Good Papers

Showing papers from ISTA Show all papers

45%Niche pick
?Niche pickVote to see the score

Stranger Things: When Objects Appear Without Their Typical Neighbours

Siddhartha Gairola, Jiahao Xie, Anna Kukleva, Francesco Locatello and 1 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
45%Niche pick
?Niche pickVote to see the score

Algorithms for Linear Equations with Min and Max Operators Under (Absolutely) Halting Condition

Krishnendu Chatterjee, Ruichen Luo, Raimundo Saona, Jakub Svoboda

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
45%Niche pick
?Niche pickVote to see the score

Efficient Algorithms for Distributed Saddle Problems

Ruichen Luo, Anton Rodomanov, Sebastian Stich

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
57%Worth a look
?Worth a lookVote to see the score

MorphGen: Controllable Cell-Image Generation with Biological Representation Alignment

Berker Demirel, Marco Fumero, Theofanis Karaletsos, Francesco Locatello

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
57%Worth a look
?Worth a lookVote to see the score

Causal Discovery Under Hard Selection Bias: A New Robust Score-Matching Approach

Yiwen Qiu, Francesco Montagna, Shimeng Huang, Francesco Locatello

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
57%Worth a look
?Worth a lookVote to see the score

On Differentially Private Mechanisms for Linear Regression

Bardiya Aryanfard, Monika Henzinger, Farhood Rostamkhani

Paris Poster Session 1, Wed, Dec 9, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

– ReadersNo votes yet
1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
78%Highly rated
?Highly ratedVote to see the score

Causal learning with the invariance principle

Assuming acyclic, invariant causal relations across environments, two auxiliary environments identify arbitrary nonlinear causal graphs and enable correct counterfactual inference.

Francesco Montagna, Francesco Locatello

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
11/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 11 of 20 reviewers recommend it
lenient 5/5
medium 5/10
strict 1/5
71%Highly rated
?Highly ratedVote to see the score

Reconciling Causality and Non-Equilibrium Thermodynamics with Hamiltonian Causal Models

Hamiltonian Causal Models separate equations of motion from intervenable mechanisms and define causal effects as interventional path discrepancies, showing entropy production witnesses trajectory-level causal effects invisible to standard average treatment effects.

Dario Rancati, Max Welling, Francesco Locatello

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
6/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 6 of 20 reviewers recommend it
lenient 2/5
medium 4/10
strict 0/5
76%Highly rated
?Highly ratedVote to see the score

The Rate-Distortion-Polysemanticity Tradeoff in SAEs

Sparse autoencoders face a rate-distortion-polysemanticity tradeoff where monosemanticity raises reconstruction cost and data co-occurrence drives polysemanticity.

Tommaso Mencattini, Francesco Montagna, Francesco Locatello

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
10/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 10 of 20 reviewers recommend it
lenient 4/5
medium 5/10
strict 1/5
86%Must read
?Must readVote to see the score

Assessing Sample Quality in Conditional Generation under Compositional Shift

A per-sample trust score combining global realism and attribute-wise faithfulness evaluates conditional generations under compositional shift without reference data, enabling filtering and ranking that improves biological imaging and vision benchmarks.

Berker Demirel, Valentino Maiorca, Marco Fumero, Theofanis Karaletsos and 1 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
14/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 14 of 20 reviewers recommend it
lenient 5/5
medium 8/10
strict 1/5
76%Highly rated
?Highly ratedVote to see the score

TRACER: Token ReAssignment for Concept ERasure in Generative Recommendation

TRACER reassigns semantic item tokens to erase target concepts in generative recommendation while better preserving recommendation utility than baseline unlearning methods.

Ziheng Chen, Jiali Cheng, Zezhong Fan, Diyuan Wu and 3 more

Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026

– ReadersNo votes yet
10/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 10 of 20 reviewers recommend it
lenient 5/5
medium 5/10
strict 0/5