Barbara E Engelhardt

In this work, we study an inverse reinforcement learning (IRL) problem where the experts are planning *under a shared reward function but with different, unknown planning horizons*. Without the knowledge of discount factors, the reward function has a larger feasible solution set, which makes it harder for existing IRL approaches to identify a reward function. To overcome this challenge, we develop algorithms that can learn a global multi-agent reward function with agent-specific discount factors that reconstruct the expert policies. We characterize the feasible solution space of the reward function and discount factors for both algorithms and demonstrate the generalizability of the learned reward function across multiple domains.

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RLJ Journal 2024 Journal Article

Inverse Reinforcement Learning with Multiple Planning Horizons

Jiayu Yao
Weiwei Pan
Finale Doshi-Velez
Barbara E Engelhardt

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AAMAS Conference 2024 Conference Paper

Reinforcement Learning Interventions on Boundedly Rational Human Agents in Frictionful Tasks

Eura Nofshin
Siddharth Swaroop
Weiwei Pan
Susan Murphy
Finale Doshi-Velez

Many important behavior changes are frictionful; they require individuals to expend effort over a long period with little immediate gratification. Here, an artificial intelligence (AI) agent can provide personalized interventions to help individuals stick to their goals. In these settings, the AI agent must personalize rapidly (before the individual disengages) and interpretably, to help us understand the behavioral interventions. In this paper, we introduce Behavior Model Reinforcement Learning (BMRL), a framework in which an AI agent intervenes on the parameters of a Markov Decision Process (MDP) belonging to a boundedly rational human agent. Our formulation of the human decision-maker as a planning agent allows us to attribute undesirable human policies (ones that do not lead to the goal) to their maladapted MDP parameters, such as an extremely low discount factor. Furthermore, we propose a class of tractable human models that captures fundamental behaviors in frictionful tasks. Introducing a notion of MDP equivalence specific to BMRL, we theoretically and empirically show that AI planning with our human models can lead to helpful policies on a wide range of more complex, ground-truth humans.

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JMLR Journal 2024 Journal Article

Rethinking Discount Regularization: New Interpretations, Unintended Consequences, and Solutions for Regularization in Reinforcement Learning

Sarah Rathnam
Sonali Parbhoo
Siddharth Swaroop
Weiwei Pan
Susan A. Murphy
Finale Doshi-Velez

Discount regularization, using a shorter planning horizon when calculating the optimal policy, is a popular choice to avoid overfitting when faced with sparse or noisy data. It is commonly interpreted as de-emphasizing or ignoring delayed effects. In this paper, we prove two alternative views of discount regularization that expose unintended consequences and motivate novel regularization methods. In model-based RL, planning under a lower discount factor acts like a prior with stronger regularization on state-action pairs with more transition data. This leads to poor performance when the transition matrix is estimated from data sets with uneven amounts of data across state-action pairs. In model-free RL, discount regularization equates to planning using a weighted average Bellman update, where the agent plans as if the values of all state-action pairs are closer than implied by the data. Our equivalence theorems motivate simple methods that generalize discount regularization by setting parameters locally for individual state-action pairs rather than globally. We demonstrate the failures of discount regularization and how we remedy them using our state-action-specific methods across empirical examples with both tabular and continuous state spaces. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2024. ( edit, beta )

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ICML Conference 2023 Conference Paper

The Unintended Consequences of Discount Regularization: Improving Regularization in Certainty Equivalence Reinforcement Learning

Sarah Rathnam
Sonali Parbhoo
Weiwei Pan
Susan A. Murphy
Finale Doshi

Discount regularization, using a shorter planning horizon when calculating the optimal policy, is a popular choice to restrict planning to a less complex set of policies when estimating an MDP from sparse or noisy data (Jiang et al. , 2015). It is commonly understood that discount regularization functions by de-emphasizing or ignoring delayed effects. In this paper, we reveal an alternate view of discount regularization that exposes unintended consequences. We demonstrate that planning under a lower discount factor produces an identical optimal policy to planning using any prior on the transition matrix that has the same distribution for all states and actions. In fact, it functions like a prior with stronger regularization on state-action pairs with more transition data. This leads to poor performance when the transition matrix is estimated from data sets with uneven amounts of data across state-action pairs. Our equivalence theorem leads to an explicit formula to set regularization parameters locally for individual state-action pairs rather than globally. We demonstrate the failures of discount regularization and how we remedy them using our state-action-specific method across simple empirical examples as well as a medical cancer simulator.

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JMLR Journal 2022 Journal Article

Mitigating the Effects of Non-Identifiability on Inference for Bayesian Neural Networks with Latent Variables

Yaniv Yacoby
Weiwei Pan
Finale Doshi-Velez

Bayesian Neural Networks with Latent Variables (BNN+LVs) capture predictive uncertainty by explicitly modeling model uncertainty (via priors on network weights) and environmental stochasticity (via a latent input noise variable). In this work, we first show that BNN+LV suffers from a serious form of non-identifiability: explanatory power can be transferred between the model parameters and latent variables while fitting the data equally well. We demonstrate that as a result, in the limit of infinite data, the posterior mode over the network weights and latent variables is asymptotically biased away from the ground-truth. Due to this asymptotic bias, traditional inference methods may in practice yield parameters that generalize poorly and misestimate uncertainty. Next, we develop a novel inference procedure that explicitly mitigates the effects of likelihood non-identifiability during training and yields high-quality predictions as well as uncertainty estimates. We demonstrate that our inference method improves upon benchmark methods across a range of synthetic and real data-sets. [abs] [ pdf ][ bib ] &copy JMLR 2022. ( edit, beta )

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UAI Conference 2021 Conference Paper

Efficient online inference for nonparametric mixture models

Rylan Schaeffer
Blake Bordelon
Mikail Khona
Weiwei Pan
Ila Rani Fiete

Natural data are often well-described as belonging to latent clusters. When the number of clusters is unknown, Bayesian nonparametric (BNP) models can provide a flexible and powerful technique to model the data. However, algorithms for inference in nonparametric mixture models fail to meet two critical requirements for practical use: (1) that inference can be performed online, and (2) that inference is efficient in the large time/sample limit. In this work, we propose a novel Bayesian recursion to efficiently infer a posterior distribution over discrete latent variables from a sequence of observations in an online manner, assuming a Chinese Restaurant Process prior on the sequence of latent variables. Our recursive filter, which we call the Recursive Chinese Restaurant Process (R-CRP), has quasilinear average time complexity and logarithmic average space complexity in the total number of observations. We experimentally compare our filtering method against both online and offline inference algorithms including Markov chain Monte Carlo, variational approximations and DP-Means, and demonstrate that our inference algorithm achieves comparable or better performance for a fraction of the runtime.

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AAAI Conference 2020 Conference Paper

Ensembles of Locally Independent Prediction Models

Andrew Ross
Weiwei Pan
Leo Celi
Finale Doshi-Velez

Ensembles depend on diversity for improved performance. Many ensemble training methods, therefore, attempt to optimize for diversity, which they almost always deﬁne in terms of differences in training set predictions. In this paper, however, we demonstrate the diversity of predictions on the training set does not necessarily imply diversity under mild covariate shift, which can harm generalization in practical settings. To address this issue, we introduce a new diversity metric and associated method of training ensembles of models that extrapolate differently on local patches of the data manifold. Across a variety of synthetic and real-world tasks, we ﬁnd that our method improves generalization and diversity in qualitatively novel ways, especially under data limits and covariate shift.

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