Publications

Discovering Personalized Semantics for Soft Attributes in Recommender Systems using Concept Activation Vectors

Christina Göpfert, Alex Haig, Chih-wei Hsu, Yinlam Chow, Ivan Vendrov, Tyler Lu, Deepak Ramachandran, Hubert Pham, Mohammed Ghavamzadeh, Craig Boutilier

Supersedes “Discovering Personalized Semantics for Soft Attributes in Recommender Systems Using Concept Activation Vectors” in ACM Web Conference, 2022. Authors: Christina Göpfert, Chih-wei Hsu, Yinlam Chow, Ivan Vendrov, Tyler Lu, Deepak Ramachandran, Craig Boutilier

  • Interactive recommender systems have emerged as a promising paradigm to overcome the limitations of the primitive user feedback used by traditional recommender systems (e.g., clicks, item consumption, ratings). They allow users to express intent, preferences, constraints, and contexts in a richer fashion, often using natural language (including faceted search and dialogue). Yet more research is needed to find the most effective ways to use this feedback. One challenge is inferring a user’s semantic intent from the open-ended terms or attributes often used to describe a desired item. This is critical for recommender systems that wish to support users in their everyday, intuitive use of natural language to refine recommendation results. Leveraging concept activation vectors (CAVs) [26], a recently developed approach for model interpretability in machine learning, we develop a framework to learn a representation that captures the semantics of such attributes and connects them to user preferences and behaviors in recommender systems. One novel feature of our approach is its ability to distinguish objective and subjective attributes (both subjectivity of degree and of sense) and associate different senses of subjective attributes with different users. We demonstrate on both synthetic and real-world datasets that our CAV representation not only accurately interprets users’ subjective semantics but also can be used to improve recommendations through interactive item critiquing.Here

ConQUR: Mitigating Delusional Bias in Deep Q-Learning

Dijia Su, Jayden Ooi, Tyler Lu, Dale Schuurmans, Craig Boutilier

  • Delusional bias is a fundamental source of error in approximate Q-learning. To date, the only techniques that explicitly address delusion require comprehensive search using tabular value estimates. In this paper, we develop efficient methods to mitigate delusional bias by training Q- approximators with labels that are “consistent” with the underlying greedy policy class. We introduce a simple penalization scheme that encourages Q-labels used across training batches to remain (jointly) consistent with the expressible policy class. We also propose a search framework that allows multiple Q-approximators to be generated and tracked, thus mitigating the effect of premature (implicit) policy commitments. Experimental results demonstrate that these methods can improve the performance of Q-learning in a variety of Atari games, sometimes dramatically.

Gradient-Based Optimization for Bayesian Preference Elicitation

Ivan Vendrov, Tyler Lu, Qingqing Huang, Craig Boutilier

  • Effective techniques for eliciting user preferences have taken on added importance as recommender systems (RSs) become increasingly interactive and conversational. A common and conceptually appealing Bayesian criterion for selecting queries is expected value of information (EVOI). Unfortunately, it is computationally prohibitive to construct queries with maximum EVOI in RSs with large item spaces. We tackle this issue by introducing a continuous formulation of EVOI as a differentiable network that can be optimized using gradient methods available in modern machine learning (ML) computational frameworks (e.g., TensorFlow, PyTorch). We exploit this to develop a novel, scalable Monte Carlo method for EVOI optimization, which is more scalable for large item spaces than methods requiring explicit enumeration of items. While we emphasize the use of this approach for pairwise (or k-wise) comparisons of items, we also demonstrate how our method can be adapted to queries involving subsets of item attributes or "partial items," which are often more cognitively manageable for users. Experiments show that our gradient-based EVOI technique achieves state-of-the-art performance across several domains while scaling to large item spaces.

Preference Elicitation and Robust Winner Determination for Single- and Multi- winner Social Choice

Tyler Lu, Craig Boutilier

  • The use of voting schemes based on rankings of alternatives to solve social choice problems can often impose significant burden on voters, both in terms of communication and cognitive requirements. In this paper, we develop techniques for preference elicitation in voting settings (i.e., vote elicitation) that can alleviate this burden by minimizing the amount of preference information needed to find (approximately or exactly) optimal outcomes. We first describe robust optimization techniques for determining winning alternatives given partial preference information (i.e., partial rankings) using the notion of minimax regret. We show that the corresponding computational problem is tractable for some important voting rules, and intractable for others. We then use the solution to the minimax-regret optimization as the basis for vote elicitation schemes that determine appropriate preference queries for voters to quickly reduce potential regret. We apply these techniques to multi-winner social choice problems as well, in which a slate of alternatives must be selected, developing both exact and greedy robust optimization procedures. Empirical results on several data sets validate the effectiveness of our techniques.

Non-delusional Q-learning and value-iteration

Tyler Lu, Dale Schuurmans, Craig Boutilier

  • We identify a fundamental source of error in Q-learning and other forms of dynamic programming with function approximation. Delusional bias arises when the approximation architecture limits the class of expressible greedy policies. Since standard Q-updates make globally uncoordinated action choices with respect to the expressible policy class, inconsistent or even conflicting Q-value estimates can result, leading to pathological behaviour such as over/under-estimation, instability and even divergence. To solve this problem, we introduce a new notion of policy consistency and define a local backup process that ensures global consistency through the use of information sets---sets that record constraints on policies consistent with backed-up Q-values. We prove that both the model-based and model-free algorithms using this backup remove delusional bias, yielding the first known algorithms that guarantee optimal results under general conditions. These algorithms furthermore only require polynomially many information sets (from a potentially exponential support). Finally, we suggest other practical heuristics for value-iteration and Q-learning that attempt to reduce delusional bias.

Data Center Cooling using Model-predictive Control

Nevena Lazic, Craig Boutilier, Tyler Lu, Eehern Wong, Binz Roy, MK Ryu and Greg Imwalle

  • Despite impressive recent advances in reinforcement learning (RL), its deployment in real-world physical systems is often complicated by unexpected events, limited data, and the potential for expensive failures. In this paper, we describe an application of RL “in the wild” to the task of regulating temperatures and airflow inside a large-scale data center (DC). Adopting a data-driven, model-based approach, we demonstrate that an RL agent with little prior knowledge is able to effectively and safely regulate conditions on a server floor after just a few hours of exploration, while improving operational efficiency relative to existing PID controllers.

Budget Allocation using Weakly Coupled, Constrained Markov Decision Processes

Craig Boutilier, Tyler Lu

  • We consider the problem of budget (or other resource) allocation in sequential decision problems involving a large number of concurrently running sub-processes, whose only interaction is through their consumption of budget. Our first contribution is the introduction of budgeted MDPs (BMDPs), an MDP model in which policies/values are a function of available budget, (c.f. constrained MDPs which are solved given a fixed budget). BMDPs allow one to explicitly trade off allocated budget and expected value. We show that optimal value functions are concave, non-decreasing in budget, and piecewise-linear in the finite horizon case, and can be computed by dynamic programming (and support ready approximation). Our second contribution is a method that exploits BMDP solutions to allocate budget to a large number of independent BMDPs, coupled only by their common budget pool. The problem can be cast as a multiple-choice knapsack problem, which admits an efficient, optimal greedy algorithm. Empirical results in an online advertising domain confirm the efficacy of our methods.

Value-directed Compression of Large-scale Assignment Problems

Tyler Lu, Craig Boutilier

  • Data-driven analytics — in areas ranging from consumer marketing to public policy — often allow behavior prediction at the level of individuals rather than population segments, offering the opportunity to improve decisions that impact large populations. Modeling such (generalized) assignment problems as linear programs, we propose a general value-directed compression technique for solving such problems at scale. We dynamically segment the population into cells using a form of column generation, constructing groups of individuals who can provably be treated identically in the optimal solution. This compression allows problems, unsolvable using standard LP techniques, to be solved effectively. Indeed, once a compressed LP is constructed, problems can solved in milliseconds. We provide a theoretical analysis of themethods, outline the distributed implementation of the requisite data processing, and show how a single compressed LP can be used to solve multiple variants of the original LP near-optimally in real-time (e.g., tosupport scenario analysis). We also show how the method can be leveraged in integer programming models.  Experimental results on marketing contact optimization and political legislature problems validate the performance of our technique.

Optimal Social Choice Functions: A Utilitarian View

Craig Boutilier, Ioannis Caragiannis, Simi Haber, Tyler Lu, Ariel Procaccia, Or Sheffet

Prominent Paper Award, 2022

Supersedes the conference version (appendix) with same title and authors that appeared in ACM Electronic Commerce, 2012.

  • The use of voting schemes based on rankings of alternatives to solve social choice problems can often impose significant burden on voters, both in terms of communication and cognitive requirements. In this paper, we develop techniques for preference elicitation in voting settings (i.e., vote elicitation) that can alleviate this burden by minimizing the amount of preference information needed to find (approximately or exactly) optimal outcomes. We first describe robust optimization techniques for determining winning alternatives given partial preference information (i.e., partial rankings) using the notion of minimax regret. We show that the corresponding computational problem is tractable for some important voting rules, and intractable for others. We then use the solution to the minimax-regret optimization as the basis for vote elicitation schemes that determine appropriate preference queries for voters to quickly reduce potential regret. We apply these techniques to multi-winner social choice problems as well, in which a slate of alternatives must be selected, developing both exact and greedy robust optimization procedures. Empirical results on several data sets validate the effectiveness of our techniques.

Effective Sampling and Learning for Mallows Models with Pairwise-Preference Data

Tyler Lu, Craig Boutilier

Supersedes “Learning Mallows Models with Pairwise Preference” that appeared in ICML 2011 under the same authors.

  • The use of voting schemes based on rankings of alternatives to solve social choice problems can often impose significant burden on voters, both in terms of communication and cognitive requirements. In this paper, we develop techniques for preference elicitation in voting settings (i.e., vote elicitation) that can alleviate this burden by minimizing the amount of preference information needed to find (approximately or exactly) optimal outcomes. We first describe robust optimization techniques for determining winning alternatives given partial preference information (i.e., partial rankings) using the notion of minimax regret. We show that the corresponding computational problem is tractable for some important voting rules, and intractable for others. We then use the solution to the minimax-regret optimization as the basis for vote elicitation schemes that determine appropriate preference queries for voters to quickly reduce potential regret. We apply these techniques to multi-winner social choice problems as well, in which a slate of alternatives must be selected, developing both exact and greedy robust optimization procedures. Empirical results on several data sets validate the effectiveness of our techniques.

On the Value of Using Group Discounts under Price Competition

Reshef Meir, Tyler Lu, Moshe Tennenholtz, Craig Boutilier

Supersedes the conference version with the same title and authors that appeared in AAAI 2013.

  • The increasing use of group discounts has provided opportunities for buying groups with diverse preferences to coordinate their behavior in order to exploit the best offers from multiple vendors. We analyze this problem from the viewpoint of the vendors, asking under what conditions a vendor should adopt a volume-based price schedule rather than posting a fixed price, either as a monopolist or when competing with other vendors. When vendors have uncertainty about buyers’ valuations specified by a known distribution, we show that a vendor is always better off posting a fixed price, provided that buyers’ types are i.i.d. and that other vendors also use fixed prices. We also show that these assumptions cannot be relaxed: if buyers are not i.i.d., or other vendors post discount schedules, then posting a schedule may yield higher profit for the vendor. We provide similar results under a distribution-free uncertainty model, where vendors minimize their maximum regret over all type realizations.

Multi-winner Social Choice with Incomplete Preferences

Tyler Lu, Craig Boutilier

  • Multi-winner social choice considers the problem of selecting a slate of K options to realize some social objective. It has found application in the construction of political legislatures and committees, product recommendation, and related problems, and has recently attracted attention from a computational perspective. We address the multi-winner problem when facing incomplete voter preferences, using the notion of minimax regret to determine a robust slate of options in the presence of preference uncertainty. We analyze the complexity of this problem and develop new exact and greedy robust optimization algorithms for its solution. Using these techniques, we also develop preference elicitation heuristics which, in practice, allow us to find near-optimal slates with considerable savings in the preference information required vis-à-vis complete votes.

Bayesian Vote Manipulation: Optimal Strategies and Impact on Welfare

Tyler Lu, Pingzhong Tang, Ariel Procaccia, Craig Boutilier

  • Most analyses of manipulation of voting schemes have adopted two assumptions that greatly diminish their practical import. First, it is usually assumed that the manipulators have full knowledge of the votes of the non-manipulating agents. Second, analysis tends to focus on the probability of manipulation rather than its impact on the social choice objective (e.g., social welfare). We relax both of these assumptions by analyzing optimal Bayesian manipulation strategies when the manipulators have only partial probabilistic information about nonmanipulator votes, and assessing the expected loss in social welfare (in the broad sense of the term). We present a general optimization framework for the derivation of optimal manipulation strategies given arbitrary voting rules and distributions over preferences. We theoretically and empirically analyze the optimal manipulability of some popular voting rules using distributions and real data sets that go well beyond the common, but unrealistic, impartial culture assumption. We also shed light on the stark difference between the loss in social welfare and the probability of manipulation by showing that even when manipulation is likely, impact to social welfare is slight (and often negligible).

Matching Models for Preference-sensitive Group Purchasing

Tyler Lu, Craig Boutilier

  • Matching buyers and sellers is one of the most fundamental problems in economics and market design. An interesting variant of the matching problem arises when self-interested buyers come together in order to induce sellers to offer quantity or volume discounts, as is common in buying consortia, and more recently in the consumer group couponing space (e.g., Groupon). We consider a general model of this problem in which a group or buying consortium is faced with volume discount offers from multiple vendors, but group members have distinct preferences for different vendor offerings. Unlike some recent formulations of matching games that involve quantity discounts, the combination of varying preferences and discounts can render the core of the matching game empty, in both the transferable and nontransferable utility sense. Thus, instead of coalitional stability, we propose several forms of Nash stability under various knowledge and transfer/payment assumptions. We investigate the computation of buyer-welfare maximizing matchings and show the existence of transfers (subsidized prices) of a particularly desirable form that support stable matchings. We also study a nontransferable utility model, showing that stable matchings exist; we develop a further variant of the problem in which buyers provide a simple preference ordering over "deals" rather than specific valuations---a model that is especially attractive in the consumer space---and also show the existence of stable matchings. Finally, computational experiments with buyer-welfare maximization demonstrate the value of our approach.

Vote Elicitation with Probabilistic Preference Models: Empirical Estimation and Cost Tradeoffs

Tyler Lu, Craig Boutilier

  • A variety of preference aggregation schemes and voting rules have been developed in social choice to support group decision making. However, the requirement that participants provide full preference information in the form of a complete ranking of alternatives is a severe impediment to their practical deployment. Only recently have incremental elicitation schemes been proposed that allow winners to be determined with partial preferences; however, while minimizing the amount of information provided, these tend to require repeated rounds of interaction from participants. We propose a probabilistic analysis of vote elicitation that combines the advantages of incremental elicitation schemes—namely, minimizing the amount of information revealed—with those of full information schemes—single (or few) rounds of elicitation. We exploit distributional models of preferences to derive the idealranking threshold k, or number of top candidates each voter should provide, to ensure that either a winning or a high quality candidate (as measured by max regret) can be found with high probability. Our main contribution is a general empirical methodology, which uses preference profile samples to determine the ideal ranking threshold for many common voting rules. We develop probably approximately correct (PAC) sample complexity results for one-round protocols with any voting rule and demonstrate the efficacy of our approach empirically on one-round protocols with Borda scoring.

Learning Mallows Models with Pairwise Preferences

Tyler Lu, Craig Boutilier

  • Learning preference distributions is a key problem in many areas (e.g., recommender systems, IR, social choice). However, many existing methods require restrictive data models for evidence about user preferences. We relax these restrictions by considering as data arbitrary pairwise comparisons—the fundamental building blocks of ordinal rankings. We develop the first algorithms for learning Mallows models (and mixtures) with pairwise comparisons. At the heart is a new algorithm, the generalized repeated insertion model (GRIM), for sampling from arbitrary ranking distributions. We develop approximate samplers that are exact for many important special cases—and have provable bounds with pairwise evidence—and derive algorithms for evaluating log-likelihood, learning Mallows mixtures, and non-parametric estimation. Experiments on large, real-world datasets show the effectiveness of our approach.

Robust Approximation and Incremental Elicitation in Voting Protocols

Tyler Lu, Craig Boutilier

  • While voting schemes provide an effective means for aggregating preferences, methods for the effective elicitation of voter preferences have received little attention. We address this problem by first considering approximate winner determination when incomplete voter preferences are provided. Exploiting natural scoring metrics, we use max regret to measure the quality or robustness of proposed winners, and develop polynomial time algorithms for computing the alternative with minimax regret for several popular voting rules. We then show how minimax regret can be used to effectively drive incremental preference/vote elicitation and devise several heuristics for this process. Despite worst-case theoretical results showing that most voting protocols require nearly complete voter preferences to determine winners, we demonstrate the practical effectiveness of regret-based elicitation for determining both approximate and exact winners on several real-world data sets.

Budgeted Social Choice: From Consensus to Personalized Decision Making

Tyler Lu, Craig Boutilier

  • We develop a general framework for social choice problems in which a limited number of alternatives can be recommended to an agent population. In our budgeted social choice model, this limit is determined by a budget, capturing problems that arise naturally in a variety of contexts, and spanning the continuum from pure consensus decision making (i.e., standard social choice) to fully personalized recommendation. Our approach applies a form of segmentation to social choice problems— requiring the selection of diverse options tailored to different agent types—and generalizes certain multi-winner election schemes. We show that standard rank aggregation methods perform poorly, and that optimization in our model is NP-complete; but we develop fast greedy algorithms with some theoretical guarantees. Experiments on real-world datasets demonstrate the effectiveness of our algorithms.

The Unavailable Candidate Model: A Decision-Theoretic View of Social Choice

Tyler Lu, Craig Boutilier

  • We develop a general framework for social choice problems in which a limited number of alternatives can be recommended to an agent population. In our budgeted social choice model, this limit is determined by a budget, capturing problems that arise naturally in a variety of contexts, and spanning the continuum from pure consensus decision making (i.e., standard social choice) to fully personalized recommendation. Our approach applies a form of segmentation to social choice problems— requiring the selection of diverse options tailored to different agent types—and generalizes certain multi-winner election schemes. We show that standard rank aggregation methods perform poorly, and that optimization in our model is NP-complete; but we develop fast greedy algorithms with some theoretical guarantees. Experiments on real-world datasets demonstrate the effectiveness of our algorithms.

Impossibility Theorems for Domain Adaptation

Shai Ben-David, Tyler Lu, Teresa Luu and Dávid Pál

  • The domain adaptation problem in machine learning occurs when the test data generating distribution differs from the one that generates the training data. It is clear that the success of learning under such circumstances depends on similarities between the two data distributions. We study assumptions about the relationship between the two distributions that one needed for domain adaptation learning to succeed. We analyze the assumptions in an agnostic PAC-style learning model for a the setting in which the learner can access a labeled training data sample and an unlabeled sample generated by the test data distribution. We focus on three assumptions: (i) Similarity between the unlabeled distributions, (ii) Existence of a classifier in the hypothesis class with low error on both training and testing distributions, and (iii) The covariate shift assumption. I.e., the assumption that the conditioned label distribution (for each data point) is the same for both the training and test distributions. We show that without either assumption (i) or (ii), the combination of the remaining assumptions is not sufficient to guarantee successful learning. Our negative results hold with respect to any domain adaptation learning algorithm, as long as it does not have access to target labeled examples. In particular, we provide formal proofs that the popular covariate shift assumption is rather weak and does not relieve the necessity of the other assumptions. We also discuss the intuitively appealing paradigm of reweighing the labeled training sample according to the target unlabeled distribution. We show that, somewhat counter intuitively, that paradigm cannot be trusted in the following sense. There are DA tasks that are indistinguishable, as far as the input training data goes, but in which reweighing leads to significant improvement in one task, while causing dramatic deterioration of the learning success in the other.

Showing Relevant Ads via Lipschitz Context Multi-Armed Bandits

Tyler Lu, Dávid Pál, Martin Pál

  • The domain adaptation problem in machine learning occurs when the test data generating distribution differs from the one that generates the training data. It is clear that the success of learning under such circumstances depends on similarities between the two data distributions. We study assumptions about the relationship between the two distributions that one needed for domain adaptation learning to succeed. We analyze the assumptions in an agnostic PAC-style learning model for a the setting in which the learner can access a labeled training data sample and an unlabeled sample generated by the test data distribution. We focus on three assumptions: (i) Similarity between the unlabeled distributions, (ii) Existence of a classifier in the hypothesis class with low error on both training and testing distributions, and (iii) The covariate shift assumption. I.e., the assumption that the conditioned label distribution (for each data point) is the same for both the training and test distributions. We show that without either assumption (i) or (ii), the combination of the remaining assumptions is not sufficient to guarantee successful learning. Our negative results hold with respect to any domain adaptation learning algorithm, as long as it does not have access to target labeled examples. In particular, we provide formal proofs that the popular covariate shift assumption is rather weak and does not relieve the necessity of the other assumptions. We also discuss the intuitively appealing paradigm of reweighing the labeled training sample according to the target unlabeled distribution. We show that, somewhat counter intuitively, that paradigm cannot be trusted in the following sense. There are DA tasks that are indistinguishable, as far as the input training data goes, but in which reweighing leads to significant improvement in one task, while causing dramatic deterioration of the learning success in the other.

Learning Low-Density Separators

Shai Ben-David, Tyler Lu, Dávid Pál and Miroslava Sotakova

  • The domain adaptation problem in machine learning occurs when the test data generating distribution differs from the one that generates the training data. It is clear that the success of learning under such circumstances depends on similarities between the two data distributions. We study assumptions about the relationship between the two distributions that one needed for domain adaptation learning to succeed. We analyze the assumptions in an agnostic PAC-style learning model for a the setting in which the learner can access a labeled training data sample and an unlabeled sample generated by the test data distribution. We focus on three assumptions: (i) Similarity between the unlabeled distributions, (ii) Existence of a classifier in the hypothesis class with low error on both training and testing distributions, and (iii) The covariate shift assumption. I.e., the assumption that the conditioned label distribution (for each data point) is the same for both the training and test distributions. We show that without either assumption (i) or (ii), the combination of the remaining assumptions is not sufficient to guarantee successful learning. Our negative results hold with respect to any domain adaptation learning algorithm, as long as it does not have access to target labeled examples. In particular, we provide formal proofs that the popular covariate shift assumption is rather weak and does not relieve the necessity of the other assumptions. We also discuss the intuitively appealing paradigm of reweighing the labeled training sample according to the target unlabeled distribution. We show that, somewhat counter intuitively, that paradigm cannot be trusted in the following sense. There are DA tasks that are indistinguishable, as far as the input training data goes, but in which reweighing leads to significant improvement in one task, while causing dramatic deterioration of the learning success in the other.

Does Unlabeled Data Provably Help? Worst-case Analysis of the Sample Complexity of Semi-Supervised Learning

Shai Ben-David, Tyler Lu, Dávid Pál

  • The domain adaptation problem in machine learning occurs when the test data generating distribution differs from the one that generates the training data. It is clear that the success of learning under such circumstances depends on similarities between the two data distributions. We study assumptions about the relationship between the two distributions that one needed for domain adaptation learning to succeed. We analyze the assumptions in an agnostic PAC-style learning model for a the setting in which the learner can access a labeled training data sample and an unlabeled sample generated by the test data distribution. We focus on three assumptions: (i) Similarity between the unlabeled distributions, (ii) Existence of a classifier in the hypothesis class with low error on both training and testing distributions, and (iii) The covariate shift assumption. I.e., the assumption that the conditioned label distribution (for each data point) is the same for both the training and test distributions. We show that without either assumption (i) or (ii), the combination of the remaining assumptions is not sufficient to guarantee successful learning. Our negative results hold with respect to any domain adaptation learning algorithm, as long as it does not have access to target labeled examples. In particular, we provide formal proofs that the popular covariate shift assumption is rather weak and does not relieve the necessity of the other assumptions. We also discuss the intuitively appealing paradigm of reweighing the labeled training sample according to the target unlabeled distribution. We show that, somewhat counter intuitively, that paradigm cannot be trusted in the following sense. There are DA tasks that are indistinguishable, as far as the input training data goes, but in which reweighing leads to significant improvement in one task, while causing dramatic deterioration of the learning success in the other.

Faster Set Intersection Algorithms for Text Searching

Jeremy Barbay, Alejandro Lopez-Ortiz, Tyler Lu, Alejandro Salinger

  • The domain adaptation problem in machine learning occurs when the test data generating distribution differs from the one that generates the training data. It is clear that the success of learning under such circumstances depends on similarities between the two data distributions. We study assumptions about the relationship between the two distributions that one needed for domain adaptation learning to succeed. We analyze the assumptions in an agnostic PAC-style learning model for a the setting in which the learner can access a labeled training data sample and an unlabeled sample generated by the test data distribution. We focus on three assumptions: (i) Similarity between the unlabeled distributions, (ii) Existence of a classifier in the hypothesis class with low error on both training and testing distributions, and (iii) The covariate shift assumption. I.e., the assumption that the conditioned label distribution (for each data point) is the same for both the training and test distributions. We show that without either assumption (i) or (ii), the combination of the remaining assumptions is not sufficient to guarantee successful learning. Our negative results hold with respect to any domain adaptation learning algorithm, as long as it does not have access to target labeled examples. In particular, we provide formal proofs that the popular covariate shift assumption is rather weak and does not relieve the necessity of the other assumptions. We also discuss the intuitively appealing paradigm of reweighing the labeled training sample according to the target unlabeled distribution. We show that, somewhat counter intuitively, that paradigm cannot be trusted in the following sense. There are DA tasks that are indistinguishable, as far as the input training data goes, but in which reweighing leads to significant improvement in one task, while causing dramatic deterioration of the learning success in the other.

Probabilistic and Utility-theoretic Models in Social Choice: Challenges for Learning, Optimization, Elicitation, and Manipulation

Craig Boutilier, Tyler Lu

  • The abundance of inexpensive preference data facilitated by online commerce, search, recommender systems, and social networks has the potential to stretch the boundaries of social choice. Specifically, concepts and models usually applied to high stakes domains such as political elections, public or corporate policy decisions, and the like, will increasingly find themselves used in the lower stakes, high-frequency domains addressed by online systems.

Algorithmic Facilitation for Deliberative Online Forums

Manon Revel, Smitha Milli, Tyler Lu, Jamelle Watson-Daniels, Max Nickel

Working Paper, November 2024

  • Coming soon