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Ashish Goel

Publications and source records attributed to Ashish Goel.

At least 19 recordsLinked to original sources

Nash Core in Multiwinner Election

In the approval-based committee selection problem, a committee is said to be in the core if no subset of voters has an incentive to deviate by selecting a \emph{blocking} committee of proportional size, such that every voter in the deviating group strictly prefers the blocking committee. We consider the setting where candidates can be selected fractionally. Under a mild regularity assumption, we show that there always exists a weighting of candidates such that the fractional committee maximizing the candidate-weighted Nash Social Welfare is in the core. We refer to such a solution as being in the \emph{Nash core}. Additionally, we show that a Nash core solution admits a payment assignment between voters and candidates, where each voter pays a candidate they approve in proportion to the weight. For the discrete setting, where each candidate is either included or excluded from the committee, we prove that every approval-based committee election with at most eight equally weighted voters has a core committee by rounding the fractional Nash core solution. Although the non-emptiness of the core in this setting remains an open question and checking core membership is coNP-hard, we extend the notion of the Nash core to the discrete case, yielding a formulation that is efficiently verifiable and offers a promising path toward establishing core existence in discrete settings. Finally, we test our approach on real voting data using a payment-guided heuristic. We empirically show that the Nash core solution can be efficiently computed through an iterative algorithm in both the fractional and discrete settings.

cs.GT

BEVIO: Efficient Bird's-Eye-View based Sparse-Update Visual-Inertial Odometry for Lunar Day-Night Navigation

Visual-Inertial Odometry (VIO) provides smooth, high-rate state estimates and has been widely used for robotic navigation in both terrestrial and planetary applications. However, its performance is typically dependent on the frequency of visual updates, which is a challenge for planetary rovers operating under extreme resource constraints and low frame rates. This work investigates enabling reliable VIO with very sparse visual updates for lunar rover applications, addressing both day and night-time operations where feature associations become especially difficult under self-illumination conditions. We propose a Bird's Eye View (BEV)-based image matching scheme that remains robust to larger inter-frame motions and more reliable feature matching despite significant visual appearance changes. We extensively evaluate our proposed approach, BEVIO, through high-fidelity photorealistic lunar and real-time robotic experiments conducted using a half-scale lunar rover, in a long-term day-night deployment at Plaster City, CA, USA. The results demonstrate that our method enables reliable day and nighttime self-illuminated traverses at visual update rates as low as 0.25 Hz, underscoring its suitability for navigation on power- and compute-limited lunar rovers.

cs.RO

Quality-Sensitive Matrix Factorization for Community Notes: Towards Sample Efficiency and Manipulation Resistance

Community Notes is X's crowdsourced fact-checking program: contributors write short notes that add context to potentially misleading posts, and other contributors rate whether those notes are helpful. Its algorithm uses a matrix factorization model to separate ideology from note quality, so notes are surfaced only when they receive support across ideological lines. After ideology is accounted for, however, the model gives all raters equal influence on quality estimates. This slows consensus formation and leaves the quality estimate vulnerable to noisy or strategic raters. We propose Quality-Sensitive Matrix Factorization (QSMF), which uses a per-rater quality-sensitivity parameter \(\hat\rho_i\) estimated jointly with all other parameters. This connects QSMF to peer prediction: without external ground truth, it gives more influence to raters whose ideology-adjusted ratings are more consistent with the note-quality estimates learned from all the ratings. We evaluate QSMF on 45M ratings over 365K notes from the six months before the 2024 U.S. presidential election. Split-half tests confirm that quality sensitivity is a stable, empirically recoverable rater trait. In evaluation on high-traffic notes, QSMF requires 26--40\% fewer ratings to match the baseline's accuracy. In semi-synthetic coordinated attacks on notes of opposing ideology, QSMF substantially reduces displacement on the estimated quality estimates of targeted notes relative to the baseline. In synthetic data with known ground truth, \(\hat\rho_i\) separates good from bad raters with an AUC above 0.94, and achieves much lower error in recovering the true note quality estimates in the presence of bad raters. These gains come from a single additional scalar parameter per rater, with no external ground truth and no manual moderation.

cs.SI

Beyond Polarization: Opinion Mixing and Social Influence in Deliberation

Deliberative processes are often discussed as increasing or decreasing polarization. This approach misses a different, and arguably more diagnostic, dimension of opinion change: whether deliberation reshuffles who agrees with whom, or simply moves everyone in parallel while preserving the pre-deliberation rank ordering. We introduce \opinion mixing, measured by Kendall's rank correlation (\tau) between pre- and post-deliberation responses, as a complement to variance-based polarization metrics. Across two large online deliberative polls spanning 32 countries (MCF-2022: n=6,342; MCF-2023: n=1,529), deliberation increases opinion mixing relative to survey-only controls: treatment groups exhibit lower rank correlation on (97%) and (93%) of opinion questions, respectively. Polarization measures based on variance tell a more heterogeneous story: controls consistently converge, while treated groups sometimes converge and sometimes diverge depending on the issue. To probe mechanisms, we link transcripts and surveys in a third event (SOF: (n=617), 116 groups) and use LLM-assisted coding of 6,232 discussion statements. Expressed support in discussion statements strongly predicts subsequent group-level opinion shifts; this correlation is amplified by justification quality in the statements but not by argument novelty. To our knowledge, we are the first to observe how different notions of argument quality have different associations with the outcome of deliberation. This suggests that opinion change after deliberation is related to selective uptake of well-reasoned arguments, producing complex patterns of opinion reorganization that standard polarization metrics may miss.

cs.SI

Question the Questions: Auditing Representation in Online Deliberative Processes

A central feature of many deliberative processes, such as citizens' assemblies and deliberative polls, is the opportunity for participants to engage directly with experts. While participants are typically invited to propose questions for expert panels, only a limited number can be selected due to time constraints. This raises the challenge of how to choose a small set of questions that best represent the interests of all participants. We introduce an auditing framework for measuring the level of representation provided by a slate of questions, based on the social choice concept known as justified representation (JR). We present the first algorithms for auditing JR in the general utility setting, with our most efficient algorithm achieving a runtime of $O(mn\log n)$, where $n$ is the number of participants and $m$ is the number of proposed questions. We apply our auditing methods to historical deliberations, comparing the representativeness of (a) the actual questions posed to the expert panel (chosen by a moderator), (b) participants' questions chosen via integer linear programming, (c) summary questions generated by large language models (LLMs). Our results highlight both the promise and current limitations of LLMs in supporting deliberative processes. By integrating our methods into an online deliberation platform that has been used for over hundreds of deliberations across more than 50 countries, we make it easy for practitioners to audit and improve representation in future deliberations.

cs.AI

AI Space Cortex: An Experimental System for Future Era Space Exploration

Our Robust, Explainable Autonomy for Scientific Icy Moon Operations (REASIMO) effort contributes to NASA's Concepts for Ocean worlds Life Detection Technology (COLDTech) program, which explores science platform technologies for ocean worlds such as Europa and Enceladus. Ocean world missions pose significant operational challenges. These include long communication lags, limited power, and lifetime limitations caused by radiation damage and hostile conditions. Given these operational limitations, onboard autonomy will be vital for future Ocean world missions. Besides the management of nominal lander operations, onboard autonomy must react appropriately in the event of anomalies. Traditional spacecraft rely on a transition into 'safe-mode' in which non-essential components and subsystems are powered off to preserve safety and maintain communication with Earth. For a severely time-limited Ocean world mission, resolutions to these anomalies that can be executed without Earth-in-the-loop communication and associated delays are paramount for completion of the mission objectives and science goals. To address these challenges, the REASIMO effort aims to demonstrate a robust level of AI-assisted autonomy for such missions, including the ability to detect and recover from anomalies, and to perform missions based on pre-trained behaviors rather than hard-coded, predetermined logic like all prior space missions. We developed an AI-assisted, personality-driven, intelligent framework for control of an Ocean world mission by combining a mix of advanced technologies. To demonstrate the capabilities of the framework, we perform tests of autonomous sampling operations on a lander-manipulator testbed at the NASA Jet Propulsion Laboratory, approximating possible surface conditions such a mission might encounter.

cs.RO

Multi-Selection for Recommendation Systems

We present the construction of a multi-selection model to answer differentially private queries in the context of recommendation systems. The server sends back multiple recommendations and a ``local model'' to the user, which the user can run locally on its device to select the item that best fits its private features. We study a setup where the server uses a deep neural network (trained on the Movielens 25M dataset as the ground truth for movie recommendation. In the multi-selection paradigm, the average recommendation utility is approximately 97\% of the optimal utility (as determined by the ground truth neural network) while maintaining a local differential privacy guarantee with $\epsilon$ ranging around 1 with respect to feature vectors of neighboring users. This is in comparison to an average recommendation utility of 91\% in the non-multi-selection regime under the same constraints.

cs.LG

Metric Distortion of Small-group Deliberation

We consider models for social choice where voters rank a set of choices (or alternatives) by deliberating in small groups of size at most $k$, and these outcomes are aggregated by a social choice rule to find the winning alternative. We ground these models in the metric distortion framework, where the voters and alternatives are embedded in a latent metric space, with closer alternative being more desirable for a voter. We posit that the outcome of a small-group interaction optimally uses the voters' collective knowledge of the metric, either deterministically or probabilistically. We characterize the distortion of our deliberation models for small $k$, showing that groups of size $k=3$ suffice to drive the distortion bound below the deterministic metric distortion lower bound of $3$, and groups of size $4$ suffice to break the randomized lower bound of $2.11$. We also show nearly tight asymptotic distortion bounds in the group size, showing that for any constant $\epsilon > 0$, achieving a distortion of $1+\epsilon$ needs group size that only depends on $1/\epsilon$, and not the number of alternatives. We obtain these results via formulating a basic optimization problem in small deviations of the sum of $i.i.d.$ random variables, which we solve to global optimality via non-convex optimization. The resulting bounds may be of independent interest in probability theory.

cs.GT

Flight Demonstration and Model Validation of a Prototype Variable-Altitude Venus Aerobot

This paper details a significant milestone towards maturing a buoyant aerial robotic platform, or aerobot, for flight in the Venus clouds. We describe two flights of our subscale altitude-controlled aerobot, fabricated from the materials necessary to survive Venus conditions. During these flights over the Nevada Black Rock desert, the prototype flew at the identical atmospheric densities as 54 to 55 km cloud layer altitudes on Venus. We further describe a first-principle aerobot dynamics model which we validate against the Nevada flight data and subsequently employ to predict the performance of future aerobots on Venus. The aerobot discussed in this paper is under JPL and Aerostar development for an in-situ mission flying multiple circumnavigations of Venus, sampling the chemical and physical properties of the planet's atmosphere and also remotely sensing surface properties.

cs.RO

Estimating Contribution Quality in Online Deliberations Using a Large Language Model

Deliberation involves participants exchanging knowledge, arguments, and perspectives and has been shown to be effective at addressing polarization. The Stanford Online Deliberation Platform facilitates large-scale deliberations. It enables video-based online discussions on a structured agenda for small groups without requiring human moderators. This paper's data comes from various deliberation events, including one conducted in collaboration with Meta in 32 countries, and another with 38 post-secondary institutions in the US. Estimating the quality of contributions in a conversation is crucial for assessing feature and intervention impacts. Traditionally, this is done by human annotators, which is time-consuming and costly. We use a large language model (LLM) alongside eight human annotators to rate contributions based on justification, novelty, expansion of the conversation, and potential for further expansion, with scores ranging from 1 to 5. Annotators also provide brief justifications for their ratings. Using the average rating from other human annotators as the ground truth, we find the model outperforms individual human annotators. While pairs of human annotators outperform the model in rating justification and groups of three outperform it on all four metrics, the model remains competitive. We illustrate the usefulness of the automated quality rating by assessing the effect of nudges on the quality of deliberation. We first observe that individual nudges after prolonged inactivity are highly effective, increasing the likelihood of the individual requesting to speak in the next 30 seconds by 65%. Using our automated quality estimation, we show that the quality ratings for statements prompted by nudging are similar to those made without nudging, signifying that nudging leads to more ideas being generated in the conversation without losing overall quality.

cs.AI

Few-shot Scooping Under Domain Shift via Simulated Maximal Deployment Gaps

Autonomous lander missions on extraterrestrial bodies need to sample granular materials while coping with domain shifts, even when sampling strategies are extensively tuned on Earth. To tackle this challenge, this paper studies the few-shot scooping problem and proposes a vision-based adaptive scooping strategy that uses the deep kernel Gaussian process method trained with a novel meta-training strategy to learn online from very limited experience on out-of-distribution target terrains. Our Deep Kernel Calibration with Maximal Deployment Gaps (kCMD) strategy explicitly trains a deep kernel model to adapt to large domain shifts by creating simulated maximal deployment gaps from an offline training dataset and training models to overcome these deployment gaps during training. Employed in a Bayesian Optimization sequential decision-making framework, the proposed method allows the robot to perform high-quality scooping actions on out-of-distribution terrains after a few attempts, significantly outperforming non-adaptive methods proposed in the excavation literature as well as other state-of-the-art meta-learning methods. The proposed method also demonstrates zero-shot transfer capability, successfully adapting to the NASA OWLAT platform, which serves as a state-of-the-art simulator for potential future planetary missions. These results demonstrate the potential of training deep models with simulated deployment gaps for more generalizable meta-learning in high-capacity models. Furthermore, they highlight the promise of our method in autonomous lander sampling missions by enabling landers to overcome the deployment gap between Earth and extraterrestrial bodies.

cs.RO

Differential Privacy with Multiple Selections

We consider the setting where a user with sensitive features wishes to obtain a recommendation from a server in a differentially private fashion. We propose a ``multi-selection'' architecture where the server can send back multiple recommendations and the user chooses one from these that matches best with their private features. When the user feature is one-dimensional -- on an infinite line -- and the accuracy measure is defined w.r.t some increasing function $\mathfrak{h}(.)$ of the distance on the line, we precisely characterize the optimal mechanism that satisfies differential privacy. The specification of the optimal mechanism includes both the distribution of the noise that the user adds to its private value, and the algorithm used by the server to determine the set of results to send back as a response and further show that Laplace is an optimal noise distribution. We further show that this optimal mechanism results in an error that is inversely proportional to the number of results returned when the function $\mathfrak{h}(.)$ is the identity function.

cs.DS

Geophysical Observations of the 24 September 2023 OSIRIS-REx Sample Return Capsule Re-Entry

Sample Return Capsules (SRCs) entering Earth's atmosphere at hypervelocity from interplanetary space are a valuable resource for studying meteor phenomena. The 24 September 2023 arrival of the OSIRIS-REx (Origins, Spectral Interpretation, Resource Identification, and Security-Regolith Explorer) SRC provided an unprecedented chance for geophysical observations of a well-characterized source with known parameters, including timing and trajectory. A collaborative effort involving researchers from 16 institutions executed a carefully planned geophysical observational campaign at strategically chosen locations, deploying over 400 ground-based sensors encompassing infrasound, seismic, distributed acoustic sensing (DAS), and GPS technologies. Additionally, balloons equipped with infrasound sensors were launched to capture signals at higher altitudes. This campaign (the largest of its kind so far) yielded a wealth of invaluable data anticipated to fuel scientific inquiry for years to come. The success of the observational campaign is evidenced by the near-universal detection of signals across instruments, both proximal and distal. This paper presents a comprehensive overview of the collective scientific effort, field deployment, and preliminary findings. The early findings have the potential to inform future space missions and terrestrial campaigns, contributing to our understanding of meteoroid interactions with planetary atmospheres. Furthermore, the dataset collected during this campaign will improve entry and propagation models as well as augment the study of atmospheric dynamics and shock phenomena generated by meteoroids and similar sources.

astro-ph.EP

Distributed Instruments for Planetary Surface Science: Scientific Opportunities and Technology Feasibility

In this paper, we assess the scientific promise and technology feasibility of distributed instruments for planetary science. A distributed instrument is an instrument designed to collect spatially and temporally correlated data from multiple networked, geographically distributed point sensors. Distributed instruments are ubiquitous in Earth science, where they are routinely employed for weather and climate science, seismic studies and resource prospecting, and detection of industrial emissions. However, to date, their adoption in planetary surface science has been minimal. It is natural to ask whether this lack of adoption is driven by low potential to address high-priority questions in planetary science; immature technology; or both. To address this question, we survey high-priority planetary science questions that are uniquely well-suited to distributed instruments. We identify four areas of research where distributed instruments hold promise to unlock answers that are largely inaccessible to monolithic sensors, namely, weather and climate studies of Mars; localization of seismic events on rocky and icy bodies; localization of trace gas emissions, primarily on Mars; and magnetometry studies of internal composition. Next, we survey enabling technologies for distributed sensors and assess their maturity. We identify sensor placement (including descent and landing on planetary surfaces), power, and instrument autonomy as three key areas requiring further investment to enable future distributed instruments. Overall, this work shows that distributed instruments hold great promise for planetary science, and paves the way for follow-on studies of future distributed instruments for Solar System in-situ science.

astro-ph.EP

Rank, Pack, or Approve: Voting Methods in Participatory Budgeting

Participatory budgeting is a popular method to engage residents in budgeting decisions by local governments. The Stanford Participatory Budgeting platform is an online platform that has been used to engage residents in more than 150 budgeting processes. We present a data set with anonymized budget opinions from these processes with K-approval, K-ranking or knapsack primary ballots. For a subset of the voters, it includes paired votes with a different elicitation method in the same process. This presents a unique data set, as the voters, projects and setting are all related to real-world decisions that the voters have an actual interest in. With data from primary ballots we find that while ballot complexity (number of projects to choose from, number of projects to select and ballot length) is correlated with a higher median time spent by voters, it is not correlated with a higher abandonment rate. We use vote pairs with different voting methods to analyze the effect of voting methods on the cost of selected projects, more comprehensively than was previously possible. In most elections, voters selected significantly more expensive projects using K-approval than using knapsack, although we also find a small number of examples with a significant effect in the opposite direction. This effect happens at the aggregate level as well as for individual voters, and is influenced both by the implicit constraints of the voting method and the explicit constraints of the voting interface. Finally, we validate the use of K-ranking elicitation to offer a paper alternative for knapsack voting.

cs.CY

Learning and Autonomy for Extraterrestrial Terrain Sampling: An Experience Report from OWLAT Deployment

Extraterrestrial autonomous lander missions increasingly demand adaptive capabilities to handle the unpredictable and diverse nature of the terrain. This paper discusses the deployment of a Deep Meta-Learning with Controlled Deployment Gaps (CoDeGa) trained model for terrain scooping tasks in Ocean Worlds Lander Autonomy Testbed (OWLAT) at NASA Jet Propulsion Laboratory. The CoDeGa-powered scooping strategy is designed to adapt to novel terrains, selecting scooping actions based on the available RGB-D image data and limited experience. The paper presents our experiences with transferring the scooping framework with CoDeGa-trained model from a low-fidelity testbed to the high-fidelity OWLAT testbed. Additionally, it validates the method's performance in novel, realistic environments, and shares the lessons learned from deploying learning-based autonomy algorithms for space exploration. Experimental results from OWLAT substantiate the efficacy of CoDeGa in rapidly adapting to unfamiliar terrains and effectively making autonomous decisions under considerable domain shifts, thereby endorsing its potential utility in future extraterrestrial missions.

cs.RO

Opinion Change or Differential Turnout: Changing Opinions on the Austin Police Department in a Budget Feedback Process

In 2020 the tragic murder of George Floyd at the hands of law enforcement ignited and intensified nationwide protests, demanding changes in police funding and allocation. This happened during a budgeting feedback exercise where residents of Austin, Texas were invited to share opinions on the budgets of various city service areas, including the Police Department, on an online platform designed by our team. Daily responses increased by a hundredfold and responses registered after the "exogenous shock" overwhelmingly advocated for reducing police funding. This opinion shift far exceeded what we observed in 14 other Participatory Budgeting elections on our Participatory Budgeting Platform, and can't be explained by shifts in the respondent demographics. Analysis of the results from an Austin budgetary feedback exercise in 2021 and a follow-up survey indicates that the opinion shift from 2020 persisted, with the opinion gap on police funding widening. We conclude that there was an actual change of opinion regarding police funding. This study not only sheds light on the enduring impact of the 2020 events and protests on public opinion, but also showcases the value of analysis of clustered opinions as a tool in the evaluation toolkit of survey organizers.

cs.CY

A Mechanism for Participatory Budgeting With Funding Constraints and Project Interactions

Participatory budgeting (PB) has been widely adopted and has attracted significant research efforts; however, there is a lack of mechanisms for PB which elicit project interactions, such as substitution and complementarity, from voters. Also, the outcomes of PB in practice are subject to various minimum/maximum funding constraints on 'types' of projects. We propose a novel preference elicitation scheme for PB which allows voters to express how their utilities from projects within 'groups' interact. We consider preference aggregation done under minimum and maximum funding constraints on 'types' of projects, where a project can have multiple type labels as long as this classification can be defined by a 1-laminar structure (henceforth called 1-laminar funding constraints). Overall, we extend the Knapsack voting model of Goel et al. [26] in two ways - enriching the preference elicitation scheme to include project interactions and generalizing the preference aggregation scheme to include 1-laminar funding constraints. We show that the strategyproofness results of Goel et al. [26] for Knapsack voting continue to hold under 1-laminar funding constraints. Moreover, when the funding constraints cannot be described by a 1-laminar structure, strategyproofness does not hold. Although project interactions often break the strategyproofness, we study a special case of vote profiles where truthful voting is a Nash equilibrium under substitution project interactions. We then study the computational complexity of preference aggregation. Social welfare maximization under project interactions is NP-hard. As a workaround for practical instances, we give a fixed parameter tractable (FPT) algorithm for social welfare maximization with respect to the maximum number of projects in a group when the overall budget is specified in a fixed number of bits.

cs.GT