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Researcher profile

Scott Sanner

Scott Sanner contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Artificial IntelligenceMachine LearningInformation RetrievalRoboticsComputation and LanguageComputer VisionNeural and Evolutionary Computingeess.SYHuman-Computer InteractionSystems and Control

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Published work

13 published item(s)

preprint2026arXiv

CommonWhy: A Dataset for Evaluating Entity-Based Causal Commonsense Reasoning in Large Language Models

To effectively interact with the real world, Large Language Models (LLMs) require entity-based commonsense reasoning, a challenging task that necessitates integrating factual knowledge about specific entities with commonsense inference. Existing datasets for evaluating LLM entity-based commonsense reasoning have largely focused on True/False or multiple-choice questions, leaving the explicit assessment of the model's ability in abductive reasoning about causes and effects and generating explanations largely unexamined. In this work, we introduce CommonWhy, a dataset of 15,000 why questions designed to evaluate entity-based commonsense reasoning about causal relationships in LLMs. CommonWhy also serves as a Knowledge Graph Question Answering (KGQA) benchmark, as all supporting knowledge required to answer its queries is available in the Wikidata knowledge graph. Unlike existing KGQA datasets, which primarily test fact retrieval, CommonWhy targets causal commonsense reasoning, establishing a new paradigm for KGQA evaluation. Experiments with state-of-the-art LLMs and LLM-based KGQA methods reveal their significant shortcomings, including frequent factual hallucinations and failures in causal reasoning.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Goal-Oriented Reasoning for RAG-based Memory in Conversational Agentic LLM Systems

LLM-based conversational AI agents struggle to maintain coherent behavior over long horizons due to limited context. While RAG-based approaches are increasingly adopted to overcome this limitation by storing interactions in external memory modules and performing retrieval from them, their effectiveness in answering challenging questions (e.g., multi-hop, commonsense) ultimately depends on the agent's ability to reason over the retrieved information. However, existing methods typically retrieve memory based on semantic similarity to the raw user utterance, which lacks explicit reasoning about missing intermediate facts and often returns evidence that is irrelevant or insufficient for grounded reasoning. In this work, we introduce Goal-Mem, a goal-oriented reasoning framework for RAG-based agentic memory that performs explicit backward chaining from the user's utterance as a goal. Rather than progressively expanding from retrieved context, Goal-Mem decomposes each goal into atomic subgoals, performs targeted memory retrieval to satisfy each subgoal, and iteratively identifies what information from memory should be retrieved when intermediate goals cannot be resolved. We formalize this process in Natural Language Logic, a logical system that combines the verifiability of reasoning provided by FOL with the expressivity of natural language. Through extensive experiments on two datasets and comparing to nine strong memory baselines, we show that Goal-Mem consistently improves performance, particularly on tasks requiring multi-hop reasoning and implicit inference.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

ExCon: Explanation-driven Supervised Contrastive Learning for Image Classification

Contrastive learning has led to substantial improvements in the quality of learned embedding representations for tasks such as image classification. However, a key drawback of existing contrastive augmentation methods is that they may lead to the modification of the image content which can yield undesired alterations of its semantics. This can affect the performance of the model on downstream tasks. Hence, in this paper, we ask whether we can augment image data in contrastive learning such that the task-relevant semantic content of an image is preserved. For this purpose, we propose to leverage saliency-based explanation methods to create content-preserving masked augmentations for contrastive learning. Our novel explanation-driven supervised contrastive learning (ExCon) methodology critically serves the dual goals of encouraging nearby image embeddings to have similar content and explanation. To quantify the impact of ExCon, we conduct experiments on the CIFAR-100 and the Tiny ImageNet datasets. We demonstrate that ExCon outperforms vanilla supervised contrastive learning in terms of classification, explanation quality, adversarial robustness as well as probabilistic calibration in the context of distributional shift.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Sample-efficient Iterative Lower Bound Optimization of Deep Reactive Policies for Planning in Continuous MDPs

Recent advances in deep learning have enabled optimization of deep reactive policies (DRPs) for continuous MDP planning by encoding a parametric policy as a deep neural network and exploiting automatic differentiation in an end-to-end model-based gradient descent framework. This approach has proven effective for optimizing DRPs in nonlinear continuous MDPs, but it requires a large number of sampled trajectories to learn effectively and can suffer from high variance in solution quality. In this work, we revisit the overall model-based DRP objective and instead take a minorization-maximization perspective to iteratively optimize the DRP w.r.t. a locally tight lower-bounded objective. This novel formulation of DRP learning as iterative lower bound optimization (ILBO) is particularly appealing because (i) each step is structurally easier to optimize than the overall objective, (ii) it guarantees a monotonically improving objective under certain theoretical conditions, and (iii) it reuses samples between iterations thus lowering sample complexity. Empirical evaluation confirms that ILBO is significantly more sample-efficient than the state-of-the-art DRP planner and consistently produces better solution quality with lower variance. We additionally demonstrate that ILBO generalizes well to new problem instances (i.e., different initial states) without requiring retraining.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Unintended Bias in Language Model-driven Conversational Recommendation

Conversational Recommendation Systems (CRSs) have recently started to leverage pretrained language models (LM) such as BERT for their ability to semantically interpret a wide range of preference statement variations. However, pretrained LMs are well-known to be prone to intrinsic biases in their training data, which may be exacerbated by biases embedded in domain-specific language data(e.g., user reviews) used to fine-tune LMs for CRSs. We study a recently introduced LM-driven recommendation backbone (termed LMRec) of a CRS to investigate how unintended bias i.e., language variations such as name references or indirect indicators of sexual orientation or location that should not affect recommendations manifests in significantly shifted price and category distributions of restaurant recommendations. The alarming results we observe strongly indicate that LMRec has learned to reinforce harmful stereotypes through its recommendations. For example, offhand mention of names associated with the black community significantly lowers the price distribution of recommended restaurants, while offhand mentions of common male-associated names lead to an increase in recommended alcohol-serving establishments. These and many related results presented in this work raise a red flag that advances in the language handling capability of LM-drivenCRSs do not come without significant challenges related to mitigating unintended bias in future deployed CRS assistants with a potential reach of hundreds of millions of end-users.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Batch-level Experience Replay with Review for Continual Learning

Continual learning is a branch of deep learning that seeks to strike a balance between learning stability and plasticity. The CVPR 2020 CLVision Continual Learning for Computer Vision challenge is dedicated to evaluating and advancing the current state-of-the-art continual learning methods using the CORe50 dataset with three different continual learning scenarios. This paper presents our approach, called Batch-level Experience Replay with Review, to this challenge. Our team achieved the 1'st place in all three scenarios out of 79 participated teams. The codebase of our implementation is publicly available at https://github.com/RaptorMai/CVPR20_CLVision_challenge

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Bayesian Experience Reuse for Learning from Multiple Demonstrators

Learning from demonstrations (LfD) improves the exploration efficiency of a learning agent by incorporating demonstrations from experts. However, demonstration data can often come from multiple experts with conflicting goals, making it difficult to incorporate safely and effectively in online settings. We address this problem in the static and dynamic optimization settings by modelling the uncertainty in source and target task functions using normal-inverse-gamma priors, whose corresponding posteriors are, respectively, learned from demonstrations and target data using Bayesian neural networks with shared features. We use this learned belief to derive a quadratic programming problem whose solution yields a probability distribution over the expert models. Finally, we propose Bayesian Experience Reuse (BERS) to sample demonstrations in accordance with this distribution and reuse them directly in new tasks. We demonstrate the effectiveness of this approach for static optimization of smooth functions, and transfer learning in a high-dimensional supply chain problem with cost uncertainty.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Compact and Efficient Encodings for Planning in Factored State and Action Spaces with Learned Binarized Neural Network Transition Models

In this paper, we leverage the efficiency of Binarized Neural Networks (BNNs) to learn complex state transition models of planning domains with discretized factored state and action spaces. In order to directly exploit this transition structure for planning, we present two novel compilations of the learned factored planning problem with BNNs based on reductions to Weighted Partial Maximum Boolean Satisfiability (FD-SAT-Plan+) as well as Binary Linear Programming (FD-BLP-Plan+). Theoretically, we show that our SAT-based Bi-Directional Neuron Activation Encoding is asymptotically the most compact encoding relative to the current literature and supports Unit Propagation (UP) -- an important property that facilitates efficiency in SAT solvers. Experimentally, we validate the computational efficiency of our Bi-Directional Neuron Activation Encoding in comparison to an existing neuron activation encoding and demonstrate the ability to learn complex transition models with BNNs. We test the runtime efficiency of both FD-SAT-Plan+ and FD-BLP-Plan+ on the learned factored planning problem showing that FD-SAT-Plan+ scales better with increasing BNN size and complexity. Finally, we present a finite-time incremental constraint generation algorithm based on generalized landmark constraints to improve the planning accuracy of our encodings through simulated or real-world interaction.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Contextual Policy Transfer in Reinforcement Learning Domains via Deep Mixtures-of-Experts

In reinforcement learning, agents that consider the context, or current state, when selecting source policies for transfer have been shown to outperform context-free approaches. However, none of the existing approaches transfer knowledge contextually from model-based learners to a model-free learner. This could be useful, for instance, when source policies are intentionally learned on diverse simulations with plentiful data but transferred to a real-world setting with limited data. In this paper, we assume knowledge of estimated source task dynamics and policies, and common sub-goals but different dynamics. We introduce a novel deep mixture-of-experts formulation for learning state-dependent beliefs over source task dynamics that match the target dynamics using state trajectories collected from the target task. The mixture model is easy to interpret, demonstrates robustness to estimation errors in dynamics, and is compatible with most learning algorithms. We then show how this model can be incorporated into standard policy reuse frameworks, and demonstrate its effectiveness on benchmarks from OpenAI-Gym.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Noise Contrastive Estimation for Autoencoding-based One-Class Collaborative Filtering

One-class collaborative filtering (OC-CF) is a common class of recommendation problem where only the positive class is explicitly observed (e.g., purchases, clicks). Autoencoder based recommenders such as AutoRec and variants demonstrate strong performance on many OC-CF benchmarks, but also empirically suffer from a strong popularity bias. While a careful choice of negative samples in the OC-CF setting can mitigate popularity bias, Negative Sampling (NS) is often better for training embeddings than for the end task itself. To address this, we propose a two-headed AutoRec to first train an embedding layer via one head using Negative Sampling then to train for the final task via the second head. While this NS-AutoRec improves results for AutoRec and outperforms many state-of-the-art baselines on OC-CF problems, we notice that Negative Sampling can still take a large amount of time to train. Since Negative Sampling is known to be a special case of Noise Contrastive Estimation (NCE), we adapt a recently proposed closed-form NCE solution for collaborative filtering to AutoRec yielding NCE-AutoRec. Overall, we show that our novel two-headed AutoRec models (NCE-AutoRec and NS-AutoRec) successfully mitigate the popularity bias issue and maintain competitive performance in comparison to state-of-the-art recommenders on multiple real-world datasets.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Optimizing Search API Queries for Twitter Topic Classifiers Using a Maximum Set Coverage Approach

Twitter has grown to become an important platform to access immediate information about major events and dynamic topics. As one example, recent work has shown that classifiers trained to detect topical content on Twitter can generalize well beyond the training data. Since access to Twitter data is hidden behind a limited search API, it is impossible (for most users) to apply these classifiers directly to the Twitter unfiltered data streams ("firehose"). Rather, applications must first decide what content to retrieve through the search API before filtering that content with topical classifiers. Thus, it is critically important to query the Twitter API relative to the intended topical classifier in a way that minimizes the amount of negatively classified data retrieved. In this paper, we propose a sequence of query optimization methods that generalize notions of the maximum coverage problem to find the subset of query terms within the API limits that cover most of the topically relevant tweets without sacrificing precision. We evaluate the proposed methods on a large dataset of Twitter data collected during 2013 and 2014 labeled using manually curated hashtags for eight topics. Among many insights, our analysis shows that the best of the proposed methods can significantly outperform the firehose on precision and F1-score while achieving high recall within strict API limitations.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Scalable Planning with Deep Neural Network Learned Transition Models

In many real-world planning problems with factored, mixed discrete and continuous state and action spaces such as Reservoir Control, Heating Ventilation, and Air Conditioning, and Navigation domains, it is difficult to obtain a model of the complex nonlinear dynamics that govern state evolution. However, the ubiquity of modern sensors allows us to collect large quantities of data from each of these complex systems and build accurate, nonlinear deep neural network models of their state transitions. But there remains one major problem for the task of control -- how can we plan with deep network learned transition models without resorting to Monte Carlo Tree Search and other black-box transition model techniques that ignore model structure and do not easily extend to mixed discrete and continuous domains? In this paper, we introduce two types of nonlinear planning methods that can leverage deep neural network learned transition models: Hybrid Deep MILP Planner (HD-MILP-Plan) and Tensorflow Planner (TF-Plan). In HD-MILP-Plan, we make the critical observation that the Rectified Linear Unit transfer function for deep networks not only allows faster convergence of model learning, but also permits a direct compilation of the deep network transition model to a Mixed-Integer Linear Program encoding. Further, we identify deep network specific optimizations for HD-MILP-Plan that improve performance over a base encoding and show that we can plan optimally with respect to the learned deep networks. In TF-Plan, we take advantage of the efficiency of auto-differentiation tools and GPU-based computation where we encode a subclass of purely continuous planning problems as Recurrent Neural Networks and directly optimize the actions through backpropagation. We compare both planners and show that TF-Plan is able to approximate the optimal plans found by HD-MILP-Plan in less computation time...

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

ε-BMC: A Bayesian Ensemble Approach to Epsilon-Greedy Exploration in Model-Free Reinforcement Learning

Resolving the exploration-exploitation trade-off remains a fundamental problem in the design and implementation of reinforcement learning (RL) algorithms. In this paper, we focus on model-free RL using the epsilon-greedy exploration policy, which despite its simplicity, remains one of the most frequently used forms of exploration. However, a key limitation of this policy is the specification of $\varepsilon$. In this paper, we provide a novel Bayesian perspective of $\varepsilon$ as a measure of the uniformity of the Q-value function. We introduce a closed-form Bayesian model update based on Bayesian model combination (BMC), based on this new perspective, which allows us to adapt $\varepsilon$ using experiences from the environment in constant time with monotone convergence guarantees. We demonstrate that our proposed algorithm, $\varepsilon$-\texttt{BMC}, efficiently balances exploration and exploitation on different problems, performing comparably or outperforming the best tuned fixed annealing schedules and an alternative data-dependent $\varepsilon$ adaptation scheme proposed in the literature.

0 citations0 reviewsNo code linkedOpen access