The Twelfth International Conference on Learning Representations (2024)
Embeddings have become a pivotal means to represent complex, multi-faceted information about entities, concepts, and relationships in a condensed and useful format. Nevertheless, they often preclude direct interpretation. While downstream tasks make use of these compressed representations, meaningful interpretation usually requires visualization using dimensionality reduction or specialized machine learning interpretability methods. This paper addresses the challenge of making such embeddings more interpretable and broadly useful, by employing large language models (LLMs) to directly interact with embeddings -- transforming abstract vectors into understandable narratives. By injecting embeddings into LLMs, we enable querying and exploration of complex embedding data. We demonstrate our approach on a variety of diverse tasks, including: enhancing concept activation vectors (CAVs), communicating novel embedded entities, and decoding user preferences in recommender systems. Our work couples the immense information potential of embeddings with the interpretative power of LLMs.View details
WWW22: The Web Conference 2022, Lyon, France, pp. 2411-2421
Interactive Recommender Systems (RSs) have emerged as a promising paradigm to overcome the limitations of the primitive user feedback used by traditional RSs (e.g., clicks, item consumption, ratings), allowing users to express intent, preferences, constraints, and contexts in a richer fashion using natural language. Still, more research is needed to find the most effective ways to use this feedback. One major challenge is inferring a user's intended semantic intent from given the open-ended terms (say, attributes or tags) used to describe a desired item, and utilize that to refine recommendation results.
Leveraging Concept Activation Vectors (CAVs) , we develop a framework to learn a representation that captures the semantics of such attributes and connect them to user preferences and behaviors in RSs. One novel feature of our approach is its ability to distinguish objective and subjective attributes (including subjectivity of degree and of sense) and associate different senses of subjective attributes with different user. We demonstrate on both synthetic and real-world datasets that our CAV representation not only accurately interprets users' subjective semantics, but can also be used to improve recommendations.View details
Proceedings of the 30th ACM SIGSPATIAL Intl. Conf. on Advances in Geographic Information Systems (SIGSPATIAL-22), Seattle, WA (2022) (to appear)
This paper considers the calibration of travel demand inputs, defined as a set of origin-destination matrices (ODs), for stochastic microscopic urban traffic simulators. The goal of calibration is to find a (set of) travel demand input(s) that replicate sparse field count data statistics. While traditional approaches use only first-order moment information from the field data, it is well known that the OD calibration problem is underdetermined in realistic networks. We study the value of using higher-order statistics from spatially sparse field data to mitigate underdetermination, proposing a variational inference technique that identifies an OD distribution. We apply our approach to a high-dimensional setting in Salt Lake City, Utah. Our approach is flexible—it can be readily extended to account for arbitrary types of field data (e.g., road, path or trip data).View details
Proceedings of the 38th International Conference on Machine Learning (ICML 2021), pp. 5884-5893
Efficient exploration in multi-armed bandits is a fundamental online learning problem. In this work, we propose a variant of Thompson sampling that learns to explore over time by interacting with problem instances sampled from an unknown prior distribution. This algorithm meta-learns the prior and therefore we call it Meta-TS. We propose efficient implementations of Meta-TS and analyze it in Gaussian bandits. Our analysis captures the improvement due to learning the prior and is of a broader interest, because we derive the first prior-dependent upper bound on the Bayes regret. Our regret bound is complemented by empirical evaluation, which shows that Meta-TS quickly adapts to the unknown prior.View details
The development of recommender systems that optimize multi-turn interaction with users, and model the interactions of different
agents (e.g., users, content providers, vendors) in the recommender ecosystem have drawn increasing attention in recent years.
Developing and training models and algorithms for such recommenders can be especially difficult using static datasets, which often
fail to offer the types of counterfactual predictions needed to evaluate policies over extended horizons. To address this, we develop
RecSim NG, a probabilistic platform for the simulation of multi-agent recommender systems. RecSim NG is a scalable, modular,
differentiable simulator implemented in Edward2 and TensorFlow. It offers: a powerful, general probabilistic programming language for
agent-behavior specification; tools for probabilistic inference and latent-variable model learning, backed by automatic differentiation
and tracing; a TensorFlow-based runtime for running simulations on accelerated hardware. We describe RecSim NG and illustrate
how it can be used to create transparent, configurable, end-to-end models of a recommender ecosystem, complemented by a small
set of simple use cases that demonstrate how RecSim NG can help both researchers and practitioners easily develop and train novel algorithms for recommender systems.
A short version of this paper was published at RecSys 2020.View details
Advances in Neural Information Processing Systems 33 (NeurIPS 2020), pp. 2122-2134
Exploration policies in Bayesian bandits maximize the average reward over problem instances drawn from some distribution P. In this work, we learn such policies for an unknown distribution P using samples from P. Our approach is a form of meta-learning and exploits properties of P without making strong assumptions about its form. To do this, we parameterize our policies in a differentiable way and optimize them by policy gradients, an approach that is pleasantly general and easy to implement. We derive effective gradient estimators and propose novel variance reduction techniques. We also analyze and experiment with various bandit policy classes, including neural networks and a novel softmax policy. The latter has regret guarantees and is a natural starting point for our optimization. Our experiments show the versatility of our approach. We also observe that neural network policies can learn implicit biases expressed only through the sampled instances.View details
RecSys '20: Fourteenth ACM Conference on Recommender Systems (2020), pp. 591-593
We develop RecSim NG, a probabilistic platform that supports natural, concise specification and learning of models for multi-agent recommender systems simulation. RecSim NG is a scalable, modular,
differentiable simulator implemented in Edward2 and TensorFlow.
An extended version of this paper is available as arXiv:2103.08057.View details
We propose RecSim, a configurable platform for authoring simulation environments for recommender systems (RSs) that naturally supports sequential interaction with users. RecSim allows the creation of new environments that reflect particular aspects of user behavior and item structure at a level of abstraction well-suited to pushing the limits of current reinforcement learning (RL) and RS techniques in sequential interactive recommendation problems. Environments can be easily configured that vary assumptions about: user preferences and item familiarity; user latent state and its dynamics; and choice models and other user response behavior. We outline how RecSim offers value to RL and RS researchers and practitioners, and how it can serve as a vehicle for academic-industrial collaboration.View details
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